Best Enterprise Big Data Processing And Distribution Systems

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

Total Products under this Category: 123

Category Stats (Sep 2026)

  • Average Rating: 4.4/5 The average rating of products in this category, based on all submitted ratings
  • Top Trending Product: IBM Analytics Engine (+1.99%) - Among all products in this category, IBM Analytics Engine recorded the largest rating increase compared to last month

Last updated: September 26, 2026

How Does G2 Rank Big Data Processing And Distribution Systems Products?

Why You Can Trust G2's Software Rankings:

  • 30 Analysts and Data Experts
  • 9,500+ Authentic Reviews
  • 123+ Products
  • Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.

G2 Grid® for Big Data Processing And Distribution Systems

G2 Grid® for Big Data Processing And Distribution Systems plotting products by satisfaction and market presence

Highlighted products: Databricks, Google Cloud BigQuery, Amazon EMR, IBM watsonx.data, Snowflake, Microsoft SQL Server, Cloudera, and Azure Data Lake Store.

Underlying data: [Grid® JSON](https://www.g2.com/categories/big-data-processing-and-distribution/grids.json?focus%5B%5D=databricks&focus%5B%5D=google-cloud-bigquery&focus%5B%5D=amazon-emr&focus%5B%5D=ibm-watsonx-data&focus%5B%5D=snowflake&focus%5B%5D=microsoft-sql-server&focus%5B%5D=cloudera&focus%5B%5D=azure-data-lake-store&segment=enterprise)

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: 1,342

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: 47% 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 appreciate the ease of use of Databricks, enhancing their experience with its intuitive interface and efficient features.
  • Users value the seamless integrations with AWS services that enhance efficiency and support diverse business needs.
  • Users value the seamless collaboration provided by Databricks, enhancing teamwork on data projects and insights sharing.
  • Users value the wide array of integrated analytical features in Databricks, enhancing efficiency and collaboration in data projects.
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 find the steep learning curve of Databricks challenging, particularly for those unfamiliar with big data tools.
  • Users find the complexity of Databricks challenging, especially during initial setup and navigation of advanced features.
  • Users encounter complex setup challenges with Databricks initially, but support helps resolve issues quickly.

What Are Recent G2 Reviews of Databricks?

What Are G2 Users Discussing About Databricks?

Google Cloud BigQuery

BigQuery is an AI-ready, petabyte-scale, and cost-effective data warehouse that lets you run analytics over vast amounts of data in near real time. Store 10 GiB of data and run up to 1 TiB of queries for free per month.

Average Rating: 4.5/5.0

Total Reviews: 1,144

How Do G2 Users Rate Google Cloud BigQuery?

  • Has the product been a good partner in doing business?: 8.6/10 (Category avg: 8.8/10)
  • Real-Time Data Collection: 8.7/10 (Category avg: 8.8/10)
  • Machine Scaling: 8.7/10 (Category avg: 8.6/10)
  • Data Preparation: 8.8/10 (Category avg: 8.6/10)

Who Is the Company Behind Google Cloud BigQuery?

  • Seller: Google
  • Year Founded: 1998
  • HQ Location: Mountain View, CA
  • Twitter: @google
    31,899,995 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    301,144 employees on LinkedIn®
  • Ownership: NASDAQ:GOOG

Who Uses This Product?

  • Who Uses This: Data Engineer, Data Analyst
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 38% Large, 35% Medium

What Do G2 Reviewers Say About Google Cloud BigQuery?

AI-generated summary from verified user reviews

Pros
  • Users value the ease of use of Google Cloud BigQuery, enabling fast analysis without needing to manage infrastructure.
  • Users appreciate the incredible speed of BigQuery, making data processing effortless and efficient for large datasets.
  • Users value the seamless integrations of Google Cloud BigQuery, enhancing analytics and supporting various data types effortlessly.
  • Users appreciate the fast querying capabilities of Google Cloud BigQuery, enabling quick analysis of massive datasets effortlessly.
  • Users value the query efficiency of BigQuery, enabling fast analysis of massive datasets with minimal effort.
Cons
  • Users find the cost structure expensive, especially with complex queries leading to rapidly escalating charges.
  • Users often face query issues with BigQuery, as inefficient queries can rapidly increase costs and complicate budgeting.
  • Users find the cost management challenging, facing unpredictable pricing and needing strict governance to maintain budgets.
  • Users face cost issues with Google Cloud BigQuery, often leading to unexpectedly high bills and budget management challenges.
  • Users find the steep learning curve for advanced features challenging, requiring significant time and effort to master.

What Are Recent G2 Reviews of Google Cloud BigQuery?

What Are G2 Users Discussing About Google Cloud BigQuery?

Amazon EMR

Amazon EMR is a web-based service that simplifies big data processing, providing a managed Hadoop framework that makes it easy, fast, and cost-effective to distribute and process vast amounts of data across dynamically scalable Amazon EC2 instances.

Average Rating: 4.2/5.0

Total Reviews: 62

How Do G2 Users Rate Amazon EMR?

  • Has the product been a good partner in doing business?: 8.9/10 (Category avg: 8.8/10)
  • Real-Time Data Collection: 8.2/10 (Category avg: 8.8/10)
  • Machine Scaling: 8.7/10 (Category avg: 8.6/10)
  • Data Preparation: 8.8/10 (Category avg: 8.6/10)

Who Is the Company Behind Amazon EMR?

  • Seller: Amazon Web Services (AWS)
  • Year Founded: 2006
  • HQ Location: Seattle, WA
  • Twitter: @awscloud
    2,232,483 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    147,094 employees on LinkedIn®
  • Ownership: NASDAQ: AMZN

Who Uses This Product?

  • Top Industries: Computer Software, Financial Services
  • Company Size: 59% Large, 21% Small

What Do G2 Reviewers Say About Amazon EMR?

AI-generated summary from verified user reviews

Pros
  • Users value the data integration capabilities of Amazon EMR, effectively managing large datasets from multiple sources.
  • Users find Amazon EMR's ease of use beneficial for running single jobs and accessing precise error logs.
  • Users value the efficiency with large datasets in Amazon EMR, enhancing their business logic processing capabilities.
Cons
  • Users often face performance issues due to scaling complications, requiring manual tuning to optimize functionality.
  • Users report that poor performance due to slow auto-scaling affects job execution and resource availability on EMR clusters.
  • Users report slow performance in auto-scaling for nodes, often causing job failures due to resource shortages.

What Are Recent G2 Reviews of Amazon EMR?

What Are G2 Users Discussing About Amazon EMR?

IBM watsonx.data

IBM® watsonx.data® helps you access, integrate and understand all your data —structured and unstructured—across any environment. It optimizes workloads for price and performance while enforcing consistent governance across sources, formats and teams. Watch the demo to learn how watsonx.data empowers you to build gen AI apps and powerful AI agents. Free Trial available: https://ibm.biz/Watsonx-data_Trial

Average Rating: 4.4/5.0

Total Reviews: 169

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How Do G2 Users Rate IBM watsonx.data?

  • Has the product been a good partner in doing business?: 8.8/10 (Category avg: 8.8/10)
  • Real-Time Data Collection: 8.7/10 (Category avg: 8.8/10)
  • Machine Scaling: 8.7/10 (Category avg: 8.6/10)
  • Data Preparation: 8.8/10 (Category avg: 8.6/10)

Who Is the Company Behind IBM watsonx.data?

  • Seller: IBM
  • Company Website:
  • Year Founded: 1911
  • HQ Location: Armonk, New York, United States
  • Twitter: @IBMSecurity
    74,660 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    344,328 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Software Engineer, Software Developer
  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 34% Small, 32% Large

What Do G2 Reviewers Say About IBM watsonx.data?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of IBM watsonx.data, finding it reliable and efficient for data management.
  • Users value the seamless data integration and user-friendly interface of IBM watsonx.data for efficient analytics.
  • Users appreciate the organized and efficient data management of IBM watsonx.data, simplifying analytics and enhancing team collaboration.
  • Users value the seamless data source integration of IBM watsonx.data, enhancing efficiency and flexibility in their workflows.
  • Users appreciate the flexible analytics capabilities of IBM watsonx.data, enabling faster insights from diverse data sources.
Cons
  • Users find the steep learning curve of IBM watsonx.data challenging, hindering easy adoption for newcomers.
  • Users find the complexity of setting up IBM watsonx.data a barrier, especially for newcomers and small teams.
  • Users find the pricing steep for IBM watsonx.data, especially for smaller businesses with limited resources.
  • Users find the difficult setup process time-consuming, with a steep learning curve and extensive documentation review required.
  • Users find performance tuning difficult with IBM watsonx.data, especially for beginners and teams with limited IT resources.

What Are Recent G2 Reviews of IBM watsonx.data?

Snowflake

Snowflake makes enterprise AI easy, efficient and trusted. Thousands of companies around the globe, including hundreds of the world’s largest, use Snowflake’s AI Data Cloud to share data, build applications, and power their business with AI. The era of enterprise AI is here. Learn more at snowflake.com (NYSE: SNOW).

Average Rating: 4.6/5.0

Total Reviews: 713

How Do G2 Users Rate Snowflake?

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

Who Is the Company Behind Snowflake?

  • Seller: Snowflake, Inc.
  • Company Website:
  • Year Founded: 2012
  • HQ Location: 135 Constitution Drive, Menlo Park CA
  • Twitter: @SnowflakeDB
    278 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    12,574 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Data Engineer, Data Analyst
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 45% Medium, 42% Large

What Do G2 Reviewers Say About Snowflake?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of Snowflake, which simplifies data sharing and enhances productivity across teams.
  • Users value the reliable features and user-friendly interface of Snowflake, enhancing data management and analytics efficiency.
  • Users appreciate the ease of use and efficient data integration in Snowflake for their warehousing projects.
  • Users value the seamless scalability of Snowflake, enabling efficient handling of large datasets and workload changes without performance loss.
  • Users value the fast and efficient data processing capabilities of Snowflake, enhancing their analysis experience significantly.
Cons
  • Users highlight the high costs of Snowflake, making it less accessible for smaller businesses with limited budgets.
  • Users find feature limitations in Snowflake, such as lack of code blocks and restricted permissions, frustrating.
  • Users find the learning curve steep, requiring training due to its complexity and overwhelming interface for beginners.
  • Users often struggle with high costs due to unoptimized queries and inadequate cost control measures in Snowflake.
  • Users find the cost structure challenging, requiring time to optimize for efficient use of Snowflake.

What Are Recent G2 Reviews of Snowflake?

What Are G2 Users Discussing About Snowflake?

Microsoft SQL Server

SQL Server 2017 brings the power of SQL Server to Windows, Linux and Docker containers for the first time ever, enabling developers to build intelligent applications using their preferred language and environment. Experience industry-leading performance, rest assured with innovative security features, transform your business with AI built-in, and deliver insights wherever your users are with mobile BI.

Average Rating: 4.4/5.0

Total Reviews: 2,132

How Do G2 Users Rate Microsoft SQL Server?

  • Has the product been a good partner in doing business?: 8.4/10 (Category avg: 8.8/10)
  • Real-Time Data Collection: 8.6/10 (Category avg: 8.8/10)
  • Machine Scaling: 8.2/10 (Category avg: 8.6/10)
  • Data Preparation: 8.5/10 (Category avg: 8.6/10)

Who Is the Company Behind Microsoft SQL Server?

  • Seller: Microsoft
  • Year Founded: 1975
  • HQ Location: Redmond, Washington
  • Twitter: @microsoft
    13,091,739 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    232,750 employees on LinkedIn®
  • Ownership: MSFT

Who Uses This Product?

  • Who Uses This: Software Engineer, Software Developer
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 45% Large, 37% Medium

What Do G2 Reviewers Say About Microsoft SQL Server?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of Microsoft SQL Server, highlighting its seamless integration and simplicity in management.
  • Users appreciate the robust database management of Microsoft SQL Server, enhancing performance and simplifying data handling.
  • Users appreciate the powerful performance of Microsoft SQL Server, recognizing it as an industry standard for databases.
  • Users appreciate the easy integrations of Microsoft SQL Server, enhancing their reporting and data management experience.
  • Users value the enterprise-grade security of Microsoft SQL Server, ensuring safety for sensitive data is prioritized.
Cons
  • Users find Microsoft SQL Server's high licensing costs a burden, especially for small businesses and budget constraints.
  • Users express concern about the high licensing costs of Microsoft SQL Server, making it tough for small businesses.
  • Users find the high licensing costs of Microsoft SQL Server a barrier for small businesses and budget constraints.
  • Users find the licensing costs steep, making Microsoft SQL Server less accessible for small businesses and limiting flexibility.
  • Users report slow performance of Microsoft SQL Server, especially when multitasking, affecting overall efficiency and usability.

What Are Recent G2 Reviews of Microsoft SQL Server?

What Are G2 Users Discussing About Microsoft SQL Server?

Cloudera

Cloudera is the only hybrid data and AI platform company that large organizations trust to bring AI to their data anywhere it lives. Unlike other providers, Cloudera delivers a consistent cloud experience that converges public clouds, on-prem data centers, and the edge, leveraging a proven open-source foundation. As the pioneer in big data, Cloudera empowers businesses to apply AI and assert control over 100% of their data, in all forms, improving security, governance, and real-time and predictive insights. The world’s largest brands across all industries rely on Cloudera to transform decision-making and ultimately boost bottom lines, safeguard against threats, and save lives. Cloudera Anywhere Cloud™: Build and scale applications across any environment. The modular hybrid data and AI platform engineered for the agentic era empowers teams to deploy production-grade data and AI workloads across multi-cloud, on-premises, and sovereign environments while maintaining digital sovereignty. The Cloudera data and AI platform includes: Cloudera AI: Deploy and scale any AI model, anywhere. Cloudera brings compute to governed data where it lives for Private AI anywhere by design. Complete control, security, and governance of mission-critical data, models, agents, and inference ensure faster sovereign AI deployments. Cloudera Data-in-Motion: Make fast decisions from real-time data anywhere. Move data with any structure from any source to any destination seamlessly across hybrid environments, enabling in-the-moment business-critical decisions by processing and analyzing real-time data anywhere, from the edge to AI, as business happens. Cloudera Open Data Lakehouse: Process any data, anywhere, for actionable insights. Make smart decisions with an open data lakehouse powered by Apache Iceberg that delivers trusted, reliable, and unified data to fuel agents, AI applications, and analytics, improving collaboration, breaking silos, and simplifying sharing. Cloudera Unified Data Fabric: Unify security and governance across the entire data estate. Move beyond fragmented data management: Break down silos and connect disparate data sources intelligently and securely to provide a unified view of all organizational data and centralized end-to-end control across complex hybrid data environments.

Average Rating: 4.2/5.0

Total Reviews: 190

How Do G2 Users Rate Cloudera?

  • Has the product been a good partner in doing business?: 8.5/10 (Category avg: 8.8/10)
  • Real-Time Data Collection: 8.1/10 (Category avg: 8.8/10)
  • Machine Scaling: 9.1/10 (Category avg: 8.6/10)
  • Data Preparation: 8.2/10 (Category avg: 8.6/10)

Who Is the Company Behind Cloudera?

  • Seller: Cloudera
  • Company Website:
  • Year Founded: 2008
  • HQ Location: Santa Clara, CA
  • Twitter: @cloudera
    106,442 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    3,505 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Data Engineer, Software Engineer
  • Top Industries: Information Technology and Services, Banking
  • Company Size: 39% Large, 35% Small

What Do G2 Reviewers Say About Cloudera?

AI-generated summary from verified user reviews

Pros
  • Users praise the user-friendly interface of Cloudera, highlighting its simplicity in managing big data efficiently.
  • Users value the easy scalability of Cloudera, enabling efficient management of large amounts of data effortlessly.
  • Users value the robust security features of Cloudera, ensuring safe and reliable data management across platforms.
  • Users value the comprehensive suite of tools in Cloudera for effective data management and analytics.
  • Users find Cloudera's scalability and centralized administration invaluable for efficient monitoring and management of data processes.
Cons
  • Users express concerns over the high costs of Cloudera, noting it's expensive for its complexity and maintenance.
  • Users find Cloudera's database to be complex, making it challenging for inexperienced professionals to utilize effectively.
  • Users find Cloudera's setup difficult to learn, particularly challenging for beginners without adequate tutorials or guidance.
  • Users find the poor documentation of Cloudera frustrating, complicating navigation and setup for complex data configurations.
  • Users often face access issues with Cloudera, particularly with unauthorized errors in Airflow tasks and limited documentation.

What Are Recent G2 Reviews of Cloudera?

What Are G2 Users Discussing About Cloudera?

Azure Data Lake Store

Azure Data Lake Storage is a cloud-based, enterprise-grade data lake solution designed to store and analyze massive amounts of data in its native format. It enables organizations to eliminate data silos by providing a single storage platform that supports structured, semi-structured, and unstructured data. This service is optimized for high-performance analytics workloads, allowing businesses to derive insights from their data efficiently. Key Features and Functionality: - Scalability: Offers virtually unlimited storage capacity, accommodating data of any size and type without the need for upfront capacity planning. - Security: Provides robust security mechanisms, including encryption at rest, advanced threat protection, and integration with Microsoft Entra ID (formerly Azure Active Directory) for role-based access control. - Integration: Seamlessly integrates with various Azure services such as Azure Databricks, Azure Synapse Analytics, and Azure HDInsight, facilitating comprehensive data processing and analytics. - Cost Optimization: Allows independent scaling of storage and compute resources, supports tiered storage options, and offers lifecycle management policies to optimize costs. - Performance: Supports high-throughput and low-latency data access, enabling efficient processing of large-scale analytics queries. Primary Value and Solutions Provided: Azure Data Lake Storage addresses the challenges of managing and analyzing vast amounts of diverse data by offering a scalable, secure, and cost-effective storage solution. It eliminates data silos, enabling organizations to store all their data in a single repository, regardless of format or size. This unified approach facilitates seamless data ingestion, processing, and visualization, empowering businesses to unlock valuable insights and drive informed decision-making. By integrating with popular analytics frameworks and Azure services, it streamlines the development of big data solutions, reducing time-to-insight and enhancing overall productivity.

Average Rating: 4.5/5.0

Total Reviews: 37

How Do G2 Users Rate Azure Data Lake Store?

  • Has the product been a good partner in doing business?: 8.7/10 (Category avg: 8.8/10)
  • Real-Time Data Collection: 9.1/10 (Category avg: 8.8/10)
  • Machine Scaling: 8.9/10 (Category avg: 8.6/10)
  • Data Preparation: 9.1/10 (Category avg: 8.6/10)

Who Is the Company Behind Azure Data Lake Store?

  • Seller: Microsoft
  • Year Founded: 1975
  • HQ Location: Redmond, Washington
  • Twitter: @microsoft
    13,091,739 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    232,750 employees on LinkedIn®
  • Ownership: MSFT

Who Uses This Product?

  • Who Uses This: Senior Data Engineer
  • Top Industries: Information Technology and Services
  • Company Size: 45% Large, 33% Medium

What Do G2 Reviewers Say About Azure Data Lake Store?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the easy integration with other Azure and non-Azure products, enhancing their data management experience.
  • Users value the fast processing capabilities of Azure Data Lake Store, enhancing data retrieval and integration seamlessly.
Cons
  • Users find it challenging due to the inability to see folder sizes and download entire folders, complicating their usage experience.

What Are Recent G2 Reviews of Azure Data Lake Store?

What Are G2 Users Discussing About Azure Data Lake Store?

Teradata Autonomous Knowledge Platform

Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI. Learn more at Teradata.com.

Average Rating: 4.3/5.0

Total Reviews: 354

How Do G2 Users Rate Teradata Autonomous Knowledge Platform?

  • Has the product been a good partner in doing business?: 8.2/10 (Category avg: 8.8/10)
  • Real-Time Data Collection: 7.9/10 (Category avg: 8.8/10)
  • Machine Scaling: 8.8/10 (Category avg: 8.6/10)
  • Data Preparation: 9.0/10 (Category avg: 8.6/10)

Who Is the Company Behind Teradata Autonomous Knowledge Platform?

Who Uses This Product?

  • Who Uses This: Data Engineer, Software Engineer
  • Top Industries: Information Technology and Services, Financial Services
  • Company Size: 69% Large, 22% Medium

What Do G2 Reviewers Say About Teradata Autonomous Knowledge Platform?

AI-generated summary from verified user reviews

Pros
  • Users highlight the extreme performance of Teradata Autonomous Knowledge Platform, emphasizing its speed in processing large data volumes.
  • Users value the high performance and scalability of Teradata for handling complex queries and data integration.
  • Users value the scalability of Teradata Autonomous Knowledge Platform, seamlessly integrating and managing vast data resources efficiently.
  • Users commend the extreme performance of Teradata, highlighting its speed in processing large datasets seamlessly.
  • Users value the fast processing of large datasets in Teradata, appreciating its stability and integration capabilities.
Cons
  • Users identify a steep learning curve for Teradata Autonomous Knowledge Platform, hindering new user adaptation and productivity.
  • Users find the steep learning curve of Teradata Autonomous Knowledge Platform challenging, especially for those less technically inclined.
  • Users find the complexity of the Teradata platform challenging, especially for non-technical users and new adopters.
  • Users struggle with the cost transparency of Teradata Autonomous Knowledge Platform, needing close management to avoid issues.
  • Users express concerns about the high cost of the Teradata Autonomous Knowledge Platform, highlighting affordability issues.

What Are Recent G2 Reviews of Teradata Autonomous Knowledge Platform?

What Are G2 Users Discussing About Teradata Autonomous Knowledge Platform?

Kyvos Semantic Layer

Kyvos is a semantic layer for AI and BI. It gives organizations a single, consistent, business-friendly view of their entire data estate. By standardizing how data is defined and understood, Kyvos eliminates metric drift across BI tools and ensures that LLMs and AI agents work with governed business semantics rather than raw tables. Kyvos also delivers lightning-fast analytics at massive scale and high concurrency — including granular multidimensional analysis on the cloud — without the sluggish query times and escalating cloud costs that typically come with it. Why Organizations Use Kyvos Unified Semantic Foundation for AI and BI Kyvos semantic layer standardizes how metrics, KPIs, dimensions, hierarchies, relationships, calculations, and business rules are modelled across the enterprise — so that dashboards, analytics tools, notebooks, and AI systems all operate on the same understanding of the business. Kyvos enables: - Shared semantics — one common data language across every tool, team, and system - Governed access — data exploration within defined security, role, and permission boundaries - Platform interoperability — consistent semantic context across diverse platforms and environments - AI readiness — LLMs and agents work with governed business semantics rather than raw tables or ambiguous schema AI Grounded in Business Context Kyvos grounds AI systems in the governed semantic model, ensuring they operate on established business context rather than raw schemas — improving the accuracy, traceability, and reliability of AI-generated insights. Consistent Metrics Across BI Tools Kyvos centralizes metric and KPI definitions in the semantic layer and applies them consistently across every analytics interface — eliminating metric drift and improving trust in analytics. High-Performance Analytics at Scale Kyvos delivers high-performance analytics that scale with demand, enabling: - Sub-second query performance across massive datasets - High concurrency across thousands of users and workloads - Consistent response times regardless of data volume or concurrency - No performance degradation as adoption grows - Multidimensional Analytics on the Cloud Kyvos enables deep multidimensional analytics, supporting: - Granular analysis across billions of rows - Thousands of measures and dimensions in a single model - Fast drill-down across complex hierarchies - Full analytical depth without sacrificing query speed Cloud Cost Efficiency Kyvos serves analytics through its semantic layer rather than routing every query to the warehouse — reducing compute consumption across analytics and AI workloads. As adoption grows, organizations can scale users, workloads, and analytical complexity without a corresponding rise in warehouse compute costs.

Average Rating: 4.8/5.0

Total Reviews: 267

How Do G2 Users Rate Kyvos Semantic Layer?

  • Has the product been a good partner in doing business?: 9.6/10 (Category avg: 8.8/10)

Who Is the Company Behind Kyvos Semantic Layer?

  • Seller: Kyvos Insights
  • Year Founded: 2014
  • HQ Location: Los Gatos, CA
  • Twitter: @KyvosInsights
    689 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    145 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Senior Software Engineer, Software Engineer
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 57% Medium, 38% Large

What Do G2 Reviewers Say About Kyvos Semantic Layer?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of Kyvos, allowing quick access to insights and simplifying complex data management.
  • Users appreciate the fast data processing of Kyvos, enabling instant analysis and visualization of large datasets.
  • Users value the remarkable speed and performance of Kyvos, enabling swift data analytics for large datasets.
  • Users appreciate the lightning-fast analytics of Kyvos Semantic Layer, making data processing and visualization seamless and efficient.
  • Users value the fast querying capabilities of Kyvos Semantic Layer, enabling quick analysis of large data volumes.
Cons
  • Users find the learning curve steep for Kyvos, especially with advanced features and MDX queries requiring specialized knowledge.
  • Users find the difficult setup of Kyvos Semantic Layer challenging, despite effective support easing the process.
  • Users find the initial setup and MDX complexity challenging, though support significantly eases the deployment process.
  • Users find feature limitations in Kyvos, particularly lacking advanced analytics and graphical options for data visualization.
  • Users find that learning difficulty can hinder new users' experience, despite abundant training resources available.

What Are Recent G2 Reviews of Kyvos Semantic Layer?

Starburst

Starburst is the data platform for analytics, applications, and AI, unifying data across clouds and on-premises to accelerate AI innovation. Organizations—from startups to Fortune 500 enterprises in 60+ countries—rely on Starburst for fast data access, seamless collaboration, and enterprise-grade governance on an open hybrid data lakehouse. Wherever data lives, Starburst unlocks its full potential, powering data and AI from development to deployment. By future-proofing data architecture, Starburst helps businesses fuel innovation with AI. Learn more at starburst.ai

Average Rating: 4.3/5.0

Total Reviews: 114

How Do G2 Users Rate Starburst?

  • Has the product been a good partner in doing business?: 9.0/10 (Category avg: 8.8/10)
  • Real-Time Data Collection: 8.2/10 (Category avg: 8.8/10)
  • Machine Scaling: 8.3/10 (Category avg: 8.6/10)
  • Data Preparation: 8.3/10 (Category avg: 8.6/10)

Who Is the Company Behind Starburst?

  • Seller: Starburst
  • Company Website:
  • Year Founded: 2017
  • HQ Location: Boston, MA
  • Twitter: @starburstdata
    3,454 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    543 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Senior Data Engineer
  • Top Industries: Information Technology and Services, Financial Services
  • Company Size: 40% Large, 39% Small

What Do G2 Reviewers Say About Starburst?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the fast querying capabilities of Starburst, significantly enhancing their data analysis efficiency across diverse sources.
  • Users commend Starburst for its exceptional query efficiency, enabling seamless execution of complex queries across multiple data sources.
  • Users value the seamless integration with various data sources, significantly enhancing data access and analysis efficiency.
  • Users find Starburst's ease of use impressive, highlighting quick setup and maintenance-free administration.
  • Users value the quick querying of large datasets with Starburst, significantly boosting productivity and efficiency.
Cons
  • Users face query issues with performance delays, particularly with complex or large datasets impacting efficiency and insights.
  • Users experience slow performance on Starburst, especially with complex queries and multiple simultaneous users affecting efficiency.
  • Users find the setup and complexity of Starburst challenging, especially for beginners navigating its advanced features.
  • Users note a steep learning curve when setting up Starburst, making onboarding and optimization challenging for new users.
  • Users often experience performance issues with Starburst, especially when handling large data sets and complex queries.

What Are Recent G2 Reviews of Starburst?

What Are G2 Users Discussing About Starburst?

ILUM

Ilum: A Data Platform Built by Data Engineers, for Data Engineers Ilum is a Data Lakehouse platform that unifies data management, distributed processing, analytics, and AI workflows for AI engineers, data engineers, data scientists, and analysts. It belongs to the Data Platform, Data Lakehouse, and Data Engineering software categories and supports flexible deployment across cloud, on-premise, and hybrid environments. Ilum enables technical teams to build, operate, and scale modern data infrastructure using open standards. It integrates tools for batch processing, stream processing, notebook-based exploration, workflow orchestration, and business intelligence, All In a Single Platform. Ilum supports modern open table formats like Delta Lake, Apache Iceberg, Apache Hudi, and Apache Paimon. It also offers native integration with Apache Spark and Trino for compute, with Apache Flink support currently in development. Key features include: - SQL Editor: Query Delta, Iceberg, Hudi, or Spark SQL with autocomplete, result previews, and metadata inspection. - Data Lineage & Catalog: Visualize data flow using OpenLineage and explore datasets through a searchable Data Catalog. - Notebook Integration: Use built-in Jupyter notebooks pre-wired to Spark, metadata, and your data environment for exploration or modeling. - Spark Job Management: Submit, monitor, and debug Spark jobs with integrated logs, metrics, scheduling, and a built-in Spark History Server. - Trino Support: Run federated queries across multiple data sources using Trino directly from within Ilum. - Declarative Pipelines: Define repeatable ETL and analytics pipelines, with dependency tracking and recovery logic. - Automatic ERD Diagrams: Instantly generate ER diagrams from schemas to aid in data understanding and onboarding. - ML Experimentation & Tracking: Includes MLflow for managing experiments, tracking parameters, metrics, and artifacts, fully integrated with notebooks and data pipelines to streamline model development workflows. - AI Integration & Deployment: Supports both classical ML and modern AI use cases, including GenAI workflows, vector search, and embedding-based applications. Models can be registered, versioned, and deployed for inference within declarative pipelines. - Built-in AI Agent Interface: Ilum integrates, providing a GPT-style interface to interact with your data, trigger pipelines, generate SQL, or explore metadata using natural language, bringing GenAI capabilities directly into your data platform. - BI Dashboards: Native support for Apache Superset, with JDBC integration for Tableau, Power BI, and other BI tools. Additional highlights: - Multi-Cluster Management: Connect multiple Spark or Kubernetes clusters to scale and isolate workloads. - Fine-Grained Access Control: LDAP, OAuth2, and Hydra integration for secure, role-based access. - Hybrid Ready: Designed to replace Databricks or Cloudera in environments where cloud adoption is partial, regulated, or not possible.

Average Rating: 4.9/5.0

Total Reviews: 23

How Do G2 Users Rate ILUM?

  • Has the product been a good partner in doing business?: 9.7/10 (Category avg: 8.8/10)
  • Real-Time Data Collection: 10.0/10 (Category avg: 8.8/10)
  • Machine Scaling: 10.0/10 (Category avg: 8.6/10)
  • Data Preparation: 9.8/10 (Category avg: 8.6/10)

Who Is the Company Behind ILUM?

  • Seller: Ilum
  • Company Website:
  • Year Founded: 2019
  • HQ Location: Santa Fe, US
  • Twitter: @IlumCloud
    19 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    5 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Telecommunications
  • Company Size: 52% Large, 35% Medium

What Do G2 Reviewers Say About ILUM?

AI-generated summary from verified user reviews

Pros
  • Users praise ILUM for its ease of use, with a clean UI and quick deployment enhancing productivity and workflow.
  • Users praise ILUM for its seamless integration, user-friendly interface, and excellent customer support, streamlining data management effectively.
  • Users value the seamless integrations of ILUM, enhancing productivity by connecting various systems and streamlining workflows.
  • Users love the ease of setup with ILUM, noting quick deployments and user-friendly interfaces that enhance productivity.
  • Users value the easy integrations of ILUM, enhancing their data workflows and simplifying complex processes effortlessly.
Cons
  • Users note that the complex setup of ILUM can be challenging, requiring time and effort to configure properly.
  • Users note the difficult setup of ILUM, requiring experimentation and digging for advanced configurations and integrations.
  • Users note the steep learning curve for new users, though intuitive daily use improves after initial setup.
  • Users note that the UX could be improved with more intuitive navigation and clearer configuration options.
  • Users find ILUM's complexity in advanced configurations may require time and effort to fully navigate and optimize.

What Are Recent G2 Reviews of ILUM?

Dremio

Dremio is the pioneer of The Agentic Lakehouse—the only data platform built for agents, managed by agents. Organizations need to transform ideas into actions at unprecedented speed—Dremio delivers this agility by equipping AI agents with federated data access, unstructured data processing, and rich business context through its AI Semantic Layer. In the agentic-era, data engineering teams can’t manually tune performance for thousands of users and agents asking unpredictable questions every second. Dremio’s Agentic Lakehouse autonomously manages itself, removing undifferentiated management tasks, allowing engineers to focus on initiatives that drive business results. Dremio’s agentic lakehouse automatically optimizes queries, reorganizes data, and maintains performance at any scale. Dremio is trusted by thousands of global enterprises including Shell, TD Bank, and Michelin, and built on open standards. Dremio co-created Apache Polaris and Apache Arrow, and it's the only lakehouse built natively on Apache Iceberg, Polaris, and Arrow.

Average Rating: 4.6/5.0

Total Reviews: 65

How Do G2 Users Rate Dremio?

  • Has the product been a good partner in doing business?: 9.1/10 (Category avg: 8.8/10)
  • Machine Scaling: 9.1/10 (Category avg: 8.6/10)
  • Data Preparation: 8.7/10 (Category avg: 8.6/10)

Who Is the Company Behind Dremio?

  • Seller: Dremio
  • Year Founded: 2015
  • HQ Location: Santa Clara, California
  • Twitter: @dremio
    5,112 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    357 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Financial Services, Information Technology and Services
  • Company Size: 49% Large, 39% Medium

What Do G2 Reviewers Say About Dremio?

AI-generated summary from verified user reviews

Pros
  • Users find Dremio to be stupidly easy to use, enhancing efficiency in data sharing and visualization.
  • Users value Dremio's seamless integrations with tools like Power BI and Tableau for efficient data management.
  • Users commend Dremio for its impressive performance, accelerating queries and simplifying data collection across multiple sources.
  • Users value the SQL support in Dremio, facilitating seamless data integration and efficient analysis across platforms.
  • Users highlight Dremio's exceptional data management capabilities, simplifying data manipulation and enhancing analytics for informed decisions.
Cons
  • Users find the initial setup complicated and note a steep learning curve for effective implementation of Dremio.
  • Users note that customer support can be slow, occasionally leading to delays in resolving issues and assisting users.
  • Users find the learning curve steep, making it challenging to fully adopt and utilize Dremio effectively.
  • Users find the difficult setup of Dremio to be a time-consuming challenge, hindering their overall experience.
  • Users often find poor documentation frustrating, relying on forums instead of clear resources for configuration details.

What Are Recent G2 Reviews of Dremio?

What Are G2 Users Discussing About Dremio?

Google Cloud Dataflow

Cloud Dataflow is a fully-managed service for transforming and enriching data in stream (real time) and batch (historical) modes with equal reliability and expressiveness -- no more complex workarounds or compromises needed. And with its serverless approach to resource provisioning and management, you have access to virtually limitless capacity to solve your biggest data processing challenges, while paying only for what you use.

Average Rating: 4.2/5.0

Total Reviews: 42

How Do G2 Users Rate Google Cloud Dataflow?

  • Has the product been a good partner in doing business?: 9.0/10 (Category avg: 8.8/10)
  • Real-Time Data Collection: 8.3/10 (Category avg: 8.8/10)
  • Machine Scaling: 8.8/10 (Category avg: 8.6/10)
  • Data Preparation: 8.6/10 (Category avg: 8.6/10)

Who Is the Company Behind Google Cloud Dataflow?

  • Seller: Google
  • Year Founded: 1998
  • HQ Location: Mountain View, CA
  • Twitter: @google
    31,899,995 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    301,144 employees on LinkedIn®
  • Ownership: NASDAQ:GOOG

Who Uses This Product?

  • Top Industries: Computer Software
  • Company Size: 39% Small, 34% Medium

What Do G2 Reviewers Say About Google Cloud Dataflow?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use and efficiency in processing streaming events with Google Cloud Dataflow.
  • Users find Google Cloud Dataflow's ease of use invaluable for efficiently building and monitoring streaming pipelines.
  • Users appreciate the easy management of Google Cloud Dataflow, facilitating seamless integration and efficient streaming pipeline processing.
  • Users appreciate the ease of use and integration of Google Cloud Dataflow for processing streaming events efficiently.
  • Users value the ease of use for processing streaming events with Google Cloud Dataflow, facilitating efficient pipeline building.
Cons
  • Users find Google Cloud Dataflow to be costly compared to alternatives, impacting their decision-making process.
  • Users find Google Cloud Dataflow to be expensive compared to alternatives like Apache Flink, impacting their decision.
  • Users find the installation difficult with challenges like implementing watermarks compared to other solutions.
  • Users find learning difficulty in implementing Google Cloud Dataflow, especially with complex features like watermarks.

What Are Recent G2 Reviews of Google Cloud Dataflow?

What Are G2 Users Discussing About Google Cloud Dataflow?

Confluent

Today’s customers expect every digital experience to be immediate, connected, and personalized. That takes more than data at rest - it takes trusted data in motion. Confluent is the complete Data Streaming Platform for keeping data in motion from the moment business change occurs through processing, governance, and serving. Built by the original creators of Apache Kafka®, Confluent connects applications, services, and systems; processes streams in real time with Apache Flink®; and serves trusted, always-current data wherever it is needed across cloud, hybrid, and self-managed environments. With deployment options including fully managed Confluent Cloud, self-managed Confluent Platform and Brint-your-own-cloud Confluent WarpStream, teams can power event-driven applications, real-time analytics, AI, and operational workflows while choosing the operating model that fits their environment and turning live data into better customer experiences and business outcomes.

Average Rating: 4.4/5.0

Total Reviews: 111

How Do G2 Users Rate Confluent?

  • Has the product been a good partner in doing business?: 8.5/10 (Category avg: 8.8/10)
  • Real-Time Data Collection: 9.0/10 (Category avg: 8.8/10)
  • Machine Scaling: 8.2/10 (Category avg: 8.6/10)
  • Data Preparation: 7.8/10 (Category avg: 8.6/10)

Who Is the Company Behind Confluent?

  • Seller: IBM
  • Company Website:
  • Year Founded: 1911
  • HQ Location: Armonk, New York, United States
  • Twitter: @IBMSecurity
    74,660 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    344,328 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Software Engineer, Senior Software Engineer
  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 36% Large, 33% Small

What Do G2 Reviewers Say About Confluent?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the simplicity and scalability of Confluent's managed cloud services, enhancing real-time data integration.
  • Users appreciate the effortless integration of Confluent's cloud services, enhancing their experience with Kafka and Flink.
  • Users appreciate the wide range of connectors in Confluent, enhancing real-time data integration effortlessly.
  • Users appreciate the effortless data integration offered by Confluent, enhancing real-time processing with robust tools and scalability.
  • Users appreciate the ease of use of Confluent, enjoying simplified data integration and a user-friendly interface.
Cons
  • Users note the high cost estimation with data growth and a steep learning curve for effective use.
  • Users find Confluent expensive as costs rise with data volume, and learning the system can be time-consuming.
  • Users face initial difficulties with a steep learning curve and costly pricing as data volumes increase.
  • Users find a lack of features in Confluent, especially in lower tiers, leading to increased costs and complexity.
  • Users face a steep learning curve with Confluent, requiring significant time to master its workflow and features.

What Are Recent G2 Reviews of Confluent?

What Are G2 Users Discussing About Confluent?

Bijou Barry
BB
Researched and written by Bijou Barry
Updated