Meanwhile, the ever-growing value of big data is driving data scientists to increase their access along with their ability to mine and analyze data for insights and information by adapting how data repositories are managed in relation to the type of data they house. While some legacy and on-premises solutions continue to be indispensable, a mass shift to the cloud is proving to be the answer to many of the problems organizations are facing in regards to data volume, compatibility, and accessibility.

Companies of various sizes and industries adapt to progress at different rates and may migrate data for different reasons. The five major trends in data migration in 2023 reflect the industry’s attitude as a whole toward solving specific problems.

1. A Shift Towards Data Lakehouses

Data lakehouses are open data management architectures that combine the flexibility, cost-efficiency, and scale of data lakes with the data management abilities of data warehouses. The result is a unified platform used for the storage, processing, and analysis of both structured and unstructured data. One reason this approach is gaining popularity is a sustained desire to break down data silos, improve quality, and accelerate data-driven decision-making within organizations.

Data lakehouses’ large capacity enables them to handle large volumes of data in real time, making them ideal for live consumer data, Internet of Things (IoT) networks, and physical sensors. Their ability to process data from multiple sources makes it easier for organizations to gain insights from multiple data streams.

Additionally, the centralization of data lakehouses allows for a unified, up-to-date view of data across an entire organization, facilitating inter-departmental collaboration on data-based projects and greatly reducing the costs and complexity of hosting multiple data storage and processing solutions.

2. A Focus on AI and Automation in Governance

Data migration helps organizations keep pace by ensuring their systems are able to accommodate the ever-increasing flow of new data. To simplify the already complex and time-consuming task of data governance, many companies are turning to artificial intelligence (AI)/machine learning (ML) algorithms and automation.

These technologies have revolutionized data migration by allowing organizations and data managers to automate some of the many manual processes it involves. It also enables them to reduce the risk of failures due to human error and execute the migration process more accurately and efficiently. With the help of smart algorithms, organizations can also better gain insights into their data than previously possible while identifying and eliminating data duplicates, which may reduce storage costs and improve performance.

Thanks to the recent boom in AI and ML-based technologies being developed and partially launched by a number of cloud computing giants, including Microsoft and Google, the role of such technologies in the more critical processes of data migration is likely to increase as the models become more and more sophisticated.

3. Expanding Storage Capacity

The world is expected to generate around 120 zettabytes of data in 2023, a nearly 24 percent increase from the prior year. This data is generated from a wide variety of sources, including IoT devices, log files, and marketing research. In this case, bigger is better—many organizations are looking to embrace big data by expanding storage capacities through novel methods of data storage.

One prominent option is cloud storage, which stands out as a scalable, reliable solution that’s also easily accessible over the internet. However, one of the challenges that arises with data migration to the cloud is maintaining security during transit. Organizations must carefully plan their migration strategies—including encryption, backup, and recovery plans—to protect financial and medical data and personal information while it is at risk.

Organizations can also benefit from an increase in agility and compounded value of structured and unstructured data by expanding their overall data storage capacity through flexible and scalable means.

4. Handling Unstructured Data

Most data sources produce semi-structured or unstructured data that cannot be easily organized and categorized. Company mergers and system updates are prominent sources of unstructured data—the initial categorization and structure of the data must be shed in order to fit into a different system. Unstructured data tends to be much larger in volume than structured data carrying the same amount of information and insights.

This poses a problem when migrating data. Not only is the massive volume costly to transfer and secure, both in-transit and at-rest, but it cannot be analyzed or stored in relational databases. However, that doesn’t make it void of value, as many organizations are seeking data science and migration solutions that would help structure incoming data.

Solving the unstructured data problem is a time-sensitive endeavor for many organizations. That’s because situational data quickly loses its value with time and gets replaced by more recent data, often in greater volume.

5. A Move From On-Premises Legacy Systems to Cloud Storage

Most data originates in the cloud, from such sources as digital logs, monitoring devices, customer transactions, and IoT devices and sensors. Many organizations are finding it more efficient to migrate entirely to the cloud rather than remaining split between legacy on-premises systems and cloud storage.

This approach would involve the integration of legacy data and systems with already-present data stored in the cloud, creating a more unified and comprehensive approach to data management and enabling remote access. A move to the cloud would also be accompanied by embracing multi-cloud architectures, allowing companies to optimize costs by working and switching between multiple cloud providers simultaneously.

Moving entirely to the cloud would also facilitate data storage segmentation, enabling data managers to differentiate data by type, purpose, and origin in addition to sensitivity and the level of security it may require. Organizations with data split between legacy and cloud systems may seek to unify the multiple sources in the cloud, enabling them to develop a richer, more holistic view of their data and how they might be able to use it.

Predictions for the Future of Data Migration

Data migration is expected to continue to grow in popularity alongside the exponential growth in the average volume of data produced annually by organizations. As businesses increasingly adopt cloud-based alternatives to everything from computing and processing to hosting software, cloud-based data solutions are likely to follow.

This will spark a wave of innovation, creating modern tools and technologies that aim to simplify the data migration process, ensuring the security and reliability of data in transit. Combined with the latest advancements in AI, ML, and automation, the migration process is likely to become faster, more efficient, and less prone to errors, making data migration as a concept more accessible to startups and emerging businesses who want to shift to the cloud and make the most out of their data.

Data migration involves moving data from one system to another, often to upgrade or replace a legacy system. ETL—which stands for Extract, Transform, and Load—is the process of pulling data from one or more sources, transforming it into a suitable format, and loading it into the target location. The key difference is scale. Data migration is typically used to transfer whole databases while ETL is often used for smaller datasets or parts of a database. Organizations are more likely to use data migration when replacing an outdated system, moving to the cloud, or merging with another company because it allows for better business continuity by moving all of the company’s data wholesale.

How Does Data Migration Work?

At a high level, data migration is simply the process of moving a database from one storage system to another. There are several approaches, including transfering the data directly or exporting it externally and then importing it to the new system. The goal of the process is to ensure all data is retained during the move and that it remains consistent with the new system’s data format.

One of the biggest challenges of data migration comes when moving it from an outdated system to a new system, which can increase the likelihood of data loss or corruption. It’s important to have a migration strategy in place that takes both systems and transfer paths into consideration.

How Does ETL Work?

ETL is a migration process that involves extracting data from its sources, transforming it to fit the specific format of the target system, and loading it into the new system. Done in three separate steps, ETL is often used by smaller organizations or when smaller data sets are required for a hyper-specific purpose, such as annual reports or business intelligence.

The first step is data extraction, which can be done using a variety of methods from querying a database to directly reading a file. Once the data has been extracted, it may or may not need to go through a format transformation process using a series of rules and algorithms. Finally, the transformed data is imported, or loaded, into the target system.

ETL’s advantage is that it allows the transfer of only specific data rather than an entire database, which can save time and resources and reduce the risk of error and inconsistencies. But the process tends to require more manual intervention than data migration and it can interrupt business continuity at times.

What Are Some Popular Tools for Data Migration and ETL?

There are a number of tools available for data migration and ETL, each with its own strengths and weaknesses—here are a few of the most popular.

Microsoft SQL Server Migration Assistant

Microsoft SQL Server Migration Assistant simplifies the process of migrating data from to SQL servers. It supports a variety of database sources, such as Oracle, MySQL, and Access, as well as many data formats, including JSON, hierarchical data, spatial data, and XML data.

Pentaho Data Integration Kettle

Kettle is Pentaho’s free, open-source data migration tool capable of performing ETL processes, data cleaning, and data synchronization tasks. It supports various data formats, including XML data, CSV, and JASON, and can extract data from sources ranging from SQL and NoSQL databases to APIs, applications, and web services.

Informatica PowerCenter

Informatica PowerCenter is a comprehensive data integration solution that combines a wide range of proprietary and open-source data integration strategies and solutions. It enables companies to export, transform, and load data from a variety of sources, but it’s best used for real-time integration.

AWS Database Migration Service

AWS Database Migration Service (DMS) is a cloud-based solution that facilitates the movement of data from old systems to the AWS cloud. It supports a variety of database sources, including Oracle, SQL Servers, MySQL, and PostgreSQL. Also, as a fully-managed service, it ensures minimal downtime and continuous replication as well as automation of scalable features.

Talend Open Studio

Talend Open Studio is a free and open-source data integration tool that combines various data and application integration services, such as ETL, data quality, data profiling, and MDM (Master Data Management). It supports a wide range of data formats and can be used for batch and real-time data migration and integration.

Quest Migration Manager

Quest Migration Manager is a data migration and consolidation solution that facilitates and automates the process of moving data for active directories. It’s best used for the migration and restructuring of user accounts, data, and systems with a minimal impact on business continuity.

Bottom Line: Data Migration vs. ETL

While data migration and ETL may seem identical at first glance, there are a number of differences between the two approaches that better suit them for different tasks. Data migration is a good fit for moving entire databases, while ETL works best for limited or specific data sets. Choosing the right method depends on the volume of the data, the type of migration, and whether the data needs to be reformatted for the new database.

The need for enterprise-grade data storage has propelled the global public cloud market skyward. It is expected to almost double from $445 billion to $988 billion between 2022 and 2027. The richness and diversity of the market can make it daunting for organizations looking to upscale and upgrade their services.

Here’s a brief guide to some of the leading providers of public cloud solutions and how to choose the right provider for specific business needs.

Best Public Cloud Providers:

Amazon Web Services (AWS)

Amazon subsidiary Amazon Web Service (AWS) emerged in 2006, revolutionizing how organizations access cloud computing technology and remote resources. It offers a vast array of resources, allowing it to design and execute new solutions at a rapid pace to keep up with the global market’s evolution.

AWS’s services range from Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) to the simplified and easy-to-access and use, Software as a Service (SaaS) cloud models. Key offerings include:

Amazon EC2

Amazon Elastic Compute Cloud (EC2) is a web service that delivers secure and scalable computing capacity based in the cloud designed to facilitate web-centric computing for developers. This allows them to obtain and configure capacity with minimal friction with the infrastructure.

The services are available in a wide selection of instance types, from public to private and hybrid, that can be optimized to fit different use cases.

Amazon S3

Amazon Simple Storage Service (S3) is an object-based storage service known for its industry-leading scalability, security, performance and reliable data availability. Organizations of various sizes and industries can use it to store and retrieve any amount of data at any time, providing easy-to-use management features in order to organize data and configure it finely-tuned access control.

Amazon RDS

Amazon Relational Database Service (RDS) simplifies the setup and operations of relational databases in the cloud. AWS is responsible for automating all the redundant and time-consuming administrative tasks, such as hardware provisioning, database setup and data backup and recovery. This is best used to free up developers’ time, allowing them to focus on more pressing tasks like application development and design.

Use Cases and Industries

As a multinational corporation, AWS is able to cater to a wide variety of industries at different stages of development, from startups to established enterprises, as well as the public sector.

Use cases include:

• Application hosting
• Data processing
• Data warehousing
• Backup and restoration

This makes AWS’s service particularly useful for data-intensive industries such as healthcare, telecommunications, financial services, retail, and manufacturing.

Microsoft Azure

Microsoft launched Azure in 2010 as a comprehensive suite of cloud-based services designed to help businesses and organizations navigate the challenges that come with digital adoption. Azure was built on Microsoft’s decades-long specialty—software design—allowing its public cloud solutions to integrate seamlessly with other Microsoft products.

Azure also includes a multitude of services that range from computing and database management to storage and machine learning, including the following:

Azure Blob Storage

Azure Blob Storage is an object-based and scalable storage platform used for data lakes, warehouses and analytics as well as backup and recovery. It’s optimized for massive amounts of unstructured data, like text or binary values.

Azure Cosmos DB

Azure Cosmos DB is a database management service that’s multi-modeled, globally distributed and highly scalable, ensuring low latency that supports various APIs to facilitate access. It supports data models including SQL, MongoDB, Tables, Gremlin and Cassandra.

Azure Virtual Machines

Azure’s Virtual Machines are on-demand, scalable resources that provide users the flexibility of virtualization without the need to invest in or maintain the infrastructure that runs it. They also run on several Microsoft software platforms, supporting numerous Linux distributions for a more versatile experience.

Use Cases and Industries

When combined with Microsoft’s software and enterprise-focused approach to the public cloud, Microsoft Azure’s comprehensive services make it the ideal solution for numerous use cases, such as:

• Big data and analytics
• Application hosting
• Disaster and backup recovery
• IoT applications

Azure’s services are used by businesses and organizations in a number of industries such as e-commerce, healthcare, insurance and financial institutions.

Google Cloud Platform (GCP)

First launched in 2011 as a cloud-based subsidiary of Google, Google Cloud Platform (GCP) is a suite of cloud computing services that uses the same infrastructure as Google’s software products. Its industry-leading creations from TensorFlow and Kubernetes are some of the greatest examples of Google’s sophisticated solutions, and include the following:

Google Cloud Engine

Also known as Google Kubernetes Engine (GKE), Cloud Engine is a fully managed, user-ready environment used to deploy containerized applications and web services. Based on the open-source Kubernetes system, it’s developed by Google for managing workloads, enabling developers to flexibly and efficiently develop apps and deploy applications.

Google Cloud Storage

Google Cloud Storage is a fully managed and scalable object-oriented storage service. It includes many services ranging from serving website content to storing data for archival purposes and disaster recovery.

Google Compute Engine

Google Compute Engine is a cloud-based virtual machine solution that’s scalable and flexible. It allows users to tailor their computing environment, meeting specific requirements, and offering flexible pricing and cost savings.

Use Cases and Industries

GCP is used by organizations and businesses in IT, healthcare and retail, as well as the financial industry. Use cases include:

• Data analytics and machine learning
• Application development
• Storage and database management

IBM Cloud

IBM launched IBM Cloud in 2011 as a collection of cloud-based computing services. It leverages IBM’s vast experience, offering a robust approach to enterprise-grade public cloud platforms with an emphasis on open-source technologies and supporting a diverse set of computing models, including the following:

IBM Cloud Functions

IBM Cloud Functions is IBM’s Function as a Service (FaaS) solution built on Apache OpenWhisk. It enables developers to execute code in response to events as well as direct HTTP calls without having to manage their own hardware infrastructure.

IBM Cloud Virtual Servers

These flexible and scalable cloud computing solutions support both public and dedicated virtual servers. They’re the right balance of computing power to cost, allowing companies to deploy the servers globally and reach their customers.

IBM Cloud Databases

IBM Cloud Databases is a family of managed, public databases that support a wide variety of data models that include relational, key-value, document, and time-series applications.

Use Cases and Industries

IBM Cloud services a wide range of industries with its diverse offerings, such as IT and technology companies, healthcare organizations, financial institutions and retail providers, as well as the public sector. Use cases include:

• Public and hybrid cloud implementation
• Blockchain development
• Data analytics and management
• AI and machine learning

Oracle Cloud Infrastructure

The Oracle Cloud Infrastructure is a part of Oracle’s comprehensive cloud offering, first launched in 2012. The public cloud solution leverages Oracle’s long history in enterprise computing and data processing, enabling the company to provide robust, scalable and secure services, including the following:

Oracle Cloud Storage

Oracle Cloud Storage is a high-performance, scalable and reliable object storage service. It’s capable of storing an unlimited amount of data of any content type, including analytic data and rich content like images and video.

Oracle Cloud Compute

Oracle Cloud Compute encompasses a variety of cloud computing options set to meet the needs of small-scale applications to enterprise-grade workloads. It’s available as both bare metal and virtual machine instances, giving users a flexible, scalable environment for running applications.

Oracle Cloud Functions

Oracle’s Function as a Service (FaaS) offering lets developers write and deploy code without worrying about underlying infrastructure. It’s based on the open-source Fn Project and allows developers to build, run, and scale applications in a fully managed serverless environment.

Use Cases and Industries

With its versatile offerings, Oracle Cloud Infrastructure is able to serve a wide range of industries such as application development, insurance, healthcare and e-commerce in both the private and public sectors. Use cases include:

• High-performance computing (HPC)
• Enterprise resource planning (ERP)
• Data backup and recovery
• Data analytics

Alibaba Cloud

Launched in 2009, Alibaba Cloud is the cloud computing faction of the Alibaba Group. As the leading cloud provider in China and among the top global providers, Alibaba Cloud capitalizes on Alibaba’s massive scale and experience with e-commerce and data processing. Services include the following:

ApsaraDB

ApsaraDB is a suite of managed database services that cover a wide range of database types including relational, NoSQL and in-memory databases. These services handle database administration tasks, allowing developers to focus on their applications rather than database management.

Alibaba Object Storage Service

Alibaba Object Storage Service (OSS) is an easy-to-use service that enables users to store, backup and archive large amounts of data in the cloud. It is highly scalable, secure, and designed to store exabytes of data, making it ideal for big data scenarios.

Alibaba Elastic Compute Service

Alibaba Elastic Compute Service (ECS) provides fast memory and flexible cloud servers, allowing users to build reliable and efficient applications with ease. ECS instances come in a variety of types, each optimized for certain workloads, making them versatile for different application scenarios.

Use Cases and Industries

In essence, Alibaba Cloud’s extensive services, coupled with its strong presence in Asia, make it a compelling choice in the public cloud market. It also serves a multitude of data-heavy industries such as technology companies, media and entertainment, financial services and education. Use cases include:

• E-commerce platforms
• Big data analytics and processing
• AI and machine learning models

Emerging Public Cloud Providers

The booming market and demand for public cloud have opened the doors for numerous technology companies to start offering their own cloud computing and storage solutions. The focus of emerging cloud providers tends to be on providing straightforward, scalable, and affordable cloud services to small and midsize businesses, and key players in addition to the ones covered in this article include DigitalOcean, Linode and Vultr. All offer developer-friendly features at affordable rates alongside high-quality customer service and support.

Factors to Consider When Choosing a Public Cloud Provider

When choosing a provider of public cloud solutions, there are several factors to consider.

Scalability and performance

The cloud service provider must be able to handle workloads and be able to accommodate growth and changes as business grows.

Security

Providers must be compliant with local and federal data security and privacy regulations. Additionally, they should be able to protect data against attacks, leaks and breaches.

Pricing flexibility

Cloud services are most known for their flexible, pay-as-you-go pricing models. Multiple tiers at varying costs allow businesses to access only the resources they need.

Integration and customer service

A public cloud solution should be compatible with existing and legacy systems, ensuring seamless integration, and should include reliable customer support and service to ensure access to solutions and assistance.

Bottom Line: Public Cloud Providers

The public cloud market offers a diverse range of options, each with its own strengths and trade-offs. AWS, Microsoft Azure, GCP, IBM Cloud, Oracle Cloud Infrastructure and Alibaba Cloud are major players, each serving a multitude of industries with a broad array of services. Simultaneously, emerging providers offer compelling alternatives, especially for certain use cases or customer profiles.

When choosing a provider, considerations over scalability, performance, security, cost, integration and support are key. By understanding these factors, businesses can make informed decisions and choose the public cloud provider that best meets their specific needs.

The marketplace of data science tools approach tasks in remarkably different ways and use different methods to aggregate and process data and generate actionable reports, graphics or simulations.

Here’s a look at 15 of the most popular tools and what sets them apart.

Data Science Tools Comparison Chart

15 Data Science Tools for 2023

Data Cleaning and Preprocessing Tools

Trifacta

Trifacta is a cloud-based, self-service data platform for data scientists looking to clean, transform and enrich raw data and turn it into structured, analysis-ready datasets.

Pros:

• Intuitive and user-friendly
• Machine Learning-based
• Integrates with data storage and analysis platforms

Cons:

• Costly for smaller projects
• Limited support for programming languages

Pricing
There isn’t a free option of Trifacta. However, there’s a Starter option at $80 per user, per month for basic functionality. The Professional option costs $4,950 per user, per year for added functionality, but requires a minimum of three licenses. There’s also the option for a desktop-based or a cloud-based free trial.

OpenRefine

OpenRefine is a desktop-based, open-source data cleaning tool that helps make data more structured and easier to work with. It offers a broad range of functions, data transformation, normalizations and deduplication.

Pros:

• Open-source and free to use
• Supports multiple data formats: CVS, XML and TSV
• Supports complex data transformation

Cons:

• No built-in ML or automation features
• Limited integration with data storage and visualization tools
• Steep learning curve

Pricing
100 percent free to use.

DataWrangler

DataWrangler is a web-based data cleaning and transforming tool developed by the Stanford Visualization Group, now available on Amazon SageMaker. It allows users to explore data sets, apply transformations and prepare data for downstream analysis.

Pros:

• Web-based with no need for installation
• Built-in data manipulation operations
• Automatic suggestions for  appropriate data-cleaning actions

Cons:

• Limited integration with data storage and visualization tools
• Limited support of large datasets
• Limited updates and customer support

Pricing
The use of DataWrangler on the Amazon SageMaker cloud is charged by the hour, starting at $0.922 per hour at 64 GiB of memory for standard instances, and at $1.21 at 124 GiB of memory for optimized memory.

AI/ML-Based Frameworks

Scikit-learn

Scikit-learn is a Python-based and open-source library that encompasses a wide range of tools for data classification and clustering using AI/ML.

Pros:

• Comprehensive documentation
• Reliable and consistent API
• Wide range of algorithms

Cons:

• Limited support for neural networks and deep learning frameworks
• Not optimized for GPU-usage

Pricing
100 percent free to use.

TensorFlow

Developed by Google, TensorFlow is an open-source machine learning and deep learning library. It enables users to deploy various models across several platforms, supporting both CPU and GPU computation.

Pros:

• Scalable and suitable for large-scale projects
• Allows for on-device machine learning
• Includes an ecosystem of visualizations and management tools
• Open-source and free to use

Cons:

• Steep learning curve
• Dynamic data modeling can be challenging

Pricing
The library is 100 percent free to use, but when deployed on the AWS cloud, the typical price starts at $0.071 per hour.

PyTorch

PyTorch is an open-source ML library developed by Meta’s AI research team and based on the Torch library. It’s known for its dynamic computation graphs, computer vision and natural language processing.

Pros:

• Simplifies the implementation of neural networks
• Easy integration with Python
• Open-source and free to use
• Strong community support and documentation

Cons:

• Few built-in tools and components
• Limited support for mobile and embedded devices

Pricing
The library is 100 percent free to use, but when deployed on the AWS cloud, the typical price starts at $0.253 per hour.

Deep Learning Libraries

Keras

Keras is a high-level neural network library and Application Programming Interface (API) written in Python. It’s capable of running on top of numerous frameworks, such as TensorFlow, Theano and PlaidML. It allows users to simplify the process of building, training and deploying data-based deep learning models.

Pros:

• User-friendly and easy to use
• Extensive documentations
• Pre-made layers and components

Cons:

• Limited compatibility with low-level frameworks
• Complex models may suffer from performance issues

Pricing
100 percent free to use.

Fast.ai

Fast.ai is an open-source deep-learning library built on top of Meta’s PyTorch and designed to simplify the training of neural networks using minimal code.

Pros:

• User-friendly interface
• Built-in optimization for deep learning tasks
• Extensive documentation and educational resources

Cons:

• Limited customization options
• Smaller active community

Pricing
100 percent free to use.

Hugging Face Transformers

Hugging Face Transformers is an open-source, deep-learning library that focuses on natural languages processing models, such as GPT, BERT and RoBERTa. It offers pre-trained models along with the tools needed to fine-tune them.

Pros:

• Large repository of ready-use models
• Supports Python and TensorFlow
• Active online community

Cons:

• Limited open natural language processing tasks
• Steep learning curve

Pricing
The library is 100 percent free to use, but when combined with AWS Cloud and AWS Inferentia2, pricing starts at $0.76 per hour.

Big Data Processing Tools

Apache Spark

Apache Spark is a distributed and open-source computing system designed to simplify and speed up data processing. It supports a wide range of tasks including data transformers, ML and graph processing.

Pros:

• In-memory data processing for higher performance
• Built-in ML and graph processing libraries
• Integrates seamlessly with Hadoop ecosystems and various data sources

Cons:

• Processing is resource-intensive
• Requires pre-existing programming knowledge

Pricing
The system is 100 percent free to use, but when deployed on the AWS cloud, typical pricing starts at $0.117 per hour.

Apache Hadoop

Apache Hadoop is an open-source, distributed computing framework that processes large volumes of data across clusters of servers and databases. It consists of Hadoop Distributed File System (HDFS) for storage and MapReduce for processing.

Pros:

• Highly-scalable and fault-tolerant
• Supports a wide variety of tools such as Apache Hive and HBase for data processing
• Cost-effective

Cons:

• Disk-based storage leads to slower processing
• Limited support for real-time data processing
• MapReduce as a steep learning curve

Pricing
The framework is 100 percent free to use, but when deployed on the AWS cloud, typical pricing starts at $0.076 per hour.

Dask

Dask is a flexible, parallel computing library for Python that enables users to scale numerous well-known workflows using APIs such as Scikit-learn and NumPy. It’s designed specifically for multi-core processing and distributed computing.

Pros:

• Interface similar to Python
• Support for dynamic, real-time computation
• Lightweight and compatible with Python workflows

Cons:

• Limited support for languages other than Python
• Not ideal for processing large datasets

Pricing
100 percent free to use.

Cloud-based Data Science Platforms

Google Colab

Google Colab is a cloud-based Jupyter Notebook environment in which users are able to write and execute Python code directly in their web browsers. It’s a collaborative platform for both data science and machine learning tasks with accelerated computations.

Pros:

• No setup or installation required
• Online access to GPUs and TPUs
• Supports real-time collaboration and data sharing

Cons:

• Limited computing resources available
• Lack of built-in support for third-party integration

Pricing
With a free version available, Google Colab pricing plans start at $9.99 per month for the Colab Pro plan and $49.99 per month for the Colab Pro+ plan; a pay-as-you-go option starts at $9.99 per 100 compute units, or $49.99 per 500 compute units.

Databricks

Databricks is a unified data analytics platform that combines ML with big data processing and collaborative workspaces, all in a managed cloud environment. It’s a comprehensive solution for data engineers, scientists and ML experts.

Pros:

• Seamless integration with Apache Spark
• Supports high-performance data processing and analysis
• Built-in tools for version control, data visualization and model deployment

Cons:

• Cost ineffective for smaller projects
• Steep learning curve
• Vendor lock-in

Pricing
With a 14-day free trial available, Databricks can be deployed on the user’s choice of Azure, AWS or Google Cloud. There’s a price calculator, enabling customization of subscriptions.

Amazon SageMaker

Amazon SageMaker is a fully managed, ML platform that runs on Amazon Web Services. It allows data scientists and developers to build, train and deploy machine learning models in the cloud, providing end-to-end solutions for data processing, model training, tuning and deployment.

Pros:

• Integrates seamlessly with the AWS ecosystem and tools
• Built-in algorithms for popular machine learning frameworks, such as MX Net, PyTorch and TensorFlow
• Wide range of tools for model optimization, monitoring, and versioning

Cons:

• Steep learning curve
• High-end pricing
• Vendor lock-in

Pricing
With a free tier available, Amazon SageMaker is available in an on-demand pricing model that allows customization of services and cloud capacity.

Factors to Consider When Choosing Data Science Tools

As the importance of data continues to grow and transform industries, selecting the right tools for your organization is more critical than ever. However, with the vast array of available options, both free and proprietary, it can be challenging to identify the ideal fit for specific needs.

There are a number of factors to consider when choosing data science tools, whether it’s data processing frameworks or ML libraries.

Scalability

Scalability is a crucial factor to consider early on in the decision-making process. That’s because data science projects often involve large volumes of data and computationally-intensive algorithms. Tools like Apache Spark, TensorFlow and Hadoop are designed with big data in mind, enabling users to scale operations across multiple machines.

It’s essential to ensure that a tool can efficiently manage the data size and processing demands of the project it is chosen for, both currently and in the future as needs evolve.

Integration With Existing Infrastructure

Seamless integration with an organization’s existing infrastructure and legacy software is vital for efficient data processing and analysis. This is where caution can prevent being locked into a specific vendor.

Many online tools and platforms, such as Amazon SageMaker and Databricks, are compatible with a number of legacy systems and data storage solutions. This enables them to complement an organization’s existing technology stack and greatly simplify the implementation process, allowing users to focus on deriving insights from data.

Community Support and Documentation

A strong online community and comprehensive documentation are particularly important when choosing data science tools to be used by smaller teams. After all, active user communities are able to provide troubleshooting assistance, share best practices, and even contribute to the ongoing development of the tools.

Tools like Keras and Scikit-learn boast extensive documentation in addition to a widespread and active online community. This makes them accessible to beginners and experts alike. When it comes to documentation, it’s crucial that the available documents include up-to-date information and are regularly updated with the latest advancements.

Customizability

The ability to flexibly customize tools is essential to accommodate unique project requirements, but to also optimize performance based on available resources. Tools like PyTorch and Dask offer some of the most useful customizability options compared to their counterparts. They allow users to tailor their data processing workflows and algorithms to their specific needs.

Determining the level of customization offered by a tool and how it aligns with a project is important to guarantee the desired level of control.

Learning Curve

While all tools have a learning curve, it’s important to find data science tools with complexity levels that match the expertise of the data science and analytics teams that will be using them.

Tools such as Google Colab and Fast.ai are known for their user-friendly and intuitive interface, but other programming-based tools, like Apache Spark and TensorFlow, may be harder to master without prior experience.

The Future of Data Science Tools

The rapid development and innovation in the fields of AI and ML are also driving the development of new algorithms, frameworks and platforms used for data science and analytics. In some instances, those advancements occur too fast, and staying informed about the latest trends ensures the ability to remain competitive in an economy reliant on deriving insights from raw data.

Automation is increasingly playing a prominent role in how data is gathered, prepared and processed. Using AI and ML, tools like AutoML and H2O.ai can be used to streamline data parsing by automating some of the numerous steps that go into the process. In fact, the growing role of automation in data science is likely to shape the industry’s landscape going forward, determining which tools and skill-set are more viable and in demand.

The same is likely to apply to quantum computing, as it holds great potential to revolutionize countless data processing and optimization problems, thanks to its ability to tackle complex and large-scale tasks. Its impact could potentially lead to new algorithms, frameworks and tools specifically designed for data processing in quantum environments.

Bottom Line: Data Science Tools

Choosing the right data science tools for an organization requires a careful evaluation of factors such as scalability, integration with existing infrastructure, community support, customizability and ease of use. As the data science landscape continues to evolve, staying informed about the latest trends and developments, including ongoing innovations in AI and ML, the role of automation and the impact of quantum computing will be essential for success in the data-driven economy.

With organizations awash in data, there’s a growing need to make it digestible, understandable and actionable for humans and not just computers. Data visualization software takes data and turns it into images that can communicate concepts and ideas and in a way that words and numbers alone cannot.

What Is a Data Visualization Tool?

Data visualization tools let users find key insights in data and display them in visual form. The practice involves pulling data from a database and creating dashboards and graphics like pie charts, bar charts, scatter plots, polar area diagrams, heat maps, timelines, ring charts, matrix charts and word clouds, to name a few.

By representing myriad data points graphically it’s possible to peer deeper into important numbers, trends, metrics and Key Performance Indicators (KPIs).

Not surprisingly, data visualization tools have moved from the domain of data scientists and IT departments and into the hands of business users. Organizations are now using visualization software to better understand such varied scenarios as customer sentiment and behavior, real-time sales, healthcare trends, departmental goals and market research. In addition, advertisers and media organizations use it to generate eye-catching graphics and infographics and display complex information in simple visuals.

Of course, different data visualization tools approach the task differently. Some lean toward more conventional Business Intelligence (BI) functions while others plug in live data from social media and various applications across an organization. Some also incorporate Artificial Intelligence and Machine Learning (AI/ML) to deliver more advanced functionality and insights. Most data visualization packages include templates and connectors for building robust models, graphics and dashboards.

If you’re in the market for the best data visualization software, take the time to understand what various vendors and applications offer, how they work and whether they’re able to accommodate your organization’s data visualization needs and budget.

How To Select The Best Data Visualization Software

When selecting a data visualization tool, it’s important to focus on several factors to narrow down the options.

• What type of visualizations do you require? Different tools provide different ways to aggregate and view data. Make sure you can easily connect to and input the data you require. Most of these packages come with a robust set of Application Programming Interfaces (APIs) for ingesting data.
• What type of platform does the software run on and what devices does it support? Some solutions are cloud-based, while others reside on desktop or mobile devices. Some vendors that support an on-premises model have applications that run only on Windows, which can present problems if you have teams using Macs. Make sure the software will work for you.
• Does the package adequately support your organization’s performance requirements? Some applications encounter difficulties with extremely large files, for example, while others don’t perform well in different situations. If the rendering engine can’t support the speed required for web pages and real-time dashboards that meet your own needs, you may have a problem.
• Does the application integrate with your workflows? Flexibility and scalability are often crucial. You may need to change templates, inputs or criteria from time to time—including other programs and platforms connected through APIs. Make sure the data visualization tool can support these changes.
• What does vendor support look like? An application may produce stunning visualizations, but building them can be difficult. If you’ll need help, will your vendor provide it? Make sure a vendor offers solid documentation and support, including videos and tutorials, and check on whether the vendor offers 24×7 phone support if you get bogged down.
• What does the package cost? Some solutions are free—Google Data Studio, for example—but may not deliver the features you need. Others may lock you into a specific cloud provider. Most vendors offer tiered pricing, including an enterprise option; review the choices carefully to find the ones that best align with your budget.
• What security protections does the solution offer? Cybersecurity is critical part of almost every aspect of computing these days. Make sure that any platform you’re considering provides adequate protections for accessing, securing and sharing data.

The Best Data Visualization Tools: Comparison Table

10 Top Data Visualization Tools and Software

Jump to:

• Databox
• Google Data Studio
• iDashboards
• Infogram
• Qlik Sense
• Sisense
• Tableau
• Visme
• Whatagraph
• Zoho Analytics

See more: What is Data Visualization?

Databox

The cloud-based business analytics platform databox generates data visualizations in real-time by pulling data from a wide variety of sources, including Google Analytics, Salesforce, HubSpot, Facebook, Mixpanel and Shopify. Databox offers more than 200 built-in dashboard templates, a robust set of APIs, metrics calculators, and mobile apps for viewing data visualizations. The vendor offers a tiered pricing model.

Pros

• Innovative features including looped data boards, scheduled snapshots and annotations
• More than 70 one-click integrations with data services
• More than 200 pre-built reports
• Intuitive interface and highly flexible visualizations

Cons

• Subpar integrations lead to inaccurate data and visualizations, according to some users
• Limited customization for reports
• Frequent bugs and crashes, according to users

Google Data Studio

Cloud-based Google Data Studio incorporates interactive dashboards and automated reporting and imports data from multiple sources, including Google Analytics, Google Ads and spreadsheets. It also integrates with more than 150 other cloud, SQL, e‑commerce and digital advertising platforms. Google Data Studio supports a wide array of data visualizations, including time series, bar charts, pie charts, tables, heat maps, geo maps, scorecards, scatter charts, bullet charts and area charts.

Pros

• Free
• Drag-and-drop interface doesn’t require coding skills or heavy technical knowledge
• Strong collaboration features
• Shareable dashboards
• Built in tool for calculating metrics and formulas
• Highly customizable

Cons

• Can be difficult to integrate with non-Google platforms
• Confusing functions, difficult to use
• Frequent bugs and crashes and subpar customer support, according to users

iDashboards

iDashboards “strives for real-time operational intelligence through rich visualization capabilities,” and combines data from upwards of 160 sources, offers hundreds of chart and design options, and builds dashboards that work on nearly any device. It also can use real-time data feeds to embed graphics and dashboard visualizations, which makes it possible to build dashboards for different organizational roles while supporting websites and mobile apps.

Pros

• Straightforward and easy-to-use drag-and-drop interface
• Pulls data from almost any source; comes with nearly 300 connectors, including all major cloud and application platforms
• Generates extremely rich data visualizations
• Highly flexible and customizable
• Pricing is attractive, particularly for SMBs

Cons

• Can be difficult to set up and configure
• Number of design options can be daunting to new users
• Some users have problems connecting to or importing very large source files
• Some premium features require additional licensing and costs

Infogram

Infogram is a cloud-based marketing and media tool that supports more than 35 types of interactive data visualization formats, including infographics, reports, dashboards, maps and charts, as well as social media assets for such sites as Facebook, LinkedIn and Pinterest. It provides a drag-and-drop interface, real-time collaboration and the ability to publish online. There’s a basic free version as well as four other tiers for creatives, SMBs and large enterprises.

Pros

• Large and varied collection of designer templates, including interactive charts, maps and animations
• Intuitive and easy to use interface
• Integrates well with Google Drive, OneDrive and Dropbox
• Powerful and elegant collaboration features for teams

Cons

• Free plan doesn’t allow customizations and file downloads to systems and devices
• More advanced features and plans can be pricey
• Some users report bugs and crashes
• No ability to work on projects offline

Qlik Sense

Qlik Sense is a self-service data analytics platform designed for a broad array of users, including executives, decision-makers and analysts. Available as both on-premise or cloud software, it provides drag-and-drop functionality and connects to numerous data sources, including Snowflake and other leading products. Qlik Sense generates a varied array of data visualizations through interactive dashboards, and the application includes an open API and toolsets.

Pros

• Powerful features and tools for building complex data visualizations from nearly any data source or set
• AI-based Smart Search feature helps users uncover data relationships
• Uses AI/ML for enhanced insights
• Real-time analytics and data visualization
• Excellent mobile device functionality

Cons

• Learning curve can be steep
• Requires some technical knowledge to use the software effectively
• Customizations can challenge some users
• Can be expensive, especially with add-ons

Sisense

The AI-powered Sisense analytics platform uses a robust set of APIs to generate data visualizations and actionable analytics. Available both in the cloud or on-premises, Sisense is highly customizable and includes data connectors for most major services including Snowflake, Salesforce, Adobe Analytics, Amazon S3, Dropbox, Facebook and numerous Microsoft applications. It’s suitable for use by non-data scientists and line-of-business users.

Pros

• Powerful features and fast and rich visualizations
• Intuitive user interface
• Customizable and flexible
• Generates reports and visualizations using natural language and other AI
• Highly rated customer support

Cons

• Slow performance with heavy data loads, according to some users
• May require knowledge of coding, including JavaScript and CSS, to format visualizations
• Documentation is lacking, particularly surrounding widgets, and can be difficult to understand according to some users

Tableau

Popular business intelligence platform Tableau works with a broad array of data sources and services from spreadsheets and conventional databases to Hadoop and cloud data repositories. It features smart dashboards and a highly interactive interface that lets users drag and drop elements, manipulate and combine data and views, and display data in numerous formats. Tableau includes robust sharing features.

Pros

• Fast and powerful
• Well-designed interface
• Consistently ranked as a leader by Gartner and others
• Supports all major platforms and works on almost any device
• Connects to hundreds of data sources and supports all major data formats

Cons

• Expensive
• Mixed reviews about customer support
• May require training to use the full set of features and capabilities on the platform
• Difficult to customize
• Lacks some important security controls

Visme

Visme is focused on creating visual brand experiences and other content, including flyers, emails, reports, e-books, embedded videos, animations and social media graphics. It incorporates a drag-and-drop interface and pulls data from numerous sources to generate illustrations, infographics, presentations and more. Visme offers a basic free service and tiered plans.

Pros

• Thousands of templates for infographics, presentations, charts, maps, documents and more
• Integrations with Slack, YouTube, Vimeo, Dropbox, Google Drive, SurveyMonkey, Mailchimp, Google Maps and many other products and services
• Strong collaboration features
• Excellent tutorials and other learning materials
• Highly rated customer support

Cons

• Some user complaints about graphics being frequently reused by different companies and websites
• Can be challenging to learn
• Interface can be slow and confusing
• Some complaints about frequent bugs
• Only more expensive plans have key privacy settings

Whatagraph 

Whatagraph is designed to handle performance monitoring and reporting, and marketing professionals use it to visualize data and build cross-channel reports. The application offers a variety of pre-designed templates and widgets and offers APIs for connecting numerous data sources, including Google Analytics, Facebook, LinkedIn, YouTube, HubSpot, Amazon Advertising and more.

Pros

• Excellent features and support for social media and marketing
• Built in integrations for more than 30 data sources
• Powerful cross-channel data integration and monitoring
• Automated features for sending reports

Cons

• Not highly customizable
• Cross-channel integrations can be complex and require considerable time to set up
• Some user complaints about the speed of the application
• Some complaints about subpar customer support

Zoho Analytics

The self-service BI and data analytics software is designed to ingest large volumes of raw data and transform it into actionable visuals and reports via dashboards. It is available in both on-premises and cloud versions. The platform can pull data from numerous sources, including Google Analytics, Mailchimp, YouTube, Salesforce and Twitter. It offers a tiered pricing model.

Pros

• More than 500 data connectors
• Strong collaborative features with security protections
• AI-based augmented analytics that let users create data visualizations using natural language

Cons

• Interface is not user-friendly or as intuitive as some users would like
• Can be slow to generate data visualization with very large data sets
• Features and support for mobile platforms and devices sometimes lacking
• Lacks flexibility, particularly in regard to changing reports, according to reviews

See more: Best Data Quality Tools & Software 2021

Bottom Line: Data Visualization Tools

As data visualization tools become increasingly available to business users in all fields, they open up possibilities for organizations to share complex data and communicate difficult ideas in clear and interesting graphical representations. The best data visualization tool is the one that best meets the needs of the user while also working with their existing data systems and applications and fitting into their budget. Most packages include templates and connectors for building robust models, graphics and dashboards, but the choice will also depend upon ease of use and the user’s technical ability.

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?