What is Big Data Processing and Distribution Software?

Companies are seeking to extract more value from their data but they struggle to capture, store, and analyze all the data generated. With various types of business data being produced at a rapid rate, it is important for companies to have the proper tools in place for processing and distributing this data. These tools are critical for the management, storage, and distribution of this data, utilizing the latest technology such as parallel computing clusters, and modern Big Data processing distribution platforms now build in CI/CD and cloud integration so new pipelines can be deployed without manual infrastructure work. Unlike older tools which are unable to handle big data, this software is purpose built for large scale deployments and helps companies organize vast amounts of data. The amount of data businesses produce is too much for a single database to handle. As a result, tools are invented to chop up computations into smaller chunks, which can be mapped to many computers to perform computations and processing. Businesses that have large volumes of data (upwards of 10 terabytes) and high calculation complexity reap the benefits of big data processing and distribution software. However, it should be noted that other types of data solutions, such as relational databases are still useful for businesses for specific use cases, such as line of business (LOB) data, which is typically transactional. What Types of Big Data Processing and Distribution Software Exist? There are different methods or manners in which big data processing and distribution takes place. The chief difference lies in the type of data that is being processed. Stream processing With stream processing, data is fed into analytics tools in real time, as soon as it is generated. This method is particularly useful in cases like fraud detection where results are critical at the moment. Batch processing Batch processing refers to a technique in which data is collected over time and is subsequently sent for processing. This technique works well for large quantities of data that are not time sensitive. It is often used when data is stored in legacy systems, such as mainframes, that cannot deliver data in streams. Cases such as payroll and billing may be adequately handled with batch processing.

What are the Common Features of Big Data Processing and Distribution Software?

Based on G2 reviews, developers and big data architects evaluate big data processing and distribution software by comparing processing speed, integration breadth, and infrastructure management overhead. Big data processing and distribution software, with processing at its core, provides users with the capabilities they need to integrate their data for purposes such as analytics and application development. The following features help to facilitate these tasks: Machine learning: This software helps accelerate data science projects for data experts, such as data analysts and data scientists, helping them operationalize machine learning models on structured or semistructured data using query languages such as SQL. Some advanced tools also work with unstructured data, although these products are few and far between. Serverless: Users can get up and running quickly with serverless data warehousing, with the software provider focusing on the resource provisioning behind the scenes. Upgrading, securing, and managing infrastructure is handled by the provider, thus giving businesses more time to focus on their data and how to derive insights from it. Storage and compute: With hosted options, users are enabled to customize the amount of storage and compute they want, tailored to their particular data needs and use case. Data backup: Many products give the option to track and view historical data and allows them to restore and compare data over time. Data transfer: Especially in the current data climate, data is frequently distributed across data lakes, data warehouses, legacy systems, and more. Many big data processing and distribution software products allow users to transfer data from external data sources on a scheduled and fully managed basis. Integration: Most of these products allow integrations with other big data tools and frameworks such as the Apache big data ecosystem.

What are the Benefits of Big Data Processing and Distribution Software?

Analysis of big data allows business users, analysts, and researchers to make more informed and quicker decisions using data that was previously inaccessible or unusable. Businesses use advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing to gain new insights from previously untapped data sources independently or together with existing enterprise data. Using big data processing and distribution software, companies accelerate processes in big data environments. With open-source tools such as Apache Hadoop (along with commercial offerings, or otherwise), they are able to address the challenges they face around big data security, integration, analysis, and more. Scalability: In contradistinction, with traditional data processing software, big data processing and distribution software is able to handle vast amounts of data in an effective and efficient manner and has the ability to scale as the data output increases. Speed: With these products, businesses are able to achieve lightning-fast speeds, giving users the ability to process data in real time. Sophisticated processing: Users have the ability to perform complex queries and are able to unlock the power of their data for tasks such as analytics and machine learning.

Best Big Data Processing And Distribution Systems: User Reviews from September 2026

Databricks

Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics, and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. Founded in 2013 by the original creators of Apache Spark™, Delta Lake, MLflow and Unity Catalog, Databricks is built on an open lakehouse architecture that brings data, analytics and AI together. The platform is used by data engineers, data scientists, analysts, developers, machine learning teams, AI teams and business users to collaborate across the full data and AI lifecycle. Key Databricks capabilities include: - Data engineering: Build, automate and manage reliable batch, streaming and real-time data pipelines. - Analytics and business intelligence: Run SQL analytics, create dashboards and enable business teams to explore data. - Data governance: Discover, secure and manage data and AI assets across teams, clouds and workloads. - Machine learning and AI: Develop models, build generative AI applications and create production-grade AI agents. - Data applications: Build and deploy data-driven applications using governed enterprise data. Available across AWS, Azure and Google Cloud, Databricks helps organizations work across clouds, reduce data silos and simplify collaboration across teams and tools. Customers use Databricks for use cases such as customer personalization, fraud detection, predictive maintenance, real-time analytics, cybersecurity, healthcare research, financial risk management, supply chain optimization and AI-powered decision-making. Databricks is used across industries including financial services, healthcare and life sciences, retail, manufacturing, energy and the public sector. Organizations use the platform to modernize data infrastructure, accelerate AI adoption and turn enterprise data into business value.

Average Rating: 4.6/5.0

Total Reviews: 1,333

How Do G2 Users Rate Databricks?

Has the product been a good partner in doing business?: 8.9/10 (Category avg: 8.8/10)
Real-Time Data Collection: 8.8/10 (Category avg: 8.8/10)
Machine Scaling: 9.0/10 (Category avg: 8.6/10)
Data Preparation: 9.1/10 (Category avg: 8.6/10)

Who Is the Company Behind Databricks?

Seller: Databricks Inc.
Company Website:
Year Founded: 2013
HQ Location: San Francisco, CA
Twitter: @databricks
92,269 Twitter followers
LinkedIn® Page: www.linkedin.com
14,336 employees on LinkedIn®

Who Uses This Product?

Who Uses This: Data Engineer, Data Analyst
Top Industries: Information Technology and Services, Financial Services
Company Size: 48% Large, 38% Medium

What Do G2 Reviewers Say About Databricks?

AI-generated summary from verified user reviews

Pros

Users enjoy the ease of use and extensive features of Databricks, streamlining data warehousing and machine learning tasks.
Users value the seamless integrations with AWS services that enhance efficiency and support diverse business needs.
Users appreciate the ease of use of Databricks, enhancing their experience with its intuitive interface and efficient features.
Users value the seamless collaboration provided by Databricks, enhancing teamwork on data projects and insights sharing.
Users value the effective data management features of Databricks, simplifying their workflows and enhancing decision-making.

Cons

Users face a steep learning curve with Databricks, as its complexity can be confusing for newcomers.
Users note that the cost of Databricks can be quite high, particularly for large data projects and limited free options.
Users express frustration over missing features in Databricks, limiting its effectiveness for complex deployments and custom setups.
Users find the steep learning curve of Databricks challenging, particularly for those unfamiliar with big data tools.
Users face unintuitive UI issues that lead to random errors and complicate the experience for non-technical users.

What Are Recent G2 Reviews of Databricks?

"Databricks Streamlines ETL and Analytics with Scalable Notebooks"

Rating: 5.0/5.0 stars

— Diana C.

Product	Best for	User Review
Databricks 4.6/5(1,362)	Unified lakehouse ETL and ML pipelines	"Databricks Streamlines ETL and Analytics with Scalable Notebooks"
BigQuery 4.5/5(1,223)	Serverless SQL analytics on petabyte-scale datasets	"Easy-to-Use Cloud Tool with Shareable, Saved Queries"
IBM watsonx.data 4.4/5(174)	Federated lakehouse querying across hybrid data sources	"Clean, Smooth UI with Excellent Onboarding and Infrastructure Visuals"
Snowflake 4.6/5(762)	Elastic data warehousing with compute-storage separation	"Easy, Efficient Data Extraction with Clear Database Insights"
Apache Spark for Azure... 4.1/5(13)	Azure-native distributed ETL and in-memory analytics	"Seamless Azure Integration with Effortless Spark Scaling on HDInsight"
Amazon EMR 4.2/5(70)	AWS-native Spark and Hadoop cluster orchestration	"Fast, Easy Big Data Processing with Amazon EMR and AWS Integration"
AWS Lake Formation 4.4/5(38)	Secure data lake ingestion with AWS-native access control	"Simplifies Governance, Requires Experience for Setup"
MS SQL 4.4/5(2,288)	Relational big data pipelines with Microsoft-ecosystem integration	"Powerful Performance Tuning, Strong Security and Environment Flexible"
Azure Synapse Analytics 4.4/5(38)	Unified ETL and big data analytics on Azure	"Unified Analytics Platform with Seamless Azure Integration"

Best Big Data Processing And Distribution Systems

How Many Big Data Processing And Distribution Systems Products Does G2 Track?

Category Stats (Sep 2026)

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G2 Grid® for Big Data Processing And Distribution Systems

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What Do G2 Reviewers Say About Databricks?

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