Their primary task is monitoring and responsibly blocking incoming traffic that originates from public networks and internet connections. This enables them to effectively block malicious traffic and unauthorized individuals and servers from accessing the network’s operating system.

Host-based firewalls are only available as software and are best used to protect individual devices or servers within a network rather than the entire infrastructure.

Continue reading to learn more about how host-based firewalls work, their advantages and disadvantages, identifying the ideal situation for employing a host-based firewall, as well as the best providers of software on the market.

For more information, also see: What is Firewall as a Service?

How Host-Based Firewalls Work?

Used to protect a relatively small section of a network, host-based firewalls are much easier to set up and typically don’t function in complex ways.

Host-based firewalls are list-reliant firewalls. They require the network’s admin or device used to create a set of rules that specify with great detail the type of traffic that should be allowed to enter the host, and which should be blocked.

While this may seem too simple to be secure, the rule lists allow for an incredible level of detail. You can freely include and exclude IP addresses, ports, communications and encryption protocols depending on what you deem safe.

The rules can be set manually for ultimate control, which is sometimes the only available option, especially for budget-friendly or older software releases.

More modern versions of host-based firewalls can be set to generate list items and update them automatically. They’re able to do this by monitoring the network’s incoming traffic over a prolonged period of time and identifying patterns of malicious and suspicious behaviors as they arise, and blocking them.

For more information, also see: Artificial Intelligence in Cybersecurity

Pros and Cons of Using a Host-Based Firewall

When it comes to making a decision on the type of firewall to implement for your cybersecurity strategy, it’s important to first look at both the advantages and disadvantages of the solution.

Host-based firewalls perform a very niche role in network security. This allows them to be highly efficient in certain areas while falling short when employed to protect network resources for which they weren’t designed.

Advantages of Using a Host-based Firewall

The numerous benefits and advantages of using a host-based firewall are the reason for the popularity of the solution, especially among organizations and businesses that prefer to provide added protection for individual devices.

Host-based firewalls are some of the most affordable firewall solutions out there, with some available as the result of open-source projects. They are entirely free to use.

Even when looking for a paid solution with added features and support from the vendors, most host-based firewalls are priced under the $100 price tag.

Because the firewall software is deployed directly on the machine, host, or application it’s protecting, they automatically follow when the host is moved between environments, servers, and clouds.

Additionally, the set configurations and rules lists don’t change during the move. However, if the firewall is set to automatically update the rules through traffic monitoring, it’ll likely start adding new rules based on the new environment and its associated threats.

Host-based firewalls are more often than not implemented as the second layer of defense, rather than the first. This grants you an additional chance to detect and block malware or a malicious connection before it reaches the rest of the resources.

Paired with adequate segmentation and behavior control, host-based firewalls can be used to add a layer of protection to particularly vulnerable or critical hosts.

Using proper configurations and rule lists, host-based firewalls can also prevent insider attacks. They can be made so any user, device, or application is unable to access the protected host without meeting a set of criteria.

The firewall software installed on each device can be configured separately depending on that device’s security and privacy needs.

Additionally, the rules and configurations of individual devices are completely customizable and can be adjusted at any time, giving you full control over the functionality of the firewall.

For more information, also see: Data Security Trends

Disadvantages of Using a Host-based Firewall

Host-based firewalls aren’t an all-in-one solution. Even when implemented and configured properly, they still come with their fair share of cons that may be a deal-breaker to some users.

Host-based firewalls aren’t ideal for wide-scale use. The installations, configurations, and management of them quickly become tedious and incredibly time-consuming. Additionally, there is an increase in the total number of possible points of error, where the configuration wasn’t ideal or the software wasn’t up-to-date.

Also, traffic analysis and diagnostics aren’t their strong suit. Even if a host-based firewall successfully blocks a malicious flow of traffic, it makes it difficult for network admins to further investigate the reason for the block.

Adding to it, host-based firewalls aren’t particularly sophisticated or advanced in their approach. When they block incoming traffic, that is a sign the malicious traffic has already made its way through the perimeter of your network, where your more advanced firewall and network security solutions are situated. The further from the source the threat is, the harder it is to trace back.

For more information, also see: How to Secure a Network: 9 Steps

Host-based Firewall Guidelines

There is a set of recommendations and guidelines you should follow when implementing a host-based firewall solution, in order to ensure the best at the device level for your network.

Minimizing Remote Host Access

When working with hosts where remote access is necessary, such as wireless printers and IoT devices and networks, it’s important that you limit the number of allowed connections to the host.

For access requirements by remote users, using identity authentication and encrypted communications tunnels enables you to minimize the risks.

Connect to Network Vulnerability Scanners

Since it’s best for the host to also be protected by a more comprehensive security solution, such as a network-based firewall, it’s important to allow it access into the host when needed.

This ensures that the firewall-protected host is included in any and all vulnerability checks, audits, and malware scans performed network-wide.

Control Outbound Traffic Flow

Unmonitored outbound traffic flow can be exploited for data leaks and insider attacks. Depending on the type and the role of the host in the network, you should either restrict or outright ban outbound traffic.

Activity Logging

Activity and behavior logging, while not necessary for the active protection of the host, is incredibly beneficial for analyzing the security status of the network, audits, and conducting cyber forensics investigations when needed.

When You Should Use a Host-Based Firewall

Host-based firewalls aren’t a stand-alone solution. You should only consider adding them to your family of network security tools once you have a more holistic solution applied.

While options such as network-based firewalls and Endpoint Detection and Response (EDR) can be used to elevate the security of your network, those tend to be more extreme approaches and are not always suitable for smaller organizations and businesses.

You should consider using a host-based firewall if you have a handful of devices, servers, or applications that carry particularly sensitive data and information. They can act as an added line of defense which you can enforce with strict rules and configurations that might otherwise be too restrictive for your network as a whole.

Furthermore, it can be used as an emergency solution to protect your most vulnerable assets until a more comprehensive security solution is installed.

Best Host-Based Firewall Providers

Following are a couple of the best providers of host-based firewalls on the market:

Check Point

Check Point is a San Carlos, California-based vendor of hardware and software solutions. It offers a wide variety of security products and solutions, from cloud and endpoint security to network security and security management.

ZoneAlarm is Check Point’s anti-ransomware, host-based firewall solution that’s capable of detecting, analyzing, and blocking suspicious behavior and activity on your device. It uses Check Point’s proprietary firewall technology, OSFirewall, to stop malicious individuals from accessing your network.

It’s highly rated on multiple third-party review sites, such as PeerSpot, with a 4 out of 5 rating, and G2 with a 4.4 out of 5 rating.

GlassWire

GlassWire is an Austin, Texas-based cybersecurity company and provider of advanced network monitoring and protection solutions that includes a built-in firewall. It’s most known for its outstanding capabilities in bandwidth control and remote server monitoring.

GlassWire can also be deployed as a host-based solution, allowing you to visualize network activity for analysis and audit, in addition to alerts that ring out as soon as it detects malicious traffic or behavior.

It’s widely respected by users as showcased in its overwhelmingly high reviews on third-party review sites. It has a 4.6 out of 5 rating on G2, and a 4.7 out of 5 rating on Capterra.

Bottom Line: Host-Based Firewalls

Host-based firewalls are used to boost the security of individual devices, applications, or servers within a network. They can be configured either manually or left to develop the rules based on traffic monitoring.

While a host-based firewall is incredibly beneficial as an affordable solution that’s easy to control, it can’t be used on a wide scale.

For more information, also see: What is Big Data Security?

By being permitted to access the traffic, activity, and behavior of a network’s applications, proxy firewalls can maintain the integrity, security, and privacy of the network’s servers, apps, and databases from malicious traffic, malware, and unauthorized access attempts.

Continue reading to learn more about how application-level gateways work, their most beneficial features, their pros and cons, and examples of leading vendors. 

For more information, also see: Why Firewalls are Important for Network Security

How Application-Level Gateways Work

As the name suggests, application-level gateways work by being the only gateway between the network’s internal activities, like users and applications, and the public internet. All traffic that’s incoming or outgoing to the application layer in the network passes through the gateway and gets scanned for any malicious or unauthorized activity.

It’s also called a proxy firewall because it utilizes proxies to set up a private connection that remote users can access the network through, without compromising on speed or security. However, this type of firewall only works on Layer 7 of the Open Systems Intercommunication (OSI) model, which is the layer where the network’s applications, software, and programs operate and access the internet.

This process allows the firewall to avoid direct connections between your network’s applications and outside traffic before it’s completely verified. As a result, this creates an added barrier that makes it harder for intruders and infiltrators to either access your network or even extract information from any exchanged data packets.

With this setup, only one server per network segment has direct access to the public internet. All other devices would have to route their traffic through it, whether it’s outgoing or incoming.

For more information, also see: What is Firewall as a Service?

Features of Application-Level Gateways

Proxy firewalls are one of the best solutions available on the market for application-based networks. They stand out from all the other types of firewalls that can also protect applications, thanks to a number of features the average proxy firewall comes equipped with, such as:

Bandwidth Usage Reduction

Application-level gateways routinely save cache webpages and traffic of the most visited sites and addresses. This reduces the strain on your network’s bandwidth by not having to load frequently-requested pages multiple times in a row.

This also enables the gateway to improve overall performance. Applications and users looking to access the website can reach it more quickly, without having to go through the rest of the network’s traffic first.

Intruder Protection

By continuously monitoring the inbound network traffic and scanning it thoroughly before it even makes contact with any of the network’s internal elements, proxy firewalls are capable of detecting intruders more effectively.

Sophisticated Filtering

Application-level firewalls often carry many traffic filters used to scan both incoming and outgoing data, searching for malicious intent or suspicious behavior. Additionally, some filters are also capable of monitoring other Layer 7 activity, such as network requests, external logs, and manually saved cached files.

Security Policy Enforcement

Similarly to other types of firewalls, application-level firewalls also centralize and simplify the process of setting up and enforcing security policies on the application layer of the network.

This ensures all regulations and configurations in the network are up to date, and no application is left following outdated—and possibly risky—security policies.

Site Access Control

As the middleman between all of the network’s applications and the public internet, application-level firewalls can also restrict and control which websites can be accessed through its proxy.

You can set this up manually, blocking all communications to a number of determined websites. Alternatively, the process could be automated to block or restrict access to all websites that are flagged on databases of malicious sites or meet a set of conditions, such as a security or privacy policy you don’t deem suitable.

Internet Access Control

Application-level firewalls are capable of mass-preventing specific users and applications from gaining access to the internet as a whole. The restrictions can be exclusive to high-risk users and applications, or simply members deemed in no need of immediate internet access.

For more information, also see: Artificial Intelligence in Cybersecurity

Advantages and Disadvantages of Using Application-Level Gateways

When it comes to understanding the inner workings of application-level gateways, it’s important that you acquire a general knowledge of their advantages and disadvantages as a stand-alone solution.

Advantages of Application-Level Gateways

Application-level gateways are most known for the added level of security it provides by using proxy technology to isolate the application layer in the network from outside connections. It’s also responsible for the verification and authentication of incoming traffic and connection requests.

This allows it to greatly reduce the risks of DDoS (Distributed Denial of Service) attacks and IP spoofing attacks. Additionally, they allow for optimal user anonymity by hiding the network’s IP address from any outside parties, even during verified connections. Any connection request is forwarded through the main IP address of the network’s proxy.

When it comes to individual threats, proxy firewalls are highly effective at identifying and assessing the levels of incoming threats. Most options employ Deep Packet Inspection (DPI) technology alongside the proxy servers to analyze threats and block them promptly.

For individual applications connected to the proxy, all of their commands get screened and analyzed while in data packets before they’re executed or released outside the network. This can all be logged for further examination and auditing efforts later on.

Disadvantages of Application-Level Gateways

Application-level gateways still have a handful of drawbacks and weak points, especially when used as a stand-alone security solution with no added tools or features.

For one, they’re more prone to experiencing bottlenecks as all the network’s incoming and outgoing data is redirected towards a single point of processing. The stricter the monitoring rules on the proxy server, the slower the data flow.

Proxy firewalls also have major compatibility problems, as they can’t support a wide variety of connection types and network protocols. This can greatly limit the pool of servers and agents your application layer is able to connect with, without needing additional tools.

Similarly, not all applications are compatible with proxy servers. By not being proxy-aware, applications can sometimes ignore the presence of the proxy server and attempt to connect to the internet directly.

While some application-level gateways’ drawbacks can be fixed or reduced in effect through proper configuration, that’s not easy to do. Furthermore, any misconfigurations in the setup of the firewall may leave some gaps in your security, such as open ports.

On a related topic, also see: Top Cybersecurity Software

Examples of Application-Level Gateway Providers

There are countless cybersecurity providers on the market that offer proxy firewalls, either exclusively or as a part of a bigger ecosystem of network security solutions.

Following are a couple of the leading application-level gateways providers on the market:

F5 Networks

F5 Networks is a Seattle, Washington-based IT and technology company that provides application security, cloud management, and online fraud prevention solutions among many others. 

The Advanced Web Application Firewall (AWAF) is the core security component of F5’s suite of application delivery and management services. It employs cutting-edge technology to help you consolidate and manage traffic, network firewall, SSL inspection, and application access.

Juniper Networks

Juniper Networks is a Sunnyvale, California-based technology and networking company that develops and sells a number of computer networking software and hardware, from routers and switches to network management software and network security solutions.

The Application Layer Gateway (ALG) is a piece of software that’s capable of managing session protocols and providing application-layer-aware packet processing on network switches on devices running Junos OS.

For more information, also see: How to Secure a Network: 9 Steps

When to Use an Application-Level Gateway?

Application-level gateway solutions are the perfect solution for networks with a high percentage of their traffic originating from Layer 7 in the OSI model. It can help you better control the activity and behavior of your network’s applications and the users that access them, reducing the risks of malicious attacks, DDoS attacks, unauthorized access, and IP spoofing attacks.

It’s important that your application layer is never left to connect to the public internet unguarded and without a firewall or proxy. Whether you’re looking to segment and better specialize your network security strategy or simply need to secure the newly-added application layer to your network, proxy firewalls are the way to go.

Bottom Line: Application-Level Gateways

Application-level gateways behave as an intermediary between a network’s applications and the open internet. Also called proxy firewalls, they help you set up a proxy server between the applications and outside connection, where exchanged traffic is constantly monitored for malicious activity.

It’s the perfect solution for securing applications that regularly connect to the web. However, their capabilities don’t stretch to the remaining layers of the networks and shouldn’t be used alone as a holistic security solution.

Continue reading to learn more about the features, pros and cons, and functionality of a circuit-level gateway.

For more information, also see: Why Firewalls are Important for Network Security

How Circuit-Level Gateways Work

Circuit-level gateway firewalls work by providing a layer of security between TCP and UDP throughout the connection by acting as the handshaking agent. They authenticate the handshake by scanning and examining the IP addresses of the packets as the 5th layers, and stand between the incoming web traffic and the sending hosts.

This type of firewall is rarely used individually as a stand-alone solution for network security. They’re best combined with a stateful inspection firewall for securing layers 3 and 4, and an application-level firewall to secure Layer 7.

Circuit-level gateway firewalls are able to maintain a network’s security by constantly validating and authenticating the connection by only allowing safe data packets to pass. In the case of malicious activity detected in an incoming data packer, the firewall terminates the connection and closes the circuit connection between the nodes.

For more information, also see: What is Firewall as a Service?

Features of Circuit-Level Gateways

When implementing a circuit-level gateway firewall, whether individually or in tandem with other network security and firewall solutions, there is a set of features you can expect upon deployment.

Some of circuit-level gateway firewalls’ most notable features include:

TCP Handshake Verification

While circuit-level gateways don’t check incoming data packets for the destination IP address, they check and verify the TCP handshake required for establishing the connection, and whether it adheres to the security and privacy standards set by the network’s admins.

It checks and authenticates the connection through the three-way TCP handshake. The firewall synchronizes both sides in the connection sessions and mitigates unauthorized interception.

Hides the Network’s Information

When communicating with outside hosts, servers, and devices, a circuit-level gateway’s firewall doesn’t reveal the private information of your network to avoid the exploitation of communication information.

After the initial verification of the communicating party, this type of firewall doesn’t intervene with the type and volume of traffic exchanged.

For more information, also see: Artificial Intelligence in Cybersecurity

Stand-Alone Security Functionality

When it comes to securing the communication and movement of data packets in the 5th layer of the OSI model, circuit-level gateways are fully capable of being a stand-alone solution. It can be used to centralize the management and security policy of the entire layer without the need to integrate third-party tools.

SOCKS Protocol Configurations

When used in a network firewall setting, SOCKS servers allow the hosts of the network’s servers to fully access the public internet while providing complete protection from unauthorized actions and web traffic interception attempts.

Depending on the ports and protocols used in the network communication, the gateways can either use SOCKS as the proxy of the connection or as the client.

For more information, also see: Data Security Trends

Advantages of Circuit-Level Gateways

Similarly to the wide variety of other types of firewall solutions, circuit-level gateway firewalls come with a set of benefits and drawbacks.

Following are a handful of the most notable circuit-level gateways firewall advantages:

• Keeps private your network’s identifiable information
• Simple and quick to implement
• Doesn’t exhaust time and computational power by avoiding the monitoring and scanning of individual data packets
• Lightweight software with a low impact on the network’s performance
• Cost-efficient in both software and hardware expenses
• Doesn’t require dedicated proxy servers for each applications
• Highly flexible for address schemes development

“A circuit-level gateways firewall operates at the OSI model’s session layer, monitoring TCP (Transmission Control Protocol) connections and sessions,” writes Anshuman Singh, senior executive content developer for Naukri Learning.

“Their foremost objective is to guarantee the safety of the established connections. Circuit-level gateways are inexpensive, simple, and have little impact on network performance,” adds Singh.

Disadvantages of Circuit-Level Gateways

Following are a few of the most notable drawbacks and disadvantages of circuit-level gateways firewalls:

• Unable to detect malicious files in data packets
• No support for advanced content filtering
• Cannot monitor the communications of applications
• Only compatible with TCP connections
• Unable to protect more than Layer 5 of the OSI model
• Requires initial configuration of the network protocol stack

For more information, also see: How to Secure a Network: 9 Steps

When to Use a Circuit-Level Gateways Firewall

Picking out the primary or sole tools for securing your network can be tricky, especially with the wide variety of firewall types and generations available commercially. Luckily, the use cases for a circuit-level gateway firewall aren’t numerous.

For one, it’s the perfect option if you’re on a low budget and unable to provide the necessary hardware and bandwidth to account for the weight of more complex firewall solutions. They allow for more control over the connections of your network with minimal effort as it doesn’t need the capabilities or configuration otherwise required for in-depth packet filtering and monitoring.

On their own, circuit-level gateways aren’t considered to be the most effective at securing a network, especially one where devices and users communicate frequently with outside servers. However, compared to more simplistic options, such as a stand-alone deep-packet inspection firewall, circuit-level gateways are an improvement.

Examples of Circuit-Level Gateways Firewall Providers

Forcepoint

Forcepoint is an Austin, Texas-based software company that designs, develops, and sells network security and management software. It offers solutions ranging from data protection and cloud access security to advanced NG firewalls, and even cross-domain solutions.

Stonesoft is one of Forcepoint’s Next-Generation Firewall (NGFW) solutions. It provides both stateless and stateful packet filtering alongside circuit-level firewall capabilities with advanced TCP proxy control agents.

It’s an intelligent firewall solution that can be extended all the way to Layer 7, implementing built-in SSL VPN and IPsec capabilities.

Forcepoint’s NGFW has accumulated high user ratings over the years on various third-party review sites. For example, it has a 3.8 out of 5 rating on PeerSpot and 4.4 out of 5 on G2.

In 2020, Forcepoint was recognized for 4 years in a row by Gartner as a Visionary in Network Firewalls.

Juniper Networks

An enterprise leader, Juniper Networks is a Sunnyvale, California-based developer of computer networking products. It provides its clients with all the necessary software and hardware to build, maintain, and manage a network, from routers and switches to network security and management software.

The Juniper Networks SSR120 is a network appliance that’s software-driven with various NGFW capabilities. It’s a branch of Juniper’s SSR (Session Smart Router) portfolio and supports network security and management capabilities from Layer 2 all through to Layer 5.

Similarly, it includes various additional features such as traffic encryption, built-in VPN support, advanced traffic filtering, and DoS/DDoS protection. 

Juniper’s solution is trusted by its users, as demonstrated by the positive reviews on various third-party reviews sites, such as PeerSpot with a 4 out of 5 rating, and Gartner with a 5 out of 5 rating.

On a related topic, also see: Top Cybersecurity Software

Bottom Line: Circuit-Level Gateways

Unlike packet inspection firewalls, circuit-level gateways don’t filter and monitor the contents of exchanged data packets with outside sources. Instead, they confirm the security and authenticity of the connection, and verify that it doesn’t pose a threat to the network through its IP and address and other superficial parameters.

It’s not fully safe to use as circuit level gateway as a stand-alone solution for protecting a network with a wide variety of components, but it remains one of the most affordable and non-resource-intensive network security solutions. There are multiple firewall solutions that include, or consist of, circuit-level gateway capabilities. They are offered by household names in the computing networking cybersecurity and management software industry, such as Juniper Networks and Forcepoint.

When it comes to choosing the right type of firewall and protection for your network, there are multiple factors you should take into account. However, the first step always remains to fully understand your options, how they work, their pros and cons, and whether they fall within your financial and technical capabilities.

Continue reading to learn more about the differences between stateful and stateless firewalls, as well as examples of both offerings.

For more information, also see: Why Firewalls are Important for Network Security

Stateful vs. Stateless Firewall: Summary 

Stateful Firewall

Stateful firewalls are a network-based type of firewall that operates by scanning the contents of data packets, as well as the states of  network connections. It’s often referred to as dynamic packet filtering or in-depth packet inspection firewall and can be used in both non-commercial and established business networks.

This type of firewall works on the 3rd and 4th layers of the network. In the Open System Interconnection (OSI) model, those represent the network layer and the transport layer, overseeing the movement of data traffic and communications requests made by users and devices throughout the network.

Stateless Firewall

Stateless firewalls are also a type of packet filtering firewall operating on Layer 3 and Layer 4 of the network’s OSI model. However, they aren’t equipped with in-depth packet inspection capabilities.

Stateless firewalls strictly examine the static information of data packets exchanged during cross-network communications. They constantly monitor the traffic for the sender and recipient’s IP addresses, communication ports, and protocols, blocking any traffic that doesn’t meet the network’s security standards.

On a related topic, also see: Top Cybersecurity Software

Stateful vs. Stateless Firewall: Features

Stateful Firewall Features

Despite operating differently from the traditional firewall software, stateful firewalls are about a decade more recent than the original firewall technology and carry additional features, capabilities, and tools to the basic firewall features.

Some of the most notable stateful firewall features include:

• Network-level Policy Enforcement: A stateful firewall is capable of setting up and enforcing security and policies for activity on the 3rd and 4th layers. It enables you to manage the data transfers between hosts and network components, and control the method and ports that forward the data packets to the network’s receiving devices and accounts.
• Dynamic Packet Filtering: While packet monitoring solutions filter traffic based on superficial qualities, such as the source and receiving end, stateful firewall technology monitors and tracks the traffic of an entire connection session. 
• Self-teaching Intelligent Capabilities: Stateful firewalls can get accustomed to the traffic and threat of a set network after some time. A part of the system’s memory is dedicated to retaining and retrieving the key differentiators of safe and malicious traffic that grows with time.
• High Traffic Capacity: Stateful firewalls are capable of performing with impressive speeds and qualities even under heavy traffic flows on larger networks. They can’t be easily overwhelmed by high-traffic attacks and are still able to correctly detect and intercept forged communications attempts and unauthenticated users.

Stateless Firewall Features

Stateless firewall technology is capable of rapidly supporting network security through the scanning of static packet information.

By approaching security differently, stateless firewall solutions generally come with features and capabilities that aid them in their work, such as:

• Control of Packet Flow: Stateless firewalls enable you to oversee and manage the data flow of network connections occurring on the third and fourth layers of the OSI.
• Centralized Filter Control: The security policies and filtering requirements of a stateless firewall can be drafted and enforced throughout the network from a centralized location.
• Large Scale Traffic Blocking: Network traffic originating or heading toward a set address can be blocked for either security purposes or better rationing the network’s bandwidth.

For more information, also see: How to Secure a Network: 9 Steps 

Stateful vs. Stateless: Advantages 

Top Stateful Firewall Advantages

There are many benefits to implementing a stateful firewall as your primary network protection solution, some of which include:

1. Highly reliable at detecting forged communication attempts
2. Minimizes the number of ports open for communication requests
3. Built-in, high-detail activity logging, and analysis
4. Centralizes network communications and traffic management
5. Highly intelligent and grows to better fit your network

Top Stateless Firewall Advantages

There are many advantages to using a stateless firewall to secure the components of your network in the face of evolving cyberattacks, such as:

1. Delivers fast results without causing the system to lag
2. Withstands large and consistent flow of data packets and traffic
3. Minimizes costs from implementation to required system resources
4. Doesn’t use up a lot of memory storage
5. Capable of protecting internal network components from insider attacks

Stateful vs. Stateless: Disadvantages 

Top Stateful Firewall Disadvantages

Despite its numerous features and advantages, using a stateful firewall solution as the sole network security precaution comes with a handful of cons that you should be aware of, such as:

1. Data transfers speeds are static and generally slow
2. More susceptible to Man-in-the-Middle (MITM) attacks
3. Takes time to become custom-fit to the security needs of your network
4. Doesn’t operate on the application layer, or 7th layer
5. Requires high memory storage and computational power to run at full capacity
6. Can be tricked into allowing unauthorized connections or data packets access to the network

Top Stateless Firewall Disadvantages

Relying solely on a stateless firewall for all the security needs of your network can be detrimental to the safety of your network. Stateless firewalls fall short in a handful of ways when used alone, such as:

1. Doesn’t inspect data packets in depth
2. Requires a lot of initial configuration to work properly
3. Unable to make connections between connected signs of an attack
4. It’s susceptible to attacks through spoofed IP addresses and falsified communications requests

On a related topic, also see: Top Cybersecurity Software

Stateful vs. Stateless: Examples of Providers

Examples of Stateful Firewall Providers

There are numerous stateful firewall solutions available on the market from a number of security software and service providers. They vary in reputation, efficiency, and the variety of added features and capabilities.

A couple of examples of stateful firewall providers include:

Palo Alto Networks

Palo Alto Networks is a Santa Clara, California-based network and cybersecurity company that provides a highly-diverse portfolio of cloud, platform-based, and native security solutions to organizations.

Palo Alto’s Next-Generation Firewall (NGFW) is a stateful firewall that’s capable of managing and monitoring the network’s layer on the 4th layer, but also traffic match and application on the 7th layer.

Microsoft Azure

Microsoft Azure is a Redmond, Washington-based networking and cloud computing service and product provider by Microsoft. It offers several application management, security, Microsoft-managed data centers, and network management solutions.

The Microsoft Azure Firewall is a cloud-based, intelligent network firewall that offers protection to the data and workloads taking place on the Microsoft Azure cloud environment. It’s fully stateful in configuration and comes with pre-installed high capacity and availability that can be scaled in the cloud without a limit.

Examples of Stateless Firewall Providers

While stateless firewall solutions are generally less popular among organizations with high-security needs for large networks, the technology plays a primary role in securing enclosed networks that don’t handle a lot of traffic at a reasonable cost. 

Following are a couple of examples of stateless firewall providers:

Cisco Systems

Cisco Systems is a San Jose, California-based digital communications, security, and computing networking company. It designs, develops, and sells software and hardware to help organizations better manage and connect their networks through secure devices and proper data management and analysis.

The Cisco UCS B-Series is a family of networking servers that incorporate Cisco’s network security and data management standards. The devices support abstract and stateless capacities, allowing for a more varied network security experience.

Forcepoint

Forcepoint is an Austin, Texas-based software company that provides security, data protection, cloud access, and networking solutions to businesses and organizations. It’s most known for its cross-domain firewall and network security solutions.

Forcepoint’s Next-Generation Firewall (NGFW) protects from data theft and prevents unauthorized access and communications within and outside of your network. It’s equipped with both stateful and stateless packet filtering capabilities, allowing it to protect a wide range of network architectures.

For more information, also see: Data Security Trends

Bottom Line: Stateful vs. Stateless Firewalls

At the end of the day, both stateful and stateless firewall solutions have their benefits under the right circumstances.

While stateful firewalls inspect individual connections made outside the network, seeking signs of malicious web traffic, and can learn to become better at detecting threats, stateless firewalls are more basic in their approach.

In contrast, stateless firewalls only monitor and inspect the metadata and outwardly displayed information of a packet to determine whether it poses a threat to the network.

Each solution may be the best for your business – depending on your unique infrastructure needs.

For more information, also see: What is Firewall as a Service? 

To understand this, here’s some background: Data packets are the primary unit used for transferring data between networks in telecommunications. In addition to content, packets carry sender and receiver information from IP addresses to ports and communication protocols.

In packet filtering, data passes through a network interface or layer that stands between the sender and the network’s internal components. This layer determines whether the packet is blocked or allowed to pass, depending on its content and superficial contact information.

When this process is used in network firewalls, the result is a packet-filtering firewall. Similar to standard firewall solutions, packet-filtering firewalls sit at the outer perimeter of the network and monitor the flow of outgoing and incoming web and network traffic. Each data packet is scanned and checked against a set of security policies and configurations, allowing the software to determine whether to allow or block the communication.

Continue reading to learn about how packet-filtering firewall technology works, its unique features, pros and cons, as well as the best providers on the market.

For more information, also see: Why Firewalls are Important for Network Security

How Packet-Filtering Firewalls Work

Packet-filtering firewalls are responsible for regulating the flow of data entering and exiting the network, all while keeping network security, integrity, and privacy in mind. Most packet-filtering firewalls work by scanning the IP addresses and ports of the packets’ sources and destinations to determine whether they come from a trusted source.

What the firewall considers safe communication depends on pre-set rules and configurations. In some instances, filtering may also include the packet’s communication protocols and contents. 

For more information, also see: Data Security Trends

4 Types of Packet-Filtering Firewalls

Packet-filtering is a network security technology that can be employed in several ways, depending on an organization’s accompanying software and system configurations. These methods include static, dynamic, stateless, and stateful. 

Static Packet-Filtering Firewalls

Static packet-filtering firewalls require you to manually set up the filtering rules, allowing for more administrative control. That’s especially the case with smaller and low-traffic networks, as static packet-filtering firewalls can manually open or close internal and external network communication on demand.

Dynamic Packet-Filtering Firewalls

On the other end of the spectrum are dynamic packet-filtering firewalls. Instead of forcing you to manually open or close communication ports, this type of firewall can open and close ports automatically during specified periods of time or set time intervals. They are generally more flexible firewall solutions that can be automated to suit the current security needs of your network.

Stateless Packet-Filtering Firewalls

When looking for a packet-filtering firewall alternative that’s both lightweight and capable of handling large volumes of traffic, stateless firewalls are the answer. They do not inspect individual instances of traffic, so they are best suited for networks that strictly communicate with trusted servers. Out of the box, they require some level of configuration to operate properly.

Stateful Packet-Filtering Firewalls

Stateful packet-filtering firewalls focus on individual communications sessions rather than the data being transferred. They continuously track all of the network’s active connections, verify UDP and TCP streams, and recognize incoming and outgoing traffic through the context of IP addresses and ports.

Choosing the right variation of packet-filtering firewall for your network depends on multiple factors, such as the levels of security you require, traffic volume, technical support requirements, and coverage for the most vulnerable aspects of your network.

For more information, also see: How to Secure a Network: 9 Steps 

Advantages of Packet-Filtering Firewalls

Independent packet-filtering firewalls work to monitor traffic in the network layer — Layer 3 — of the Open Systems Interconnection (OSI) model. The advantages they offer include transparency, ease-of-use and efficiency. 

Transparency

Packet-filtering firewalls work in the background without interfering with or disturbing the operation of the network. As long as the data flows, you won’t hear from the firewall. It only sends out a notification once a packet has been blocked, along with the reason for the ban.

This makes packet-filtering firewalls user-friendly solutions that don’t demand custom software, specialized user training, or the setup of a dedicated client machine.

Ease-of-Use and Accessibility

Packet-filtering firewalls are some of the easiest firewall solutions to implement and use. Both implementation and setup don’t require intensive knowledge or training. With limited knowledge, these firewalls can be utilized to secure the entirety of your network through the network layer.

Cost-Efficiency

Packet-filtering firewalls are some of the most affordable firewalls available. They often come built-in with many router devices. The same efficiencies apply to hardware requirements: they’re lightweight, don’t use up your system’s resources, and can function with a single router device.

When used within small and medium-sized networks, packet-filtering firewalls are helpful for maintaining security on a budget.

High-Speed Efficiency

One of the leading benefits of packet-filtering firewalls is their high-efficiency processing that doesn’t compromise on speed. Since most packet-filtering firewall technology is basic and doesn’t require a lot of knowledge that’s often required to operate intelligent software, their decision-making time frame is incredibly short.

Furthermore, without an added logging feature, most packet-filtering firewalls don’t regularly keep their filtering information, which saves on data storing time and memory storage space.

On a related topic, also see: Top Cybersecurity Software

Disadvantages of Packet-Filtering Firewalls

While packet-filtering firewalls have their benefits, highly-specific packet-filtering firewalls also have some drawbacks, especially when used as stand-alone solutions. These include less security, inflexibility, and a minimal support protocol. 

Comparatively Less Security

When held up against other firewall types, packet-filtering firewalls are some of the least secure. They aren’t intelligent and are unable to protect against complex and zero-day cyber threats.

Their biggest security weak point is they allow all traffic that originates from an authorized port or IP address, making them especially vulnerable to IP spoofing attacks.

Inflexibility

Packet-filtering firewalls work in the moment. Most don’t have the necessary intelligence or flexibility needed to consider previous attacks or packet blocks.

You’ll need to manually make changes to the configurations and functionality of the firewall in order to stay up to date on the latest threats.

Lack of Built-in Logging

While the lack of built-in logging capabilities helps with keeping the firewall software fast and lightweight, it also poses difficulties for businesses and network administrators that rely on traffic logging for compliance purposes or for analyzing the state of the network.

Minimal Protocol Support

Packet-filtering firewalls are stateless firewalls that can only handle a set variety of communication protocols. Even with careful configuration, some varieties are still unable to support RPC-based protocols, such as NFS and NIS/YP.

Best Packet-Filtering Firewall Providers

When it comes to picking out the right packet-filtering firewall solution, it’s important to take into consideration the variety of added features and benefits offered by different providers.

A couple of the leading packet-filtering firewall providers include:

Sophos Group

Sophos Group is an Abingdon, United Kingdom-based developer and provider of network security software and hardware. It helps corporate clients set up, manage, and secure all aspects of their networks, from encryption and endpoint protection to email security and threat management efforts.

Sophos UTM (Unified Threat Management) is a network security solution that aims to simplify the management and administration of network security packages through a single modular appliance. Aimed at small and medium-sized businesses, it greatly simplifies network and infosec efforts through a centralized system.

Fortinet

Fortinet is a Sunnyvale, California-based developer and vendor of enterprise-grade Next-Generation Firewalls (NGFWs) and network security solutions. It aims to provide tools and solutions to simplify the management and security of IT infrastructures for organizations and corporations.

FortiGate is Fortinet’s NGFW solution that promotes security-driven management and consolidation of networking infrastructure. It employs various security capabilities such as an Intrusion Prevention System (IPS), Secure Socket Layer (SSL) inspection, and advanced filtering to examine the headers and attributes of packets, including their ports and IP addresses.

Bottom Line: Packet-Filtering Firewalls

Packet-filtering firewalls are designed to examine the IP addresses and ports of incoming and outgoing data packets to determine their validity. They’re some of the lightest, most affordable, and easy-to-use firewall solutions available. However, they fall short in the complexity of security they offer as stand-alone solutions.

They come in a variety of types, from stateless and stateful to static and dynamic, and can be purchased or enlisted from a number of trusted cybersecurity software and hardware vendors.

For more information, also see: What is Big Data Security?

One type of firewall software that focuses on packet inspection is a stateful inspection firewall.

Continue reading to learn more about the functionality of a stateful inspection firewall solution, how it works, its pros and cons, as well as examples available on the market.

For more information, also see: Why Firewalls are Important for Network Security

How Do Stateful Inspection Firewalls Work?  

A stateful inspection firewall uses network-based software to monitor the condition and states of active network connections. It inspects and analyzes the data packets exchanged during communications, scanning for data risks or unauthorized behavior.

Situated in the Open Systems Interconnection (OSI) layers 3 and 4, it’s active in both the network and the transport layers, where the majority of network activity occurs. Sometimes referred to as a dynamic packet filtering firewall, it can be configured for various levels of security and network access permissions.

By focusing on a network’s current state, the firewall software recognizes and monitors in-network devices as well as the types and targets of connection they’re looking to make.

While basic and traditional firewalls are only capable of blocking traffic previously identified as malicious or unauthorized for entering or leaving the network, stateful inspection firewalls take a more targeted approach. They monitor and analyze the context and condition of all incoming and outgoing network communications in order to determine whether they’re safe and permitted or not.

A stateful inspection firewall also inspects the data inside the exchanged packets, checking whether they contain anything that poses a threat to the network’s security or integrity. They can be made to work alongside tunneling and encryption algorithms to improve the state of security by preventing data interception.

For more information, also see: Artificial Intelligence in Cybersecurity

Top 6 Stateful Firewall Features

There are numerous features that are exclusive to stateful inspection firewall solutions, with other features that they share with traditional and other varieties of firewall software, such as:

1. Policy implementation and enforcement

Stateful firewalls can be used for setting and enforcing the security and privacy policy for the entirety of the network. Similarly, depending on the reach of the firewall, user behavior and access policy can also be centralized through the firewall software or hardware.

2. Intelligent defense

Compared to static firewall solutions, stateful inspection firewalls are capable of employing AI and machine learning-powered defense mechanisms. Traffic configurations can be simultaneously applied for both inbound and outbound network traffic, saving system admins time and energy.

3. Traffic filtering

Through smart and in-depth packet inspection and network traffic filtering, stateful firewalls can be set to protect against denial of service, malware, and brute force attacks. 

4. Network monitoring

Operating on the third and fourth layers in the network, stateful firewalls monitor direct communications, and they can be integrated with application-monitoring and filtering solutions that mitigate the risk of backdoor attacks and data leaks.

5. Communication protocol management

In addition to packets and network traffic, stateful inspection firewalls also control the communications protocol the network’s users and devices are allowed to communicate with. Furthermore, it can collect information regarding the protocols in use, and control for security and privacy.

6. Efficient traffic processing

Stateful firewalls can be set to handle massive amounts of network traffic without sacrificing the quality and reliability of their security. They use a dedicated part of the network’s computational resources and memory storage to manage their logs and tracking of malicious attempts on the network to achieve future detection and analysis.

What are the Advantages of Stateful Inspection Firewalls? 

Opting for a stateful inspection firewall solution offers many benefits and advantages to your network’s security and privacy goals, such as:

• Minimizing the number of communications ports.
• Built-in network activity tracking and logging.
• Blocking network infiltration attempts through data monitoring.
• Centralizing network security management.
• Logging attacks for cyber forensics and in-software learning.
• Configurations to deter specific cyber attacks.
• Unified Threat Management (UTM) capabilities.

Packet data analysis is a highly sophisticated task that network operation centers use to identify security threats and abnormal behavior.

What are the Disadvantages of Stateful Inspection Firewalls? 

If you’re looking to implement a stateful inspection firewall software solution as your only or primary network security solution, it’s important to understand where the technology falls short and may need to be paired with separate tools. These disadvantages include:

• Inability to monitor or properly control traffic on the seventh or application layer in the network.
• Can be rendered obsolete without frequent software updates.
• More susceptible to Man-in-the-Middle (MITM) attacks.
• Complex setup and configuration.
• Lack of support for all types of communications protocols.
• No support for user-based authentication of network connections and traffic.
• Being connection-based, they’re unable to monitor and interfere with network activity outside of established communications channels.

Examples of Stateful Inspection Firewalls

Stateful inspection firewalls are part of the global deep packet inspection and processing market, which had an estimated value of $16.41 billion in 2020. It’s projected to maintain a compound annual growth rate of 22.14% over the analysis period from 2021 to 2028, reaching an estimated $80.68 billion.

The market is dominated by a number of cybersecurity and cloud service providers that offer their own stateful firewall solutions.

Some of the leading vendors on the market include:

Barracuda Networks

Barracuda Networks is a Campbell, California-based provider of cybersecurity, storage, and computer networking solutions and products. In the security realm, Barracuda offers a variety of solutions ranging from email and web surfing security to network-based and direct web hacking protection.

Barracuda’s CloudGen firewall solution is built with several security solutions and features with stateful deep packet inspection at the heart. Not only does it scan and monitor the identifiable data on the outside of packets, but it also delves into the header and contents of your network’s traffic.

Cisco Systems

Cisco Systems is a San Jose, California-based digital communications and networking company. It helps organizations and enterprises build, manage and secure their computer networks and devices through automation and data collection and analysis.

The Cisco ASA (Adaptive Security Appliance) is a firewall hardware that merges the security capabilities of a firewall, an antivirus and a VPN. While the ASA can be configured to operate as a stateless firewall, its primary condition is stateful, enabling it to defend your network against attacks before they occur.

Juniper Networks

Juniper Networks is a Sunnyvale, California-based networking and technology company that designs, develops and sells solutions that facilitate high-performance networking for enterprises and organizations. Its offerings range from network hardware and management software to network security and software-defined networking technology.

The SRX Series by Juniper is a family of high-performance service gateways that combine security, networking, and traffic routing capabilities. It’s a stateful solution that creates sessions by receiving TCP SYN packets, where the traffic matches the current communication session.

Bottom line: Stateful Inspection Firewalls

A stateful inspection firewall is a type of firewall that operates primarily on your network’s third and fourth layers. It employs in-depth packet inspection to detect and intercept threats before they can gain access to the network’s resources.

It can’t reach the application layers on its own, which leaves a gap that can be filled with a number of external security tools. The global market for stateful firewalls and packet inspection security technologies is rapidly growing, with many trustworthy cybersecurity and networking companies offering their stateful firewall solution for consumers.

They’re becoming a major part of how businesses operate thanks to their accessible pricing and availability models. Cloud services range from cloud storage to computing power, and analytics and could even be utilized for software development.

According to Allied Market Research, the global cloud services market was estimated at $325.68 million in 2019 and it’s projected to reach a value of $1.62 billion by 2030.

Understanding the various providers of cloud services in the market can help you better leverage their power to help your business, whilst being cost-effective.

Top 5 Cloud Service Providers in 2023

• Amazon Web Services: Best in Cloud Computing
• Microsoft Azure: Best in Hybrid Cloud
• Google Cloud Platform: Best in Application Deployment
• IBM Cloud: Best in Cloud-based AI
• Oracle: Best in Databases

Comparing the Top Cloud Computing Companies

1. Amazon Web Services: Best in Cloud Computing

Amazon Web Services is a cloud computing platform and a subsidiary of Amazon. It’s best known for providing on-demand access to computing power, storage, database analysis, and artificial intelligence (AI) and machine learning (ML) capabilities in a pay-as-you-go model.

Cloud Services Offered

AWS offers a wide range of cloud-based services, such as:

• Amazon Elastic Compute Cloud
• AWS Lambda
• Amazon Simple Storage Service
• Elastic Block Store
• Amazon Virtual Private Cloud
• Amazon Route 53

Key Features of Amazon’s Cloud Computing Services

There are a number of features that make AWS solutions stand out from the competition, including:

• Scalability
• Cost-effectiveness and affordability
• Reliability
• Security
• Global reach of the services

Pricing

Thanks to a pay-as-you-go model, AWS services range in price depending on the specific service being utilized, the region, and the amount of usage.

You can expect small-scale usage to cost a few dollars per month, as Amazon sets its cloud computing prices at under $0.1 per gigabyte (GB) depending on the service. However, large-scale enterprises can expect to pay anywhere from a few hundred to thousands of dollars monthly.

2. Microsoft Azure: Best in Hybrid Cloud

Microsoft Azure is a cloud computing platform and a subsidiary of Microsoft. It offers a wide range of services for both individuals and enterprises, allowing them to build, deploy, and manage applications and services in the cloud.

Cloud Services by Azure

Azure is most known for its infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS), and software-as-a-service (SaaS) offerings, but it also offers a number of cloud services, such as:

• Azure Kubernetes
• Azure SQL
• Azure Machine Learning
• Azure Backup
• Azure Cosmos DP
• Azure Active Directory

Key Features of Azure’s Cloud Services

Some features that Azure’s hybrid and private cloud services share include:

• Flexibility
• Analytics support
• Strong IT support
• Scalability
• Affordability
• Reliability

Pricing

Microsoft Azure’s services are on the most affordable end, with a pay-as-you-go model for cloud services that users can purchase per hour. Depending on the desired availability of resources, you can expect it to cost anywhere from $0.065 per hour to $1.41 per hour of usage.

3. Google Cloud Platform: Best in Application Deployment

Google Cloud Platform (GCP) is a suite of cloud computing services offered by Google. Its technical offerings range from infrastructure and platform services to computing power and database hosting.

Cloud Services by GCP

GCP offers a number of open-source and open systems suitable for both individual users and enterprises, such as:

• Google Compute Engine
• Google Kubernetes Engine
• Google Cloud Spanner
• Google Cloud Virtual Network

Key Features of GCP’s Cloud Services

There are a number of features that make GCP the best option on the market for cloud-based application deployment, including:

• Affordability
• User-friendliness
• Speed
• Advanced admin control capabilities
• Cloud-based data transfer

Pricing

For GCP’s application deployment, pricing is calculated at $15 a month per active delivery pipeline.

4. IBM Cloud: Best in Cloud-based AI

IBM has one of the most in-depth and highly developed portfolios of enterprise solutions of any provider across the tech sector. As a cloud provider, it has a global footprint offering its IaaS and PaaS solutions from data centers from all over the world.

Cloud Services by IBM Cloud

IBM’s recent focus has been on its Watson AI initiative, alongside a number of AI, machine learning, and data analytics services, all of which are interpreted within its cloud platform.

• IBM Cloud Code Engine
• IBM Hyper Protect Virtual Servers
• IBM Cloud Functions
• IBM WebSphere Application Servers
• IBM Power Systems Virtual Servers

Key Features of IBM Cloud Services

Some of IBM cloud services key features include:

• High availability
• Cloud infrastructure administration
• Open-source technology integration
• Private, public, and hybrid cloud support
• Persistent data storage

Pricing

Pricing for IBM Cloud services varies depending on the particular services and availability. However, the starting plan for the IBM Watson AI service is $140 per month, with a free version available for individual users.

5. Oracle: Best in Databases

Oracle specializes in developing and marketing computer hardware systems and enterprise-grade software products. It offers a wide range of cloud-based database solutions including its own database-as-a-service offering.

Cloud Services by Oracle

Oracle offers a wide array of cloud-based services, such as:

• Oracle Cloud Infrastructure
• Oracle Big Data Cloud
• Oracle Database Cloud Service
• Oracle Autonomous Database

Key Features of Oracle Cloud

The company’s focus is its legacy strength in database and core enterprise offerings, mainly its Oracle software on the Oracle cloud. Key features included in the services include:

• Built-in database optimization
• Reliability and security
• Cost-efficiency and affordability
• High availability
• Scalability
• Flexibility

Pricing

The average pricing for Oracle databases depends on the type of database and the features required:

• Oracle Autonomous Database costs an average of $6.80 per hour.
• Oracle Database Cloud Service costs an average of $7.50 per hour.
• Oracle Big Data Cloud costs an average of $1.50 per hour.

6. VMware: Best in Virtual Machines

VMware is the leading company in cloud-based virtualization technology and services. It offers a number of cloud-based offerings, mainly in virtual machine management in a wide variety of environments.

Cloud Services by VMware

VMware offers a number of services that operate alongside the offerings of other cloud service providers, such as:

• VMware Cloud on AWS
• VMware Cloud Foundation
• VMware Cloud Director
• VMware vCloud Air

Key Features of VMware Cloud

The most prominent features found in VMware cloud offerings include:

• Automated deployment
• High availability and reliability
• Security
• Scalability

Pricing

Hosting a VMware virtual machine on a cloud environment is relatively affordable and is charged by the hour. Hosting a VMware machine on AWS cloud costs around $0.02 per hour, while hosting more resource-intensive machines on the VMware Cloud Foundation environment can cost upwards of $0.30 per hour.

7. Salesforce: Best in Customer Relationship Management

Salesforce is a leading cloud-based customer relationship management (CRM) platform used by businesses to streamline customer interactions, sales, and marketing. It’s the leader in cloud-based CRM solutions, with over 100,000 customers.

Cloud Services by Salesforce

Salesforce has been rapidly expanding into a wider range of cloud-based enterprise software, from marketing to commerce and integration:

• Sales Cloud
• Service Cloud
• Marketing Cloud
• Commerce Cloud

Key Features of Salesforce in Cloud Services

Salesforce is continuing to evolve its Einstein AI platform, aiming to create a smart CRM assistant as an offering. Features of Salesforce services include:

• Automation capabilities
• Software personalization
• Advanced analytics
• Security
• AI-powered customer management

Pricing

The average pricing for the Salesforce Einstein AI platform is $75 per month. This pricing includes access to all of the features mentioned above as well as unlimited users and unlimited storage.

8. Alibaba: Best in Cloud Infrastructure

Alibaba is the strongest cloud leader in Asia with a growing market in the rest of the world. Recently, the company has been focusing on improving its infrastructure and cloud IaaS offerings, alongside investing in machine learning and AI.

Cloud Services by Alibaba

Alibaba Group offers a wide range of cloud computing and infrastructure services, including:

• Elastic Compute Service
• Elastic High Performance Computing
• Serverless Kubernetes
• Object Storage Service
• Data Transmissions Service

Key Features of Alibaba Cloud

Some of the key features in Alibaba’s cloud offerings are:

• High performance and computing capabilities
• Scalability
• Cost-effectiveness
• Security and reliability
• Flexibility and customizability

Pricing

With pay-as-you-go and subscription-based options, Alibaba Cloud Computing services are offered in a number of plans starting at $19.99 per month and going up to $8,000 per month for enterprises.

9. Hewlett Packard Enterprise: Best in Intelligent Edge Computing

Hewlett Packard Enterprise (HPE) is a leading provider of cloud-based technology solutions, ranging from enterprise hardware and software to dedicated Internet of Things (IoT), cloud computing, and edge computing solutions.

Cloud Services by HPE

HPE offers a number of cloud-based services that build on a foundation of flexibility and scalability, such as:

• HPE GreenLake
• IaaS, PaaS, and SaaS
• Intelligent Edge Computing
• Virtualization
• AI, ML, and Analytics

Key Features of Hewlett Packard Enterprise in Cloud

A few of the main features found in HPE’s cloud services include:

• Fast and efficient data processing
• Data access on the edge
• Built-in IoT devices communication
• Built-in monitoring and control

Pricing

HPE offers a number of payment plans and options for its cloud computing and edge computing services. They vary depending on the services and capacity included.

10. HashiCorp: Best in Cloud Infrastructure Management

HashiCorp is best known as the toolbox of the cloud, offering a variety of solutions that makes it a leader in cloud infrastructure management. Its tools are designed to help organizations better manage their cloud infrastructure more efficiently, securely, and cost-effectively.

Cloud Services by HashiCorp

HashiCorp offers a number of enterprise-grade cloud management tools, such as:

• Terraform Cloud: Infrastructure as code platform
• Vault: Security and access control management tool
• Consul: Platform connecting and configuration tool
• Nomad: Workload orchestration platform
• Vagrant: Automated environment management

Key Features of HashiCorp Cloud Services

With applications available as plug-ins to web-based tools, a few of the key features of HashiCorp’s cloud toolbox include:

• Hybrid and multicloud support
• Built-in automation and orchestration capabilities
• Secure access to cloud resources
• End-to-end encryption
• Cost-effective
• Role-based access control

Pricing

HashiCorp offers a variety of pricing plans based on the services needed and the size of an organization. Prices range from free to enterprise plans with annual fees. The average cost of HashiCorp’s cloud services is around $100 per month or $20 per user.

11. SAP: Best in Cloud Data Processing

SAP is an enterprise-grade software company that provides application and software solutions to businesses of all sizes. It’s also widely recognized in the industry of cloud data processing and the SAP/HANA database management system.

Cloud Services by SAP

SAP offers a number of cloud-based services, such as:

• SAP Cloud Platform
• SAP Analytics Cloud
• SAP Data Warehouse Cloud
• SAP SuccessFactors

Key Features of SAP in Cloud Data Processing

Some of the key features of using SAP for cloud data processing solutions include:

• High-performance capabilities
• Scalability
• Security
• Integrated big data analytics

Pricing

SAP’s cloud services are offered on a subscription basis, and prices vary depending on the service and usage. On average, businesses can expect to pay around $50–$100 per month for their cloud services.

12. Cisco Systems: Best in Networking

Cisco Systems is one of the leading manufacturers and developers of enterprise networking technology. It provides an extensive range of products and services needed to build internal networks and cloud-based solutions for businesses of all sizes.

Cloud Services by Cisco

Cisco offers a number of services and products for building and managing a network, such as:

• Application Centric Infrastructure (ACI) Anywhere
• Cisco SD-WAN Cloud OnRamp
• Cisco Cloud Security
• Meraki vMX
• Cisco SD-WAN vEdge

Key Features of Cisco Cloud Networking

Key features of Cisco networking and cloud computing solutions include:

• Scalability
• Availability and reliability
• Compatibility with Cisco tools
• Security
• High speeds

Pricing

Cisco’s ACI platform is priced on a per-application or per-user basis. However, it varies greatly depending on factors such as deployment size, included features, and the requested level of support.

13. Nutanix: Best in Cloud Computing and Virtualization

Nutanix is a cloud computing and virtualization service provider that offers an array of innovative solutions to businesses globally. It offers a cloud platform clients can use for computing and virtualization, and it is a leading player in hyper-converged infrastructure.

Cloud Services by Nutanix

Nutanix offers a number of cloud computing and virtualization services, such as:

• Nutanix AHV
• Nutanix Clusters
• Nutanix Calm
• Nutanix Xi Leap
• Nutanix Xi Frame

Key Features of Nutanix in Cloud Computing

Key features of Nutanix cloud services include:

• Scalability
• High availability
• Hybrid and multicloud support
• User-friendliness
• Integrated data services

Pricing

Nutanix offers a number of pricing plans that vary per product. Some products are charged on a per user per month pace, while others require an upfront commitment payment.

14. ServiceNow: Best in IT Services and Operations Management

ServiceNow is a provider of IT services management (ITSM), IT operations management (ITOM), and IT business management (ITBM) solutions. It helps businesses of all sizes manage and monitor their third-party applications and workflows in the cloud.

Cloud Services by ServiceNow

ServiceNow offers a number of cloud-based IT services, such as:

• Software Asset Management
• ServiceNow Cloud Management
• SecOps
• Governance, Risk, and Compliance

Key Features of ServiceNow in IT Cloud Computing

A few of the key features found in ServiceNow solutions include:

• Automation
• Built-in analytics
• Security
• Self-service portal
• Multicloud support

Pricing

ServiceNow’s pricing model is subscription-based with the cost varying depending on the number of users and the type of service offered. It can vary from $500 to $2,500 per month or a $30,000 to $60,000 payment for packages.

15. Adobe: Best in SaaS

Adobe is the leading company in providing creative software solutions that are cloud-based, native, or on a SaaS basis. It mainly excels in the creative industries of graphic design, video editing, and web development.

Cloud Services by Adobe

Some notable cloud services offered by Adobe are:

• Adobe Creative Cloud
• Adobe Document Cloud
• Adobe Experience Cloud
• Adobe Marketing Cloud

Key Features of Adobe in SaaS

A few of the key features found in Adobe’s SaaS solutions include:

• Collaboration
• Personalization
• Built-in analytics
• Artificial intelligence tools
• Document management
• User-friendliness

Pricing

Adobe offers a number of monthly and annual plans for its products that vary in cost depending on the included features. However, they range from $9.99 per month to $79.99 per month.

16. Workday: Best in Cloud Project and Resource Management

Workday is a provider of some of the best cloud-based project and business resource management solutions. Its comprehensive platform allows businesses to better manage their projects, employees, resources, and finances online.

Cloud Services by Workday

Cloud-based services and SaaS offered by Workday include:

• Human Capital Management (HCM)
• Payroll Processing
• Analytics and Reporting
• Planning, Budgeting, and Forecasting
• Financial Management

Key Features of Workday in Cloud-based SaaS

Some of the key features of using Workday’s cloud-based solutions include:

• Remote access to services
• Scalability
• Integrated HCM and finance
• AI and machine learning capabilities
• Ensuring regulatory compliance

Pricing

Workday pricing varies based on a variety of factors, such as the number of users, the modules being used, and the level of support required. As a result, there is no single price for Workday that applies to all customers.

What to Look for in a Cloud Computing Company?

Cloud computing has become an essential part of the modern business landscape, providing companies with flexible, scalable, and cost-effective solutions for their IT needs. However, when searching for a cloud computing company, it is important to take into consideration the various factors that can affect the quality and reliability of the services provided.

Global reach and experience are important considerations because they’re an indication of the company’s ability to provide reliable and secure cloud services. Also, a company with a long history of success and a large client base is more likely to have the resources and experience to handle your needs.

Another key consideration is the technical support and customer service the company offers. You need to be confident you can access the help you need when you need it, whether it’s technical support, troubleshooting, or general guidance. Look for cloud computing companies that offer 24/7 customer support and have a reputation for excellent customer service.

Most importantly, the types of cloud services they offer are critical when determining whether a cloud computing and services company is the right fit for your business.

Choosing a Top Cloud Computing Company

As cloud computing services continue to play a role in the future of many industries and businesses, it’s important to understand the differences between their services and how they would best suit your own needs and requirements.

There are many excellent cloud computing companies that specialize in a different subdomain of cloud computing, supplying the solution to a different business needs.

This type of build allows companies to allocate a single infrastructure to several end users, rather than individually managing the maintenance and updates of multiple environments.

This article will help you understand how multi-tenancy works as well as its advantages and disadvantages and the various types of databases used in them.

Table of Contents

• How Multi-Tenancy Works
• Advantages of Multi-Tenancy
• Disadvantages of Multi-Tenancy
• 3 Main Types of Multi-Tenant Databases
• 3 Multi-Tenant Architecture Examples
• Choosing Between Multi-Tenant and Single-Tenant Architecture

How Multi-Tenancy Works

The term “tenant” is used to describe the group of users or applications that share access to the same hardware resources. In a multi-tenant architecture, all users share access to the same infrastructure resources that could facilitate collaborative work such as memory, network controller, and access to system resources.

It’s used often in cloud computing, enabling service providers like Amazon Web Services, Microsoft Azure, and Google Cloud to offer a more affordable shared-tenancy option on the public cloud. However, it can also be utilized by software-as-a-service (SaaS) companies or companies with internal software that needs to be distributed to employees in various departments and physical locations.

Multi-tenant architecture works by utilizing virtual machines (VMs). On the same physical server, they’re able to create multiple VMs that all share the same hardware but operate as separate computers in complete independence from one another. This guarantees the user’s security and privacy, especially if the cloud environment is shared with foreign individuals and entities.

This is the opposite of single-tenancy, in which the server runs one instance of the operating system and one application. This one application could be something simple like file and print apps, complex like web or application servers, or mission-critical such as a database.

What is the Difference Between a Multi-Tenant and Single-Tenant Architecture?

Single-tenancy is largely seen as the “deluxe” option, in that a client operates in a solely dedicated environment. In a multi-tenant environment, each customer shares the software application along with a single database, so multiple people from the same company can access the database. Still, even in multi-tenancy, each tenant is isolated from other tenants.

The chief advantage of multi-tenant hosting is that it is less expensive. Resource pooling greatly reduces the cost since companies only pay for what they need. And since multi-tenancy is part of a SaaS provider, companies are not paying for on-premises hardware.

Functions like system monitoring and servicing the deployment become shared among all of the customers, which makes it less expensive as the cost is spread around.

Advantages of Multi-Tenancy

The multi-tenant model is used by numerous reputable cloud providers because it’s sought after by users and clients in a wide variety of fields. The following are a handful of multi-tenancy’s most notable advantages.

Reduced Costs

Multi-tenant cloud architecture models tend to be more cost-efficient than their single-tenant counterparts. This is because most service providers follow a pay-as-you-go pricing model, where companies don’t have to pay for the entirety of the cloud environment if they’re not occupying or using it.

The cost of a single environment is, instead, shared by all of the tenants. This not only includes the costs of the hardware but also all of the software and maintenance work going into keeping the environment running.

It’s Highly Scalable

Working with cloud service providers is highly scalable. Companies don’t need to plan the purchase and onward maintenance of an extension to their environments; they simply request a larger offering.

This also goes the other way around. If companies need to scale down operations, they’re not left with unused server space that still needs maintenance. The down-scaling process is just as easy and seamless as upscaling.

Maintenance-Free

With multi-tenancy, companies are buying into a done-for-you product that already includes all of the necessary maintenance work for its software and hardware components, ranging from software updates and patches to ensuring availability, backup, and uptime.

All of the labor needed to maintain the environment is included in the contract and shared with other tenants.

Improved Security and Privacy

While single-tenant architecture offers more advanced security and privacy capabilities, multi-tenancy is still considerably more secure than relying on other methods of sharing data and software resources among a pool of users.

The security and privacy of the data processed on the multi-tenant cloud are guaranteed and maintained by the service provider. Additionally, having everything in the same environment allows for effective threat and intruder detection and prevention, compared to spread-apart resources.

Backup and Data Restoration

Some multi-tenancy providers include a built-in data backup and recovery system that allows businesses to manage data reliably. When configuring for regular backup, it’s best to implement an option offered by service providers themselves, as they tend to be more familiar with the best way to handle data on their cloud.

Disadvantages of Multi-Tenancy

Before switching to a multi-tenant cloud offering, it’s just as important to be aware of the limitations and drawbacks of using this type of architecture. The following are a handful of multi-tenancy’s most notable disadvantages.

Lacks Customizability

Multi-tenant architecture is considered an off-the-shelf product, and since businesses will share software and hardware resources with multiple other customers, they’re limited in the changes they can implement.

This reduction in control can hinder business operations and a team’s progress online, as certain features may be missing while others are in the way.

Competing for Limited Resources

While most service providers put in their best efforts to keep the resources well-divided between various users, this isn’t always guaranteed. With multiple customers using the same system resources and computing power, companies might start suffering from “noisy neighbor” syndrome, where they can’t access the resources they need, and operations slow down.

Luckily, there are provisioning protocols that can be put in place to reduce the likelihood of this occurring. This includes load balances and elastic cloud computing.

Migration Difficulty

While multi-tenant architectures are easy to adapt, they can be hard to leave. Migrating data from a multi-tenant environment to any other type of environment can be a challenge because personal data is scattered all over the shared cloud, wherever there’s room for it.

Security and Privacy Challenges

Even with careful provisioning protocols and partitioning between the various VMs, companies are still sharing hardware with other users who aren’t authorized to access their part of the cloud. Normally, this isn’t a problem. However, malicious individuals could try to take advantage of such a vulnerability.

It could also occur unintentionally. Instances of data corruption have the possibility to spread through the entirety of the software instance. A malicious attack that was targeting other users on the shared public cloud may end up reaching a different user and their sensitive data.

Global Problems and Downtime

By outsourcing data and operations to an external cloud managed by a third-party service provider, companies risk losing access to critical data and information in the case of a technical error. Also, cloud environments are susceptible to downtime; although, it’s minimal with the top providers.

3 Main Types of Multi-Tenant Databases

In a multi-tenant environment, multiple customers share the same application, in the same operating environment, on the same hardware, and with the same storage mechanism and database.

This is how Salesforce and every other SaaS operator run. Every tenant is a customer or user who has common access and specific privileges in the software instance.

The database, however, is another matter. There are three ways to architect a database in a multi-tenant system.

1. Shared Database, Shared Schema

The simplest and most straightforward application of a multi-tenant architecture involves the sharing of multiple schemas for the same database. A schema refers to the construction of a database, and it’s usually made out of database tables that relate to one another.

The tables are used to manage the simultaneous access to the same dataset, like when two people are attempting to manipulate the same table or data entry at the same time.

This database architecture is the cheapest and easiest to manage for the host. Additionally, it’s highly scalable to accommodate more tenants.

2. Shared Database, Multiple Schemas

Sharing a single database through multiple schemas is another way to manage a multi-tenant environment. With multiple schemas inside a single database, a business can have sub-databases that can divide datasets without having to set up multiple separate databases.

Unlike shared schemas, this approach allows each schema to operate in complete isolation from the rest of the database. This is suitable for applying different rules and regulations and various datasets to respect international data management laws, for example.

This approach is, however, more costly, as each individual division of the database requires its own administrative efforts. Not to mention, the scalability of the environment is somewhat limited.

3. Multiple Databases, Multiple Schemas

The multiple database approach takes the separations of schemas and datasets a step further. Clients can have different divisions of data on completely separate databases, such as segregating for sales, customers, and employees, or dividing by region.

The host would have to install the application separately for each client on their database, which adds a layer of complexity to management, maintenance, and scalability to this type of multi-tenancy deployment as well as the costs.

On the flip side, this approach to multi-tenancy affords the clients a higher level of data isolation, improving the privacy and security of their data.

3 Multi-Tenant Architecture Examples

In utilizing virtual systems in VMs, a single system would have to handle numerous instances, all running several versions or even different operating systems. Also, each one of those instances has to run its own application along with its associated database.

When implemented into a multi-tenant architecture, all of the instances within a VM have to share the same base operating system, applications, and database access. In fact, this is the same model that’s used in infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS), and SaaS offerings.

Since IaaS, PaaS, and SaaS rely on resource sharing, from hardware to software, they use multi-tenancy in running their environment. This also enables them to create high-scalability offers for customers.

That’s how 50 people from the same company can work on Salesforce CRM. Similarly, a SAP system is composed of a database back end and web application servers that host web services in a highly scalable manner. The web services that make up the SaaS app are exposed to different customers via different domain names. Scaling is achieved by starting more services.

The following are three examples of a multi-tenancy architecture:

1. URL-Based SaaS

URL-based SaaS, also known as web-based SaaS, is a method of delivering software service over the internet that can be accessed through a dedicated URL. This is an alternative approach to the installation of a desktop app keeping it up to date on the client’s front. This approach to SaaS is easier to do and allows for less complex software and hardware management.

Using a URL as the primary method of SaaS deployment is also easier for the host service provider, as they’d only have to manage a single domain and database. Each client would have a specific subdomain to access their part of the service, like subdomain1.maindomain.com, subdomain2.maindomain.com, and so on.

For the host, data management and security are handled at the applications level, rather than individually for each client. Many SaaS providers operate using this model, especially those that put a web app interface between the user and the primary database. Furthermore, the host can set up different Domain Name System (DNS) entries depending on the customer’s needs and how they’d like their traffic to be filtered.

The difference in URL allows the clients some level of customization. Of course, it’s limited since the architecture is still multi-tenant, but clients can implement their own local testing or even changes to the user interface (UI) and user experience (UX).

2. Multi-Tenant SaaS

In a multi-tenant SaaS structure, multiple customers are made to share the same software and hardware in order to cut costs and management efforts. Usually, this is done through the sharing of a single instance of the software along with its supporting data and information.

This approach tends to be slightly more complex for the host due to the number of databases and schemas accessed by clients, along with the restrictions they need to put in place at the database level. However, this is still how many SaaS apps operate as it often allows for more direct interaction with the database, cutting back on lag and wait times.

Another benefit to utilizing a multi-tenant approach to SaaS applications is the increase in computing capacity per customer. Also, individual customers won’t have to worry about server and processing power capacities, but simply access the system and pay according to the resources they use.

Similarly to other approaches to multi-tenancy, this reduces the customization options for customers. Dedicated upgrades tend to be time-consuming and more complex to implement without negatively affecting the rest of the environment for the remainder of the customers.

3. Virtualization-Based SaaS

Virtualization-based SaaS, also known as containerized SaaS, is the most complex SaaS setup approach.

Through virtualization, the SaaS provider would be creating an entirely separate virtual version of all resources needed to run the software service, including the servers, desktop, operating system files, storage, and network access. Those would have to co-exist on the same hardware infrastructure without interacting with or influencing one other.

When it comes to implementing a multi-tenant architecture alongside virtualization, regular interaction between the containers, applications, and databases is essential. This is what makes it incredibly complex to maintain. Such structures tend to require specialized container orchestration tools to manage the communication and influence between individual containers, like Kubernetes and Docker.

One example of a virtualized SaaS environment is Amazon Web Services, where Amazon hosts a number of platforms and software that are available to a large number of business clients and users.

This approach to SaaS allows for more customizability for each individual user. Also, scalability is instantaneous and doesn’t sacrifice the software’s capabilities or limit access to the client’s own dataset.

Choosing Between Multi-Tenant and Single-Tenant Architecture

Choosing between single- and multi-tenant often comes down to a choice of on-premises versus the cloud. For instance, there is no single-tenant version of Salesforce, and in contrast, major databases tend to be single-tenant so as to have full access to resources.

Security of data is clearly a concern, but that falls primarily on the shoulders of SaaS providers. They are the ones responsible for monitoring tenants and making sure there is no data bleed from one customer to another, and they do a good job of it.

“If the cost to the end user is acceptable regardless of model, multi-tenant versus single tenant is really just a trade-off between change control and acceptable security risk,” said Morey Haber, chief security officer at BeyondTrust and member of the Forbes Technology Council. “If you always want to be on the latest version, either model is acceptable. You just have to manage the change control yourself.”

The client’s primary responsibility for securing the data falls to the client’s device. All of the major SaaS providers do offer two-factor authentication to secure logins. After that, it’s up to the client to maintain the security of the endpoint device.

Multi-tenancy is at the heart of cloud computing. It is designed to help scale up thousands of users both within an enterprise and externally as companies interact and do business. Whether it’s a Salesforce account or an app the company built on AWS for customers, multi-tenancy can scale through public and private cloud and provide true economies of scale.

Implementing a Multi-Tenant Architecture

Multi-tenant architecture is an approach to data structuring, usually in a cloud environment, that allows multiple users to access and share the software and hardware resources of the environment. This is done with complete separation between individual customer entities to ensure the privacy and security of their data.

It’s more cost-efficient for both the service host and the customer. It’s also highly scalable and guarantees access to the latest version of the software and hardware of the service. However, it offers fewer customization opportunities, and the resources may be put under strain if not managed properly by the host.

Choosing between a multi-tenant and a single-tenant architecture depends on numerous factors, ranging from cost and privacy and security needs to availability and the type of service in use.

Companies, corporations, and organizations alike can use raw data to collect information about their targets. This, however, requires them to structure and organize the data into a form that’s easier to read and visualize into diagrams and graphs.

This article will help aid you in understanding the various use cases of raw data and how it’s processed by data analysts and scientists. You can also learn more about big data with our library of courses on TechRepublic Academy!

Table of Contents

• How Is Raw Data Used?
• Collecting Raw Data
• How Raw Data Is Processed in 5 Steps
• 8 Examples of Raw Data
• Why Is Raw Data Valuable?

How Is Raw Data Used?

Raw data is data that’s been collected from one or multiple sources but is still in its initial, unaltered state. At this point, and depending on the collection method, the data could contain numerous human, machine, or instrumental errors, or it lacks validation. However, any change that serves to improve the quality of the data is known as processing, and the data is no longer raw.

As a resource, raw data has infinite potential, as it comes in a variety of shapes and types, from databases and spreadsheets to videos and images.

Collecting raw data is the first step toward gaining a more thorough understanding of a demographic, system, concept, or environment. It’s used by business intelligence analysts to extract useful and accurate information about the condition of their business, including audience interest, sales, marketing campaign performance, and overall productivity.

Raw data is often cherished for having limitless potential. That’s because it can be recategorized, reorganized, and reanalyzed in several different ways to yield different results from a variety of perspectives — as long as it’s relevant and has been validated to be credible.

Collecting Raw Data

How data is collected plays a key role in its quality and future potential. Accuracy, credibility, and validity can be the difference between a database of raw data that’s a wealth of information and insights and a waste of space that barely produces any actionable results.

The first and most important step of collecting raw data is to determine the type of information you’re hoping to extract from the database afterward. If it’s userbase and customer information, then online and in-person surveys should focus on a specific age and geographical demographic, whether the process is done in-house or outsourced to a third-party company.

Other types of raw data may require planning in advance. For instance, collecting data from log records would require having a monitoring system in place for anywhere from a few weeks to a year to collect data before being able to pull it.

Second is the collection method. Choosing the appropriate technique can reduce the percentage of human or machine errors you’d have to scrub out when cleaning a raw database. Generally, electronic collecting methods tend to result in lower error rates, as you’d be eliminating the factor of illegibility of handwriting or hard-to-understand accents of slang in the case of audio and video recordings.

Once you’ve determined the source, scope, and methodology, only then does the actual data collection begins. Raw data tends to be large in volume and highly complex, and the actual volume of data acquired can only be estimated during the collection process. An accurate number is only found after the first step of processing the data, which is cleaning it of errors and invalid data points and entries.

How Raw Data Is Processed in 5 Steps

Data analysts, business intelligence tools, and sometimes artificial intelligence (AI) applications, all work together in order to transform raw data into processed and insightful data.

1. Preparing the Data

After acquiring the data through the various collection methods available, you’d then need to prepare it for processing. That’s because raw data, on its own, is considered “dirty,” carrying lots of errors and invalid values. Not to mention, the lack of a homogeneous structure and unification of formats and measuring units, especially if the data comes from a variety of sources or regions.

During data preparation, the data is cleaned, sorted, and filtered according to standard in order to eliminate unnecessary, redundant, or inaccurate data. This step is absolutely essential to ensure high-quality and reliable results from analysis and processing. After all, the results can only be as good and as accurate as the data being fed into the processing tools.

The cleaning step can be simplified or accelerated by using more reliable tools when gathering the data.

2. Inputting the Data

Data inputting, sometimes referred to as data translation, is a step that converts the data into a form that’s machine-readable depending on the tools and software that will, later on, be used in the analysis process.

In the case of digitally collected data, this step is minimal. Though, some structuring and changing of file format might be needed. However, for handwritten surveys, audio recordings, and video clips, it’s important to either manually or digitally extract the data into a form the processing software is capable of understanding.

3. Processing the Data

During this stage, the previously prepared and inputted raw data goes through a number of machine learning and AI-powered statistical data analysis algorithms. Those are responsible for interpreting the data in its raw form into insights and information by searching input for trends, patterns, anomalies, and relationships between the various elements.

This step of the process varies greatly depending on the type of data being processed, whether it comes from an online database, user submissions, system logs, or data lakes. Data scientists and analysts who are well familiar with the data itself and the type of information the organization is looking to extract are capable of fine-tuning and configuring the analysis software as needed.

4. Producing the Output

At this stage, the raw data has been fully transformed into usable and insightful data. It’s translated into a more human-friendly language and can be represented as diagrams, graphs, tables, vector files, or plain text.

This makes it possible to be used in presentations where shareholders and executives with little to no technical skills are able to fully comprehend it.

5. Storing the Data

The results produced by the analysis process should be stored in a safe and accessible location for later use. This is because even processed data can be further analyzed for more details by focusing on a certain area.

This step is critical if the data contains sensitive company information or user data and information. The storage quality needs to be on par with the rest of the company’s data and information, and it must abide by local and applicable data privacy and security laws, such as the GDPR and the CCPA.

Types of Data Processing

There are many data processing methods that can be used depending on the source of the raw data and what it is needed for. The following are six of the various processing types to choose from.

Real-time Data Processing

Real-time data processing allows organizations to extract and output from inputted data in a matter of seconds. This type is best suited for a continuous stream of data rather than an entire database.

Real-time data processing is used most in financial transactions and GPS (global positioning system) tracking.

Batch Data Processing

Batch processing handles data in chunks. The data is collected over a relatively short period of time ranging from daily analysis to weekly, monthly, and quarterly. The result is more accurate than real-time processing, and it’s capable of handling larger quantities of data. That said, it takes more time and is generally more complex to accomplish.

Batch data processing is used in employees’ payroll systems as well as in analyzing short-term sales figures.

Multi-processing

Multi-processing is a time-efficient approach to data processing, in which a single dataset is broken down into multiple parts and analyzed simultaneously using two or more CPUs (central processing units) within a computer system. This type is used for large quantities of raw data that would take an exceptionally long duration to analyze without parallel processing.

Multi-processing is most often used in training machine learning and AI models and in weather-forecasting data.

Distributed Data Processing

Distributed data processing (DDP) is an approach that breaks down datasets too large to be stored on a single machine and distributes them across multiple servers. Using this technique, a single task is shared among multiple computer devices, taking less time to complete and reducing costs for data-reliant businesses.

Thanks to its high fault tolerance, DDP is great for processing raw data from telecommunications networks, peer-to-peer networks, and online banking systems.

Time-sharing Data Processing

Time-sharing data processing allows multiple users and programs to utilize access to the same large-scale CPU unit. This allocation of computer resources allows for the processing of multiple different datasets simultaneously using the same hardware resources.

Time-sharing data processing is mainly used with centralized systems that handle the input and requests of users from multiple endpoints.

Transaction Data Processing

Transaction data processing is used for processing a steady stream of incoming data and sending it back without interruptions. Considering it’s resource-intensive, it’s mostly used on larger server computers responsible for interactive applications.

8 Examples of Raw Data

Raw data is a term that applies to a wide variety of data types. The only criteria for this label are for the data to be in its most crude form and haven’t been under any form of cleaning or processing.

In fact, raw data is more common than you might think, as it allows the utmost freedom and control over the information derived from the database. It can be divided into two categories, quantitative and qualitative data, depending on the values they measure.

Quantitative Raw Data

Quantitative data is raw data that consists of countable data, where each data point has a unique numerical value. This type of data is best used for mathematical calculations and technical statistical analysis.

Some examples of quantitative raw data include:

Customer Information

As long as answers are collected in numerical values or through predetermined multi-choice questions with no room for free answers, this is considered quantitative data. This includes data such as height, age, weight, residential postal code, and level of education.

Sales Records

Records detailing the quantity and frequency of sales of specific goods and services are considered quantifiable data. This can help to determine which variety of products is more popular with customers and at which time of the year.

Combined with customer information, you can even process for more targeted results, such as discovering which particular demographic of customers are most likely to purchase which offering.

Employee Performance

Data on employee performance can include working hours, overall productivity, quality of produced work, and compensation. It can help to calculate the return on investment of your company’s overall staff members, determining whether they’re bringing more financial value than they’re getting paid.

The various metrics, whether submitted through digital or paper surveys by the employees or collected through the internal network and activity monitoring software are quantifiable data.

Revenue and Expenses

Revenue and expenses are strictly quantitative values for a company. Using revenue and expenses data can involve tracking financial activity within an organization, including revenue coming from sold goods and services as well as acquired capital in investment, and comparing it against the expenses of the given period.

This raw data is used to produce the net revenue, which can then be further analyzed to determine which areas of the company have acceptable or unacceptable levels of return on investment.

Qualitative Raw Data

Qualitative data is data that can be recorded and observed in a non-quantifiable and non-numerical nature. It rarely includes numbers and is usually extracted from answers that vary per participant through audio and video recordings, and even one-on-one interviews.

Some examples of qualitative raw data include:

Open-Ended Responses on a Survey

In open-ended survey questions, the respondents are free to structure their own answers instead of choosing one of the predetermined responses. The data cannot be lumped together when it’s raw the same way numbers can be, but it offers a more authentic and insightful view of the thoughts and opinions of the survey takers.

Photographs

While photographs can be categorized in countless ways, there’s a lot of overlap that prevents the use of quantitative measuring methodologies. When training machine learning models for computer vision capabilities, working with raw photographic data is essential.

Customer Reviews

While the 5-star or 10-star rating of a product or service is quantitative data, the reviews left by the customers aren’t. The responses would need to be analyzed on a scale of positive to negative, and highlight the suggestions and pain points experienced by each customer.

News Reports and Public Opinion

Collecting data from news reports and articles that include the name of your company can be a great way to gain an understanding of public opinion. This data is, however, qualitative and cannot be immediately separated into positive and negative coverage, along with the details of praise and criticism mentioned without cleaning and processing the dataset.

Why Is Raw Data Valuable?

Having access to high-quality and reliable raw data serves several purposes, particularly in the realm of business intelligence. It allows experts the chance to access key statistical and predictive analytics to help shape decision-making.

Despite being an experience of trial and error, where not every processing attempt of raw data will result in actionable insights and information, companies can still try to regain and retain as much information as possible from the raw data they input into processing tools.

Some reasons why businesses heavily rely on in-house collected and outsourced raw data sources may include:

• Starting Point: Raw data is the initial source for all data-based decisions on the executive level. It permits you to create compelling charts and graphs of overarching analytical statements about the conditions of the company and anticipated future affairs.
• Data Integrity: Because raw data hasn’t been cleaned, processed, or altered, you can trust that no part of it has been subject to removal or adjustment. This, in return, guarantees more accurate results that haven’t been touched by humans or machines.
• Compatibility With Machine Learning: Machine learning and AI algorithms are incapable of analyzing data after it’s been processed and translated into more human-friendly languages. Datasets are only legible for intelligent models if they’re raw and unaltered.
• Backup Resource: With access to raw data, you can always check your work against it post-processing in case you run into a problem and need to measure findings against the data in its original state.

Raw Data in Business Intelligence

Business intelligence is an overarching concept that combines multiple practices to help better guide the processes of business decision-making through data-based insights and information. It covers business analytics, data visual representation, and data mining, in addition to database management systems and tools.

Raw data is critical in business intelligence, as it offers a reliable source of information. That’s especially important for data-reliant businesses such as healthcare, retail, and manufacturing.

Without accessible raw data, companies are confined to whatever format processed data comes in, and there’s always the risk the data has been processed in error or is misaligned with strategy.

“Any industry has the chance to drive innovation by transforming raw data into gold—if they have the digital tools to do it,” said Ben Gitenstein, vice president of product management at Qumulo and member of the Forbes Technology Council. “File data is growing exponentially, and it’s become increasingly challenging for organizations to manage.

“Retailers are manufacturers in the traditional sense, but they’ve managed to leverage the raw data they have to also become digital manufacturers, updating their services for customers through personalized shopping recommendations and improved supply chains.”

Improving Customer Satisfaction With Insights From Raw Data

Up-to-date raw data is essential in all industries, but especially in fields where the company is capable of further optimizing operations for more profit, fewer costs, and higher levels of customer satisfaction.

You can source data internally by asking existing customers to take a short survey rating their experience with the services or goods your company offers.

Alternatively, you can outsource the work to a data collection company that would target a specific demographic. Either way, raw data that’s specific to your work model and isn’t derived from a large-scale generic database available for free online or prepackaged for sale is the only way to gain direct insights into the opinions and suggestions of customers and clients.

Also, because the data is raw and hasn’t been processed, you can run it through a larger number of processing methodologies and tools to get varying results and standardize your tests. The larger a data sample is and the more expert-level analysis you run, the more familiar you can become with your customers and clients, and shift your business to meet their demands and requests.

Building Valuable Insights Starting With Raw Data

Raw data is data that hasn’t been cleaned, organized, or processed in any capacity. While it can’t directly output information and insights as it is, running it through multiple processing stages can refine it up to a point where insightful graphs, diagrams, and tables can become comprehensible for the average data analyst.

Making use of accurate and up-to-date raw data can be incredibly beneficial, from prompting a data-backed decision-making process and offering unique insights on the inner workings of a system or demographic to its ability to improve the trust of both customers and shareholders.

For companies, individual data models are built around the specific needs and requirements of the organization, and they can be visualized on various levels of abstraction depending on the information that needs to be extracted for the dataset. This type of work is often done by a team of data engineers, data analysts, and data architects, along with database administrators who are familiar with both the original database and the organization’s needs.

Before implementing a data modeling framework into your company’s information systems, it’s important to first understand what makes a database useful and usable for information extraction and how it can help you map out the connections and workflows needed at the database level.

This article can help you gain a thorough and wide-scale understanding of how data modeling works, what its various types are, and how it can benefit your business.

Table of Contents

• 3 Types of Data Modeling Categories
• 4 types of Data Modeling Infrastructure
• How Data Modeling Works
• What are the Features of Data Modeling
• 5 Benefits of Data Modeling
• Top 4 Data Modeling Tools

3 Types of Data Modeling Categories

There are different types of data modeling techniques that can be divided into three main categories: conceptual, logical, and physical. Each type serves a specific purpose depending on the format of data used, how it’s stored, and the level of abstraction needed between various data points.

Conceptual Data Model

Conceptual data models, also referred to as conceptual schemas, are high-level abstraction forms of representing data, but they’re also the most simple. This approach doesn’t go in-depth into the relationship between the various data points, simply offering a generalized layout of all of the most prominent data structures.

Thanks to their simple nature, conceptual data models are often used in the first stages of a project. They also don’t require a high level of expertise and knowledge in databases to understand, making them the perfect option to use in shareholder meetings.

Key Differentiators

High-abstraction conceptual data models are used to showcase what data is in the system. Generally, they include surface-level information about the data such as classes, characteristics, relationships, and constraints. They’re suitable for gaining an understanding of a project’s scope and defining its basic concepts.

Pros

• Starting point for future models.
• Defines the scope of the project.
• Includes shareholders in the early design process.
• Offers a broad view of the information system.

Cons

• Low returns on time and effort.
• Lacks deep understanding and nuance.
• Not suited for larger systems and applications.
• Insufficient for the later stages of a project.

Examples

There are countless applications of conceptual data modeling outside of the need for developing or improving an information system. It can be used to showcase the relations between different systems or steps ofr a process.

For an order management system, an abstract diagram can help present the relationship between the various operations that go on when a customer places an order. It can also draw a clear relationship between the storefront — digital or physical — and the invoicing system, order fulfillment department, and order delivery.

Logical Data Model

Logical data models, also referred to as logical schemas, are an expansion on the basic framework laid out in conceptual models, but it considers more relational factors. It includes some basic annotations regarding the overall properties or data attributes, but it still lacks an in-depth focus on actual units of data.

Key Differentiators

This model is particularly useful in data warehousing plans, as it’s completely independent of the physical infrastructure and can be used as a blueprint for used data in the system. It allows for a visual understanding of the relationship between data points and systems without being too invested in the physicality of the system.

Pros

• Performs feature impact analysis.
• Easy to access and maintain model documentation.
• Speeds up the information system development process.
• Components can be recycled and readapted according to feedback.

Cons

• The structure is difficult to modify.
• Lack of in-depth details of data point relations.
• Errors are difficult to spot.
• Time- and energy-consuming, especially for larger databases.

Examples

Logical data modeling is more suitable for databases with a number of complex components and relationships that would need mapping. For instance, using logical modeling to map an entire supply chain, you can have easy access to not only the attribute names but also the type of data and its indicators for mandatory and non-nullable columns.

This approach to data representation is considered database-agnostic, as the data types are still abstract in the final presentation.

Physical Data Model

Physical data models, also referred to as physical schemas, are a visual representation of data design as it’s meant to be implemented in the final version of the database management system. They’re also the most detailed of all data modeling types and are usually reserved for the final steps before database creation.

Key Differentiators

Physical data models conceptualize enough detail about data points and their relationships to create a schema or a final actionable blueprint with all the needed instructions for the database built. They represent all rational data objects and their relationships, offering a high-detail and system-specific understanding of data properties and rules.

Pros

• Reduces incomplete and faulty system implementations.
• High-resolution representation of the database’s structure.
• Direct translation of model into database design.
• Facilitates detection of errors.

Cons

• Requires advanced technical skills to comprehend.
• Complex to design and structure.
• Inflexible to last-minute changes.

Examples

Physical data modeling is best used as a roadmap that guides the development of a system or application. By being a visual representation of all contents of a database and their relations, it enables database administrators and developers to estimate the size of the system’s database and provide capacity accordingly.

4 Types of Data Model Infrastructure

In addition to the three primary types of data modeling, you can choose between several different design and infrastructure types for the visualization process. Choosing the infrastructure would determine how the data is visualized and portrayed in the final mapping. For that, there are four types you can pick from.

Hierarchical Data Model

Hierarchical data models are structured in a way that resembles a family tree, where the data is organized in parent-child relationships. This type allows you to differentiate between records with a shared origin, in which each record can be identified by a unique key belonging to it, determined by its place in the tree structure.

Key Differentiators

Hierarchical data modeling is most known for its tree-like structure. Data is stored as records and connected through identifiable links that represent how they influence and relate to one another.

Pros

• Simple and easy to understand.
• Readable by most programming languages.
• Information can be removed and added.
• Fast and easy to deploy.

Cons

• Structural dependence.
• Can be bloated with duplicate data.
• Slow to search and retrieve specific data points.
• Cannot describe relations more complex than direct parent-child links.

Examples

Hierarchical data modeling is best used with easily-categorized data that can be split into parent-child relations.

One example where this is highly beneficial is for the fulfillment of sales, in which numerous items exist under the same name but can be differentiated by associating with one sale order at a time. In this scenario, the sale order is the parent entity, and the items are the child.

Relational Data Model

Unlike hierarchical data models, relational data models aren’t restricted to the parent-child relationship model. Data points, systems, and tables can be connected to each other in a variety of manners. This type is ideal for storing data that needs to be retrieved quickly and easily with minimal computing power.

Key Differentiators

Relational data models can be differentiated by checking whether they follow ACID characteristics, which are atomicity, consistency, isolation, and durability.

Pros

• Simplicity and ease of use.
• Maintains data integrity.
• Supports simultaneous multi-user access.
• Highly secure and password-protected.

Cons

• Expensive to set up and maintain.
• Performance issue with larger databases.
• Rapid growth that’s hard to manage.
• Requires a lot of physical memory.

Examples

Relational data models are best suited for use with serial information that’s related but can be beneficial separately.

One example is maintaining a database of members, customers, or users of an establishment. The structure of rows and columns can be used to store the first and last names, birth dates, Social Security numbers, and contact information that are grouped within one another as relating to a single individual.

Entity-Relationship (ER) Data Model

Entity-relationship data models, also referred to as entity relationship diagrams (ERDs), are a visual way of representing data that relies on graphics depicting the relationship between data points, usually people, real-world objects, places, and events, in the information system.

This type is most commonly used to better understand and analyze systems in order to capture the requirements of a problem domain or system.

Key Differentiators

ER data models are best used to develop the base design of a database as it delves into the basic concepts and details required for implementation, all using a visual representation of the data and relationships.

Pros

• Simple and easy to understand.
• Compatibility with database management systems (DBMSs).
• More in-depth than conceptual modeling.

Cons

• Difficult to expand and upscale.
• Retains some ambiguity.
• Only works best for relational databases.
• Long-winded and wordy.

Examples

ER diagrams represent how databases are related as well as the flow of processes from one part of the system to the next. The overall representation resembles a flowchart but with added special symbols to better explain the various relations and operations occurring in the system.

One prominent example of ER models is used with public institutions like universities to help them better categorize and parse their demographic of students. ER diagrams showcase student names and connect them with their taken courses, mode of transportation, and occupation.

Object Oriented Data Model

Object oriented data models are a variation on conceptual data modeling that instead uses objects to make complicated real-world data points more legible by grouping entities into class hierarchies. Similarly to conceptual modeling, they’re most often used in the early stages of developing a system, especially data-heavy multimedia technologies.

Key Differentiators

Instead of focusing solely on the relationship between data points and objects, object-oriented data modeling centers the data of the real-world object, clustering them along with all related data, such as all personal information and contact information of an individual.

Pros

• Easy to store and retrieve data.
• Integrates with object-oriented programming languages.
• Improved flexibility and reliability.
• Requires minimal maintenance efforts.

Cons

• Lacks a universal data model.
• Highly complex.
• Higher chances of performance issues.
• Lack of adequate security mechanisms.

Examples

Object-oriented data models allow businesses to store customer data by separating individual attributes into various tables but without losing the links between them.

An object in the data model represents the type of customer, which can then be followed in either direction to collect the remainder of the customer’s information without having to involve unnecessary parts of the database.

How Data Modeling Works

Data modeling is the process of visualizing the relationship between and the locations of various data points by a data modeler — usually a database administrator or data architect that works in close proximity to the data. The first and most important step of data modeling is determining the right type for the applications.

Depending on whether you’re using conceptual, logical, or physical data modeling, the resulting diagram could carry varying degrees of simplicity, detail, and abstraction. Identifying user access patterns can also help to determine the most critical parts of the database to represent in order to adhere to your business’s needs.

Before concluding the data modeling process, it’s important to run a handful of test queries to identify the validity of the data model.

What are the Features of Data Modeling

When it comes to searching for a suitable data modeling tool or picking out the appropriate data modeling approach, there are functionalities and capabilities you should expect. The following are some of the key features of any approach to data modeling.

Data entities and their attributes

Entities are abstractions of real pieces of data. Attributes are the properties that characterize those entities. You can use them to find similarities and make connections across entities, which are known as relationships.

Unified modeling language (UML)

UML are the building blocks and best practices for data modeling. They’re a standard modeling language that help data professionals visualize and construct appropriate model structures for their data needs.

Normalization through unique keys

When building out relationships within a large dataset, you’ll find that several units of data need to be repeated to illustrate all necessary relationships. Normalization is the technique that eliminates repetition by assigning unique keys or numerical values to different groups of data entities.

With this labeling approach, you’ll be able to normalize, or list only keys, instead of repeating data entries in the model every time entities form a new relationship.

5 Benefits of Data Modeling

Data modeling offers several distinct benefits to enterprises as part of their data management.

Improves data quality

Data modeling allows you the opportunity to clean, organize, and structure data beforehand. This enables you to identify duplicates in data and set up monitoring to ensure its long-term quality.

Saves time and energy

Despite being an added step that may need to be repeated multiple times throughout the project’s development process, modeling a database before work begins sets up the scope and expectations for the project.

Clear-cut data modeling ensures you don’t end up spending more time and resources on a step than is necessary and justified by the data itself.

The inclusion of nontechnical departments

The early stages of a project’s development are oftentimes too abstract for individuals with little to no technical experience to fully understand.

The visual nature of data modeling, especially conceptual data modeling, allows for more collaboration and discussions among shareholders and nontechnical departments such as marketing and customer experience.

Promotes compliance with regulations

Privacy and security regulations need to be included from the earliest stages of a system’s development. Data modeling enables developers to fit all of the necessary parts for compliance into the design’s infrastructure.

By understanding how data points relate and interact with one another, you can better set the bar for secure and safe data governance.

Improves project documentation

Documentation is essential to encapsulate the development process of a system and helps with solving any future problems or inconsistencies that may arise as well as with training future employees. By building an in-depth data model early on in the development process, you’ll be able to include that into the system’s documentation to allow for a deeper understanding of how it works.

Top 4 Data Modeling Tools

Data modeling has become a pillar of the growing data governance market, particularly because of the streamlined data visibility data models allow enterprises to provide to non-data professionals within their organizations.

The data governance market is expected to grow at a compound annual growth rate of over 21% between 2021 and 2026, with an estimated value of $5.28 billion by 2026, according to a study by ReportLinker. Much of this growth will be attributed to increasing global data regulations, most notably the General Data Protection Regulation (GDPR) in the EU.

This highly lucrative market has been the driving factor of countless tech services providers creating their own data modeling tools — some open source and free to use.

Enterprise Architect

Enterprise Architect is a graphical tool designed for multi-user access, suitable for both beginner and advanced data modelers. Through a number of built-in capabilities ranging from data visualization, testing, and maintenance to documentation and reporting, it can be used to visually represent all of the data in your system’s landscape.

Apache Spark

Apache Spark is an open-source processing system for large data management and modeling. It can be used completely free of charge with no licensing costs, providing users an interface for programming clusters with implicit fault tolerance and parallelism.

Oracle SQL Developer Data Modeler

The Oracle SQL Developer Data Modeler is part of the Oracle environment. While not open source, it’s free to use for developing data models and creating, browsing, and editing conceptual, logical, and physical data models.

RapidMiner

RapidMiner is an enterprise-grade data science platform and tool that can be used to collect, analyze, and visually represent data. It’s perfect for beginner and less-experienced users with a user-friendly interface.

It integrates seamlessly with a wide variety of data source types, ranging from Access, Teradata, and Excel to Ingres, MySQL, and IBM DB2 to name a few. Furthermore, it’s capable of supporting detailed data analytics across a broad artificial intelligence (AI) life cycle.

Bottom Line: Data Modeling

Data modeling is an approach to visually representing data in graphs and diagrams that vary in abstraction, level of detail, and complexity. There are multiple types and approaches to data modeling, but its primary benefit is to help conceptualize and lead the development of a database-reliant system.

From free, open-source tools to enterprise-ready solutions and platforms, you can automate and simplify the bulk of the data modeling process, making it more accessible to smaller teams and urgent projects on a limited budget.