Best Enterprise Data Warehouse Solutions

How Many Data Warehouse Solutions Products Does G2 Track?

Total Products under this Category: 131

Category Stats (Oct 2026)

  • Average Rating: 4.38/5 (↑0.01 vs Sep 2026) The average rating of products in this category, based on all submitted ratings
  • Top Trending Product: Sesame Software (+0.35%) - Among all products in this category, Sesame Software recorded the largest rating increase compared to last month

Last updated: October 15, 2026

How Does G2 Rank Data Warehouse Solutions Products?

Why You Can Trust G2's Software Rankings:

  • 30 Analysts and Data Experts
  • 7,500+ Authentic Reviews
  • 131+ 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 Data Warehouse Solutions

G2 Grid® for Data Warehouse Solutions plotting products by satisfaction and market presence

Highlighted products: Databricks, Google Cloud BigQuery, Snowflake, SAP Datasphere, Teradata Autonomous Knowledge Platform, Amazon Redshift, VMware Greenplum, and IBM watsonx.data.

Underlying data: [Grid® JSON](https://www.g2.com/categories/data-warehouse/grids.json?focus%5B%5D=databricks&focus%5B%5D=google-cloud-bigquery&focus%5B%5D=snowflake&focus%5B%5D=sap-datasphere&focus%5B%5D=teradata-autonomous-knowledge-platform&focus%5B%5D=amazon-redshift&focus%5B%5D=vmware-greenplum&focus%5B%5D=ibm-watsonx-data&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: 1,354

How Do G2 Users Rate Databricks?

  • Ease of Use: 8.8/10 (Category avg: 8.8/10)
  • Data Governance: 8.9/10 (Category avg: 8.4/10)
  • Data Security: 8.9/10 (Category avg: 8.8/10)
  • Scalability: 9.2/10 (Category avg: 8.5/10)

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, Senior Data Engineer
  • 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?

  • Ease of Use: 8.7/10 (Category avg: 8.8/10)
  • Data Governance: 8.7/10 (Category avg: 8.4/10)
  • Data Security: 9.1/10 (Category avg: 8.8/10)
  • Scalability: 9.1/10 (Category avg: 8.5/10)

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?

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?

  • Ease of Use: 9.0/10 (Category avg: 8.8/10)
  • Data Governance: 8.9/10 (Category avg: 8.4/10)
  • Data Security: 9.1/10 (Category avg: 8.8/10)
  • Scalability: 9.3/10 (Category avg: 8.5/10)

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, 43% 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?

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SAP Datasphere

SAP Datasphere is a unified service for data integration, cataloging, semantic modeling, data warehousing, and virtualizing workloads across all your data. It enables every data professional to deliver seamless and scalable access to mission-critical business data. SAP Datasphere, and its open data ecosystem, is the foundation for a business data fabric.

Average Rating: 4.2/5.0

Total Reviews: 162

How Do G2 Users Rate SAP Datasphere?

  • Ease of Use: 8.1/10 (Category avg: 8.8/10)
  • Data Governance: 8.6/10 (Category avg: 8.4/10)
  • Data Security: 8.7/10 (Category avg: 8.8/10)
  • Scalability: 8.1/10 (Category avg: 8.5/10)

Who Is the Company Behind SAP Datasphere?

  • Seller: SAP
  • Year Founded: 1972
  • HQ Location: Walldorf
  • Twitter: @SAP
    297,052 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    149,349 employees on LinkedIn®
  • Ownership: NYSE:SAP

Who Uses This Product?

  • Who Uses This: Business Analyst
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 41% Large, 36% Medium

What Do G2 Reviewers Say About SAP Datasphere?

AI-generated summary from verified user reviews

Pros
  • Users find SAP Datasphere's ease of use enhances their workflow significantly compared to similar services.
  • Users value the easy integrations of SAP Datasphere, allowing seamless connectivity with ERP systems for enhanced data management.
  • Users value the seamless integration of diverse data sources in SAP Datasphere, enhancing data management and accessibility for analytics.
  • Users appreciate the business-contextualized data integration of SAP Datasphere, which simplifies analytics and promotes collaboration.
  • Users value the seamless collaboration of SAP Datasphere, making data access and integration efficient within the SAP ecosystem.
Cons
  • Users are frustrated by the slow performance of SAP Datasphere, especially when handling large datasets and complex tasks.
  • Users find the pricing expensive, which can make it challenging for new users to adopt SAP Datasphere effectively.
  • Users experience performance issues with SAP Datasphere, noting slow speeds and latency, especially with large datasets.
  • Users face integration issues with SAP Datasphere, finding it challenging to set up and connect various data sources.
  • Users find the complex setup of SAP Datasphere challenging, often leading to frustration during the initial configuration.

What Are Recent G2 Reviews of SAP Datasphere?

What Are G2 Users Discussing About SAP Datasphere?

VMware Greenplum

Advanced analytics meets traditional business intelligence with VMware Greenplum, the world’s first fully-featured, multi-cloud, massively parallel processing (MPP) data platform based on the open source Greenplum Database. Greenplum provides comprehensive and integrated analytics on multi-structured data. Powered by one of the world’s most advanced cost-based query optimizers, VMware Greenplum delivers unmatched analytical query performance on massive volumes of data.

Average Rating: 4.3/5.0

Total Reviews: 55

How Do G2 Users Rate VMware Greenplum?

  • Ease of Use: 8.4/10 (Category avg: 8.8/10)
  • Data Governance: 9.3/10 (Category avg: 8.4/10)
  • Data Security: 9.3/10 (Category avg: 8.8/10)
  • Scalability: 8.7/10 (Category avg: 8.5/10)

Who Is the Company Behind VMware Greenplum?

  • Seller: Broadcom
  • Year Founded: 1991
  • HQ Location: San Jose, CA
  • Twitter: @broadcom
    63,909 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    44,602 employees on LinkedIn®
  • Ownership: NASDAQ: CA

Who Uses This Product?

  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 49% Large, 31% Medium

What Are Recent G2 Reviews of VMware Greenplum?

What Are G2 Users Discussing About VMware Greenplum?

Amazon Redshift

Tens of thousands of customers use Amazon Redshift, a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. It is optimized for datasets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Average Rating: 4.3/5.0

Total Reviews: 371

How Do G2 Users Rate Amazon Redshift?

  • Ease of Use: 8.7/10 (Category avg: 8.8/10)
  • Data Governance: 8.7/10 (Category avg: 8.4/10)
  • Data Security: 8.8/10 (Category avg: 8.8/10)
  • Scalability: 8.9/10 (Category avg: 8.5/10)

Who Is the Company Behind Amazon Redshift?

  • Seller: Amazon Web Services (AWS)
  • Company Website:
  • Year Founded: 2006
  • HQ Location: Seattle, WA
  • Twitter: @awscloud
    2,232,483 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    148,266 employees on LinkedIn®

Who Uses This Product?

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

What Do G2 Reviewers Say About Amazon Redshift?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the fast querying capabilities of Amazon Redshift, enjoying efficient and smooth data access for large datasets.
  • Users appreciate the seamless integrations of Amazon Redshift, enhancing functionality and efficiency across their data solutions.
  • Users appreciate the ease of use of Amazon Redshift, finding it simple to connect and manage data effectively.
  • Users appreciate the easy integrations with other software and AWS services, enhancing their data management experience.
  • Users appreciate the impressive speed and scalability of Amazon Redshift, enhancing their data warehousing experience significantly.
Cons
  • Users note notable feature limitations in Amazon Redshift, particularly in advanced analytics and cross-language coding.
  • Users find software limitations in Redshift, experiencing issues with performance, concurrency, and data type support.
  • Users face significant complexity in optimizations with Redshift, requiring extensive management and specialized knowledge for effective use.
  • Users face query issues, requiring significant time for optimization, tuning, and managing complexity and concurrency challenges.
  • Users face a significant query optimization challenge with Redshift, requiring considerable effort and specialized knowledge.

What Are Recent G2 Reviews of Amazon Redshift?

What Are G2 Users Discussing About Amazon Redshift?

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?

  • Ease of Use: 8.3/10 (Category avg: 8.8/10)
  • Data Governance: 7.9/10 (Category avg: 8.4/10)
  • Data Security: 8.2/10 (Category avg: 8.8/10)
  • Scalability: 8.5/10 (Category avg: 8.5/10)

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?

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: 168

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How Do G2 Users Rate IBM watsonx.data?

  • Ease of Use: 8.2/10 (Category avg: 8.8/10)
  • Data Governance: 9.5/10 (Category avg: 8.4/10)
  • Data Security: 9.4/10 (Category avg: 8.8/10)
  • Scalability: 9.1/10 (Category avg: 8.5/10)

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?

IBM Db2

Built to run the world’s mission-critical workloads. Designed by the world’s leading database experts, IBM Db2 empowers developers, enterprise architects, and data engineers to run low-latency transactions and real-time analytics equipped for the most demanding workloads. From microservices to AI workloads, Db2 is the tested, resilient, and hybrid database providing the extreme availability, built-in refined security, effortless scalability, and intelligent automation for systems that run the world.

Average Rating: 4.1/5.0

Total Reviews: 600

How Do G2 Users Rate IBM Db2?

  • Ease of Use: 8.0/10 (Category avg: 8.8/10)
  • Data Governance: 8.7/10 (Category avg: 8.4/10)
  • Data Security: 9.0/10 (Category avg: 8.8/10)
  • Scalability: 8.6/10 (Category avg: 8.5/10)

Who Is the Company Behind IBM Db2?

  • Seller: IBM
  • 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®
  • Ownership: SWX:IBM

Who Uses This Product?

  • Who Uses This: Senior Software Engineer, Software Engineer
  • Top Industries: Information Technology and Services, Banking
  • Company Size: 66% Large, 21% Medium

What Do G2 Reviewers Say About IBM Db2?

AI-generated summary from verified user reviews

Pros
  • Users highlight the high performance of IBM Db2, appreciating its reliability and efficiency in managing large datasets.
  • Users praise IBM Db2 for its exceptional reliability, consistently delivering strong performance even under heavy workloads.
  • Users commend the scalability of IBM Db2, ensuring excellent performance even with large datasets and enterprise workloads.
  • Users highlight the strong security of IBM Db2, making it a reliable choice for critical workloads across industries.
  • Users find IBM Db2 to be easy to use and integrate, enhancing daily tasks and overall efficiency.
Cons
  • Users often find the complex setup of IBM Db2 challenging, requiring significant time and effort to manage effectively.
  • Users find IBM Db2 to be expensive, with high licensing costs and significant resource demands impacting their budget.
  • Users face a steep learning curve with IBM Db2, making setup and administration challenging for newcomers.
  • Users find IBM Db2's setup complex, facing high costs and limited documentation that complicate the user experience.
  • Users find the difficult setup of IBM Db2 challenging, often leading to frustration during initial configuration and administration.

What Are Recent G2 Reviews of IBM Db2?

What Are G2 Users Discussing About IBM Db2?

IBM Netezza Performance Server

Integrates database, server, storage and analytics into a single system with petabyte scalability. Fast analytics Provides a high-performance, massively parallel system that enables you to gain insight from your data and perform analytics on very large data volumes. Smart, efficient queries Simplifies analytics by consolidating all activity in one place, where the data resides. Simplified infrastructure Easy to deploy and manage; simplifies your data warehouse and analytic infrastructure. Does not require tuning, indexing or aggregated tables and needs minimal administration. Advanced security Enhanced data security is provided through self-encrypting drives as well as support for the Kerberos authentication protocol. Integrated platform Supports thousands of users, unifying data warehouse, Hadoop and business intelligence with advanced analytics.

Average Rating: 4.1/5.0

Total Reviews: 68

How Do G2 Users Rate IBM Netezza Performance Server?

  • Ease of Use: 8.8/10 (Category avg: 8.8/10)
  • Data Governance: 8.9/10 (Category avg: 8.4/10)
  • Data Security: 9.0/10 (Category avg: 8.8/10)
  • Scalability: 8.5/10 (Category avg: 8.5/10)

Who Is the Company Behind IBM Netezza Performance Server?

  • Seller: IBM
  • 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®
  • Ownership: SWX:IBM

Who Uses This Product?

  • Top Industries: Information Technology and Services, Banking
  • Company Size: 62% Large, 27% Medium

What Do G2 Reviewers Say About IBM Netezza Performance Server?

AI-generated summary from verified user reviews

Pros
  • Users value the exceptional speed of IBM Netezza Performance Server, enhancing data analysis and boosting performance effectively.
  • Users commend IBM Netezza Performance Server for its exceptional speed and efficiency in processing large data volumes effectively.
  • Users appreciate the user-friendly interface of IBM Netezza Performance Server, enhancing ease of use for data analysis tasks.
  • Users value the exceptional processing speed of IBM Netezza Performance Server, enhancing data handling and analytics efficiency.
  • Users highlight the high-speed data processing efficiency of IBM Netezza Performance Server, enhancing quick analysis and data handling.
Cons
  • Users note the high costs of IBM Netezza Performance Server, which can challenge budget-conscious organizations in justifying its value.
  • Users find the high maintenance costs of IBM Netezza Performance Server a challenge, especially for smaller enterprises.
  • Users find integration issues with other software challenging, impacting overall usability of IBM Netezza Performance Server.
  • Users find the limited customization options restrict user preferences, leading to integration challenges with other software.
  • Users experience slow performance with query times not improving even with millions of records processed.

What Are Recent G2 Reviews of IBM Netezza Performance Server?

What Are G2 Users Discussing About IBM Netezza Performance Server?

SQL Server 2019

Parallel Data Warehouse offers scalability to hundreds of terabytes and high performance through a massively parallel processing architecture.

Average Rating: 4.5/5.0

Total Reviews: 78

How Do G2 Users Rate SQL Server 2019?

  • Ease of Use: 9.0/10 (Category avg: 8.8/10)
  • Data Governance: 8.5/10 (Category avg: 8.4/10)
  • Data Security: 9.0/10 (Category avg: 8.8/10)
  • Scalability: 8.8/10 (Category avg: 8.5/10)

Who Is the Company Behind SQL Server 2019?

  • 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?

  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 37% Medium, 35% Large

What Do G2 Reviewers Say About SQL Server 2019?

AI-generated summary from verified user reviews

Pros
  • Users value the seamless data integration in SQL Server 2019, enhancing performance for complex queries and applications.
  • Users appreciate the advanced indexing and optimized data retrieval in SQL Server 2019, improving complex query handling.
Cons
  • Users note that SQL Server 2019 has high licensing costs that may be unaffordable for smaller projects.

What Are Recent G2 Reviews of SQL Server 2019?

What Are G2 Users Discussing About SQL Server 2019?

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?

  • Ease of Use: 9.3/10 (Category avg: 8.8/10)
  • Data Governance: 9.3/10 (Category avg: 8.4/10)
  • Data Security: 9.2/10 (Category avg: 8.8/10)
  • Scalability: 9.5/10 (Category avg: 8.5/10)

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?

  • Ease of Use: 9.2/10 (Category avg: 8.8/10)
  • Data Governance: 8.2/10 (Category avg: 8.4/10)
  • Scalability: 8.3/10 (Category avg: 8.5/10)

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?

Rubrik

Rubrik is the leading cyber resilience and data protection company with a mission to secure the world’s data. Rubrik pioneered Zero Trust Data SecurityTM to help organizations achieve business resilience against cyberattacks, malicious insiders, and operational disruptions. Rubrik Security Cloud, built with a Zero Trust design and powered by machine learning, delivers complete cyber resilience in a single platform across enterprise, cloud, and SaaS. Rubrik’s platform automates data policy management and enforcement, safeguards sensitive data, delivers data threat analytics and response, and orchestrates rapid cyber and operational recovery.

Average Rating: 4.6/5.0

Total Reviews: 296

How Do G2 Users Rate Rubrik?

  • Ease of Use: 9.1/10 (Category avg: 8.8/10)
  • Data Governance: 9.0/10 (Category avg: 8.4/10)
  • Data Security: 9.2/10 (Category avg: 8.8/10)
  • Scalability: 9.3/10 (Category avg: 8.5/10)

Who Is the Company Behind Rubrik?

  • Seller: Rubrik
  • Company Website:
  • Year Founded: 2014
  • HQ Location: Palo Alto, California
  • Twitter: @rubrikInc
    44,021 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    5,167 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: System Administrator, Network Engineer
  • Top Industries: Information Technology and Services, Hospital & Health Care
  • Company Size: 47% Large, 35% Medium

What Do G2 Reviewers Say About Rubrik?

AI-generated summary from verified user reviews

Pros
  • Users commend Rubrik for its ease of use, enabling staff to manage backups without specialized training.
  • Users appreciate Rubrik for its comprehensive backup solutions, offering a unified and efficient management experience for data recovery.
  • Users commend Rubrik's reliable backup performance, ensuring data security with ease of use and efficiency.
  • Users value the backup efficiency of Rubrik, enjoying intuitive management and rapid recovery in a secure environment.
  • Users admire the user-friendly interface of Rubrik, enabling simpler backup management for staff without advanced certification.
Cons
  • Users find Rubrik's pricing to be expensive, particularly for smaller environments compared to alternative solutions.
  • Users feel that Rubrik has limited features and could greatly benefit from expanded capabilities and better reporting.
  • Users find Rubrik's system to have a steep learning curve, requiring training and causing initial setup challenges.
  • Users express concern about the cost management during yearly renewals and support timelines for APAC customers.
  • Users feel that Rubrik needs to improve its offering with more features and better analytics for enhanced performance.

What Are Recent G2 Reviews of Rubrik?

What Are G2 Users Discussing About Rubrik?

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: 125

How Do G2 Users Rate Starburst?

  • Ease of Use: 8.8/10 (Category avg: 8.8/10)
  • Data Governance: 7.8/10 (Category avg: 8.4/10)
  • Data Security: 8.4/10 (Category avg: 8.8/10)
  • Scalability: 8.8/10 (Category avg: 8.5/10)

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% Small, 38% Large

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?

Shalaka Joshi
SJ
Researched and written by Shalaka Joshi
Updated
Reviews & FAQs

Reviews Summary: The Best Data Warehouse Software for Enterprise Businesses

Thanks to 7124 Data Warehouse software reviews and associated data from users working for large-scale companies, at G2 we have some of the most definitive data on the best Data Warehouse software for the large space. We have identified 24 individual Data Warehouse products offering the features, pricing and support that Enterprise companies look for when comparing Data Warehouse software and solutions. These reviews are generated using the qualitative and quantitative feedback from G2 members working in large-scale companies and who have used these Data Warehouse software products.

When G2 reviewers submit their Data Warehouse software reviews, they often provide information on the type of company they work for and what market segment they belong to. We break these different market segments into three distinct groups:

  • Small Business Data Warehouse Software Reviews (< 50 employees)
  • large Data Warehouse Software Reviews (51-1,000 employees)
  • Enterprise Data Warehouse Software Reviews (1,001+ employees)

large businesses have their own unique requirements when it comes to Data Warehouse software compared to small or enterprise companies. That is why these individual large-scale company Data Warehouse software reviews are especially helpful when comparing product set up, ease of use and overall cost.

Below you will find FAQs and review snippets for Data Warehouse software products that work well in large environments. These reviews are written by real G2 users working for these Enterprise companies who have real-world experience with these Data Warehouse software products.

FAQs About Data Warehouse Software for Enterprise Businesses

Q. What are the best Data Warehouse software products for large-scale businesses, according to G2 reviewers?
  • Databricks - 4.7-stars, 246 reviews (48% from large reviewers)
  • Google Cloud BigQuery - 4.5-stars, 249 reviews (38% from large reviewers)
  • Snowflake - 4.5-stars, 264 reviews (42% from large reviewers)
  • SAP Datasphere - 4.2-stars, 63 reviews (41% from large reviewers)
  • VMware Greenplum - 4.2-stars, 26 reviews (47% from large reviewers)
Q. What are the least-liked Data Warehouse software products for professionals at large-scale companies, based on the overall G2 rating?
  • Hive - 4.1-stars, 31 reviews (54% from large reviewers)
  • Zap Data Hub - 3.8-stars, 12 reviews (28% from large reviewers)
  • SAP BW/4HANA - 4.0-stars, 11 reviews (73% from large reviewers)
  • Yellowbrick - 4.9-stars, 10 reviews (71% from large reviewers)
  • IBM InfoSphere Information Server - 4.2-stars, 10 reviews (91% from large reviewers)
Q. What are the highest-rated Data Warehouse software tool features according to employees at Enterprise companies?
  • Scalability - 4.4-stars
  • Data Security - 4.4-stars
  • Cloud - 4.4-stars
Q. What are the features that are rated lowest by employees at Enterprise companies?
  • AI Text Summarization - 3.6-stars
  • AI Text Generation - 3.7-stars
  • In-Database Machine Learning - 3.9-stars
Q. How many Data Warehouse software reviews are from users working in large companies?

1660 out of all Data Warehouse software reviews on G2 are from users working in Enterprise companies (23%).

Here's What Reviewers Working at large Businesses Have to Say About Data Warehouse Software

What Enterprise Professionals Liked What Enterprise Professionals Disliked

Databricks: “Databricks is especially helpful because it brings data engineering, analytics, and machine learning together in a single unified platform, which reduces the need to manage multiple separate tools. Built on Apache Spark, it can process massive datasets quickly and scale smoothly as workloads grow, making it a strong fit for big data use cases. It also supports collaborative notebooks where teams can work together in languages like Python and SQL, which makes it easier for data scientists and engineers to collaborate effectively. With its lakehouse architecture powered by Delta Lake, Databricks combines the flexibility of data lakes with the reliability of data warehouses, helping ensure better data consistency and performance. In addition, it integrates with tools like MLflow to streamline the machine learning lifecycle end to end, from experimentation through deployment. Overall, Databricks simplifies complex data workflows, improves performance, and helps organizations build scalable data and AI solutions more efficiently.”

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IBM Db2: “You can't declare global variables, after using more than 5 different databases this is the first I've come across that does not support them. There is no online support or documentation that can give you the answer you are looking for. It does not support PIVOT/UNPIVOT functions so you are not able to transpose anything. This system was big in the '80s and died in the '80s. After showing clients who use DB2 a different database system (literally any, didn't matter which really) they were blown away and instantly started transitioning away from DB2 and on to the new system.”

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Databricks: “Unity Catalog has been the single biggest value-add for our enterprise migration. We moved from a Hive Metastore architecture to Unity Catalog and gained centralized governance, lineage tracking, and fine-grained access control across all our data assets without bolting on third-party tools. For a multi-domain organization (finance, manufacturing, supply chain, procurement), having one catalog that enforces consistent naming and permissions across bronze, silver, gold, and platinum layers saved us weeks of manual policy work. UI/UX: The notebook experience with inline Spark SQL and PySpark, combined with the workspace file browser, makes it straightforward for our team to develop and test transformations iteratively. The SQL editor for ad-hoc queries against Unity Catalog tables is clean and responsive. Integrations: Native Delta Lake support means we don't manage format conversions. The Azure Key Vault integration via secret scopes (dbutils.secrets.get) keeps credentials out of code. ADF integration for orchestration in our V1 environment was seamless, and Databricks Asset Bundles (DAB) for V2 deployment give us a clean CI/CD path with databricks.yml configs targeting dev/qa/prod without custom scripting. Performance: Switching to CTEs over temp views in our Gold notebooks reduced cluster memory pressure noticeably. The ability to right-size clusters per environment (1 worker for dev, 3 for production) with Standard_D4ds_v5 nodes keeps costs predictable while maintaining performance for our batch ETL workloads. Pricing/ROI: The pay-as-you-go compute model paired with single-user security mode clusters means we're not over-provisioning. Consolidating our ETL, governance, and BI serving layer into one platform eliminated licensing for separate catalog, orchestration, and data quality tools. AI/Intelligence (Genie): Genie Spaces have been an unexpected win. Our business analysts in finance and supply chain can ask natural language questions against curated Gold/Platinum tables without writing SQL. It reduced the number of ad-hoc report requests coming to the data team by giving domain users a self-service path that still respects Unity Catalog permissions. Support/Onboarding: The documentation is thorough, and the skills-based approach to learning (bundles, Unity Catalog, jobs, SQL) maps well to how our team actually works. Onboarding new engineers to the V2 architecture took about half the time compared to V1 because the platform conventions (medallion architecture, asset bundles, catalog naming) are well-documented and consistent.”

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SAP Datasphere: “The existing version of S4 does not allow for good connectors. Data replication is not reliable and can take up to 24 hours.”

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Databricks: “This is really a nice user friendly platform.”

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Hive: “. at my company it was fairly troublesome getting access since it's underlying warehouseing is in hadoop, then have to connect through hive”

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Recommendations To Others Problems Solved & Benefits

Rocket Vertica: “It is best tool for data warehousing and solving Big Data solutions. Also, It provides fastest ETL transactions and on-time data delivery.”

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Cloudera: “We help implement Cloudera for data warehouse migration and modernization, big data initiatives, and Generative AI initiatives. For data warehouse modernization and big data initiatives, our customers benefit from a modern data lakehouse stack, improved performance, and reduced data silos by creating a single source of truth on top of Cloudera. With Cloudera data services, our customers can explore data faster and run ad-hoc reports more easily, powered by Cloudera Data Explorer and Impala on top of virtual warehouses. Cloudera AI also helps our customers get started with their Generative AI use cases, enabling them to quickly create LLM-powered apps without needing to manually set up infrastructure and security.”

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Google Cloud BigQuery: “Help needed in developing complex OLTP data models”

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Cloudera: “We are under implementation phase”

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Snowflake: “Dig in and play around. Use the built in geospatial functions.”

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Cloudera: “Big Data Lake, CML”

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