# Best Enterprise Data Warehouse Solutions *By [Shalaka Joshi](https://research.g2.com/insights/author/shalaka-joshi)* Products classified in the overall Data Warehouse category are similar in many regards and help companies of all sizes solve their business problems. However, enterprise business features, pricing, setup, and installation differ from businesses of other sizes, which is why we match buyers to the right Enterprise Business Data Warehouse to fit their needs. Compare product ratings based on reviews from enterprise users or connect with one of G2's buying advisors to find the right solutions within the Enterprise Business Data Warehouse category. In addition to qualifying for inclusion in the Data Warehouse Solutions category, to qualify for inclusion in the Enterprise Business Data Warehouse Solutions category, a product must have at least 10 reviews left by a reviewer from an enterprise business. ## Top Data Warehouse Solutions at a Glance | # | Product | Rating | Best For | What Users Say | |---|---------|--------|----------|----------------| | 1 | [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) | 4.5/5.0 (1,144 reviews) | Serverless SQL analytics on petabyte-scale datasets | "[Easy-to-Use Cloud Tool with Shareable, Saved Queries](https://www.g2.com/survey_responses/google-cloud-bigquery-review-12958418)" | | 2 | [Databricks](https://www.g2.com/products/databricks/reviews) | 4.6/5.0 (1,321 reviews) | Unified lakehouse warehousing with governed analytics | "[Helpful for Managing and Analyzing Operational Data](https://www.g2.com/survey_responses/databricks-review-13090803)" | | 3 | [Snowflake](https://www.g2.com/products/snowflake/reviews) | 4.5/5.0 (707 reviews) | Elastic multi-workload cloud data warehousing | "[Easy, Efficient Data Extraction with Clear Database Insights](https://www.g2.com/survey_responses/snowflake-review-12884116)" | | 4 | [SAP Datasphere](https://www.g2.com/products/sap-datasphere/reviews) | 4.2/5.0 (166 reviews) | SAP-native semantic data warehousing with virtualization | "[SAP Datasphere Simplifies Multi-Source Data Consolidation and Integration](https://www.g2.com/survey_responses/sap-datasphere-review-12739150)" | | 5 | [IBM watsonx.data](https://www.g2.com/products/ibm-watsonx-data/reviews) | 4.4/5.0 (159 reviews) | Federated lakehouse querying without data movement | "[Powerful Query Performance and Governance, But a Steep Onboarding Learning Curve](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12836202)" | | 6 | [Amazon Redshift](https://www.g2.com/products/amazon-redshift/reviews) | 4.3/5.0 (370 reviews) | AWS-native analytical data warehousing at petabyte scale | "[Scalable and Efficient Cloud Data Platform](https://www.g2.com/survey_responses/amazon-redshift-review-12872150)" | | 7 | [Teradata Autonomous Knowledge Platform](https://www.g2.com/products/teradata-autonomous-knowledge-platform/reviews) | 4.3/5.0 (357 reviews) | Massively parallel enterprise data warehousing with in-database analytics | "[Teradata Vantage Fast Query Performance and Strong Analytics for Big Data](https://www.g2.com/survey_responses/teradata-autonomous-knowledge-platform-review-12821668)" | | 8 | [SQL Server 2019](https://www.g2.com/products/sql-server-2019/reviews) | 4.5/5.0 (78 reviews) | Relational data warehousing with Microsoft-native analytics | "[SQL Server 2019 Review](https://www.g2.com/survey_responses/sql-server-2019-review-10693637)" | | 9 | [VMware Greenplum](https://www.g2.com/products/vmware-greenplum/reviews) | 4.3/5.0 (55 reviews) | Petabyte-scale OLAP with MPP parallelism | "[Open-Source MPP Database That Supercharges Large-Scale Analytics](https://www.g2.com/survey_responses/vmware-greenplum-review-12872814)" | | 10 | [IBM Netezza Performance Server](https://www.g2.com/products/ibm-netezza-performance-server/reviews) | 4.1/5.0 (68 reviews) | High-throughput MPP analytics without query tuning | "[Unleashing intelligence with IBM Netezza, driving data analysis and expediting Insights.](https://www.g2.com/survey_responses/ibm-netezza-performance-server-review-9011395)" | ## G2 Grid® for Data Warehouse Solutions ![G2 Grid® for Data Warehouse Solutions plotting products by satisfaction and market presence](https://www.g2.com/categories/data-warehouse/grids.png?focus%5B%5D=10470&focus%5B%5D=6073&focus%5B%5D=10938&focus%5B%5D=129730&focus%5B%5D=6058&focus%5B%5D=10898&focus%5B%5D=58132&focus%5B%5D=1308796) 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) ## How Many Data Warehouse Solutions Products Does G2 Track? **Total Products under this Category:** 120 ### Category Stats (Jul 2026) - **Average Rating**: 4.37/5 The average rating of products in this category, based on all submitted ratings - **Top Trending Product**: Databricks (+0.59%) - Among all products in this category, Databricks recorded the largest rating increase compared to last month *Last updated: July 17, 2026* ## How Does G2 Rank Data Warehouse Solutions Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 7,400+ Authentic Reviews - 120+ 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. ## Which Data Warehouse Solutions Is Best for Your Use Case? - **Best for Small Businesses:** [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - **Best for Mid-Market:** [Databricks](https://www.g2.com/products/databricks/reviews) - **Best for Enterprise:** [Databricks](https://www.g2.com/products/databricks/reviews) - **Highest User Satisfaction:** [Databricks](https://www.g2.com/products/databricks/reviews) - **Best Free Software:** [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) --- **Sponsored** ### 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. 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. [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=77&secure%5Bchosen_at%5D=2026-07-18T20%3A29%3A22Z&secure%5Bdisplayable_resource_id%5D=77&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=77&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=1886&secure%5Bresource_id%5D=77&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fdata-warehouse%2Fenterprise%3Fopen_modal_url%3D%252Fproducts%252Foracle-exadata-cloud-service%252Fwishlists%253Fhost_path%253D%25252Fcategories%25252Fdata-warehouse%25252Fenterprise%2526source%253Dcategory&secure%5Btoken%5D=89a8d06bfd854a8418fd51d8f4c13036cd09fb08dbc587a3973dc46cf92debcb&secure%5Burl%5D=https%3A%2F%2Fwww.cloudera.com%2Fproducts%2Fcloudera-data-platform%2Fcdp-demos.html%3Finternal_link%3Dp18%23get-started&secure%5Burl_type%5D=custom_url) --- ## What Are the Top-Rated Data Warehouse Solutions Products in 2026? ### 1. [Databricks](https://www.g2.com/products/databricks/reviews) Databricks is a unified data and AI platform that helps organizations build, govern and scale data pipelines, analytics, machine learning, AI applications and agents. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on Databricks to work with enterprise data and AI at scale. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Agent Bricks, Lakeflow, Lakehouse, Lakebase, Genie 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:** 1,321 **How Do G2 Users Rate Databricks?** - **Ease of Use:** 8.8/10 (Category avg: 8.7/10) - **Data Governance:** 8.9/10 (Category avg: 8.4/10) - **Data Security:** 9.0/10 (Category avg: 8.8/10) - **Scalability:** 9.2/10 (Category avg: 8.5/10) **Who Is the Company Behind Databricks?** - **Seller:** [Databricks Inc.](https://www.g2.com/sellers/databricks-inc) - **Company Website:** https://databricks.com - **Year Founded:** 2013 - **HQ Location:** San Francisco, CA - **Twitter:** @databricks (92,269 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/3477522/ (15,627 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Engineer, Data Analyst - **Top Industries:** Information Technology and Services, Financial Services - **Company Size:** 48% Enterprise, 38% Mid-Market #### What Are Databricks's Pros and Cons? **Pros:** - Features (192 reviews) - Ease of Use (154 reviews) - Integrations (141 reviews) - Collaboration (114 reviews) - Analytics (112 reviews) **Cons:** - Learning Curve (78 reviews) - Expensive (71 reviews) - Steep Learning Curve (64 reviews) - Complexity (45 reviews) - Complex Setup (35 reviews) ### What Do G2 Reviewers Say About Databricks? *AI-generated summary from verified user reviews* **Pros:** - Users praise the **ease of use** and **comprehensive features** of Databricks for data warehousing and ML applications. - Users praise the **ease of use** of Databricks, enhancing their experience with intuitive interfaces and reliable services. - Users appreciate the **seamless integrations** of Databricks with AWS and other tools, enhancing daily operations and efficiency. - Users value the **seamless collaboration** offered by Databricks, enhancing teamwork on data projects with real-time insights. - Users praise the **integrated analytical features** of Databricks, enhancing collaborative data processing and insight visualization. **Cons:** - Users note a **steep learning curve** initially, with confusing permissions and compute modes affecting usability. - Users note that the **costs can be quite high** for utilizing Databricks effectively, especially for large data projects. - Users find a **steep learning curve** with Databricks, especially challenging for newcomers to big data tools. - Users find the **complexity** of Databricks challenging, especially for smaller teams and initial setup processes. - Users face **complex setup** challenges initially, though support helps simplify the experience over time. #### What Are Recent G2 Reviews of Databricks? **"[Helpful for Managing and Analyzing Operational Data](https://www.g2.com/survey_responses/databricks-review-13090803)"** **Rating:** 4.5/5.0 stars *— Vishaka C.* [Read full review](https://www.g2.com/survey_responses/databricks-review-13090803) --- **"[Streamlined Legal Workflow with Databricks](https://www.g2.com/survey_responses/databricks-review-13090519)"** **Rating:** 5.0/5.0 stars *— Pranjal S.* [Read full review](https://www.g2.com/survey_responses/databricks-review-13090519) --- #### What Are G2 Users Discussing About Databricks? - [What does Databricks software do?](https://www.g2.com/discussions/what-does-databricks-software-do) - 3 comments - [What is Databricks unified analytics platform?](https://www.g2.com/discussions/what-is-databricks-unified-analytics-platform) - 3 comments - [What is Lakehouse in Databricks?](https://www.g2.com/discussions/what-is-lakehouse-in-databricks) - 4 comments, 2 upvotes - [What are the features of Databricks?](https://www.g2.com/discussions/what-are-the-features-of-databricks) - 4 comments, 2 upvotes ### 2. [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) BigQuery is a fully managed, AI-ready data analytics platform that helps you maximize value from your data and is designed to be multi-engine, multi-format, and multi-cloud. 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: 8.7/10) - **Data Governance:** 8.8/10 (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](https://www.g2.com/sellers/google) - **Year Founded:** 1998 - **HQ Location:** Mountain View, CA - **Twitter:** @google (31,899,995 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (341,888 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% Enterprise, 35% Mid-Market #### What Are Google Cloud BigQuery's Pros and Cons? **Pros:** - Ease of Use (129 reviews) - Speed (126 reviews) - Integrations (110 reviews) - Fast Querying (105 reviews) - Query Efficiency (100 reviews) **Cons:** - Expensive (112 reviews) - Query Issues (65 reviews) - Cost Management (52 reviews) - Cost Issues (51 reviews) - Learning Curve (49 reviews) ### What Do G2 Reviewers Say About Google Cloud BigQuery? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of Google Cloud BigQuery, enabling quick analysis of massive datasets without hassle. - Users appreciate the **exceptional speed** of BigQuery, allowing for quick processing of large datasets seamlessly. - Users love the **easy integration** with Google Cloud services, enabling smooth data analysis and management. - Users appreciate the **fast querying capabilities** of Google Cloud BigQuery, allowing effortless analysis of massive datasets. - Users appreciate the **query efficiency** of BigQuery, effortlessly processing complex queries on massive datasets with speed. **Cons:** - Users find the **costs can escalate quickly** with Google Cloud BigQuery, requiring careful query optimization to manage expenses. - Users struggle with **query issues** in BigQuery, facing rising costs and challenges in query optimization and troubleshooting. - Users find **cost management challenging** with Google Cloud BigQuery due to unpredictable pricing and incidents of unexpected charges. - Users experience **cost issues** with Google Cloud BigQuery, struggling with unexpected high bills and limited pricing visibility. - Users find the **steep learning curve** of Google Cloud BigQuery challenging, particularly for advanced features and optimization techniques. #### What Are Recent G2 Reviews of Google Cloud BigQuery? **"[Easy-to-Use Cloud Tool with Shareable, Saved Queries](https://www.g2.com/survey_responses/google-cloud-bigquery-review-12958418)"** **Rating:** 4.0/5.0 stars *— Reetika P.* [Read full review](https://www.g2.com/survey_responses/google-cloud-bigquery-review-12958418) --- **"[Scalable, Secure BigQuery That Connects Seamlessly Across Services](https://www.g2.com/survey_responses/google-cloud-bigquery-review-12638747)"** **Rating:** 5.0/5.0 stars *— Aayush M.* [Read full review](https://www.g2.com/survey_responses/google-cloud-bigquery-review-12638747) --- #### What Are G2 Users Discussing About Google Cloud BigQuery? - [Is Big Query free?](https://www.g2.com/discussions/is-big-query-free) - 3 comments, 1 upvote - [Is BigQuery part of Google Cloud Platform?](https://www.g2.com/discussions/is-bigquery-part-of-google-cloud-platform) - 2 comments, 2 upvotes - [What is Google BigQuery based on?](https://www.g2.com/discussions/what-is-google-bigquery-based-on) - 1 comment - [What is Google BigQuery used for?](https://www.g2.com/discussions/what-is-google-bigquery-used-for) - 1 comment ### 3. [Snowflake](https://www.g2.com/products/snowflake/reviews) 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:** 4.5/5.0 **Total Reviews:** 707 **How Do G2 Users Rate Snowflake?** - **Ease of Use:** 9.0/10 (Category avg: 8.7/10) - **Data Governance:** 8.9/10 (Category avg: 8.4/10) - **Data Security:** 9.1/10 (Category avg: 8.8/10) - **Scalability:** 9.4/10 (Category avg: 8.5/10) **Who Is the Company Behind Snowflake?** - **Seller:** [Snowflake, Inc.](https://www.g2.com/sellers/snowflake-inc) - **Company Website:** https://www.snowflake.com - **Year Founded:** 2012 - **HQ Location:** 135 Constitution Drive, Menlo Park CA - **Twitter:** @SnowflakeDB (278 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/snowflake-computing/ (11,308 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% Mid-Market, 43% Enterprise #### What Are Snowflake's Pros and Cons? **Pros:** - Ease of Use (183 reviews) - Features (118 reviews) - Data Management (108 reviews) - Scalability (99 reviews) - Performance (90 reviews) **Cons:** - Expensive (91 reviews) - Feature Limitations (54 reviews) - Cost (44 reviews) - Cost Management (44 reviews) - Limited Features (42 reviews) ### What Do G2 Reviewers Say About Snowflake? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of Snowflake, finding it fast and effective for data sharing and analytics. - Users value the **reliable features** of Snowflake, enjoying its intuitive interface and seamless data integration for analytics. - Users find Snowflake's **data management capabilities** excellent for efficiently aggregating and querying across multiple datasets. - Users admire the **seamless scalability** of Snowflake, effortlessly accommodating work demands and ensuring optimal performance. - Users appreciate the **fast data analysis** of Snowflake, enabling quick insights without infrastructure worries. **Cons:** - Users find Snowflake's **high costs** burdensome, especially for small businesses with limited budgets. - Users find **feature limitations** in Snowflake, such as lack of code blocks and challenge in permissions management. - Users find that **cost management** requires discipline, as unexpected charges can accumulate quickly without careful monitoring. - Users find the **cost structure difficult to optimize** , leading to unexpectedly high initial expenses during implementation. - Users find Snowflake's **limited features** in dynamic scripts and monitoring hinder flexibility and usability. #### What Are Recent G2 Reviews of Snowflake? **"[Snowflake Simplifies Data Management at Scale](https://www.g2.com/survey_responses/snowflake-review-12898129)"** **Rating:** 4.0/5.0 stars *— Harshil A.* [Read full review](https://www.g2.com/survey_responses/snowflake-review-12898129) --- **"[Easy, Efficient Data Extraction with Clear Database Insights](https://www.g2.com/survey_responses/snowflake-review-12884116)"** **Rating:** 4.5/5.0 stars *— Pankaj K.* [Read full review](https://www.g2.com/survey_responses/snowflake-review-12884116) --- #### What Are G2 Users Discussing About Snowflake? - [What is Snowflake used for?](https://www.g2.com/discussions/what-is-snowflake-used-for) - 2 comments, 1 upvote ### 4. [SAP Datasphere](https://www.g2.com/products/sap-datasphere/reviews) 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:** 166 **How Do G2 Users Rate SAP Datasphere?** - **Ease of Use:** 8.1/10 (Category avg: 8.7/10) - **Data Governance:** 8.6/10 (Category avg: 8.4/10) - **Data Security:** 8.7/10 (Category avg: 8.8/10) - **Scalability:** 8.2/10 (Category avg: 8.5/10) **Who Is the Company Behind SAP Datasphere?** - **Seller:** [SAP](https://www.g2.com/sellers/sap) - **Company Website:** https://www.sap.com/ - **Year Founded:** 1972 - **HQ Location:** Walldorf - **Twitter:** @SAP (297,052 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/sap/ (141,955 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Business Analyst, Software Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 40% Enterprise, 35% Mid-Market #### What Are SAP Datasphere's Pros and Cons? **Pros:** - Ease of Use (43 reviews) - Easy Integrations (33 reviews) - Data Management (29 reviews) - Analytics (22 reviews) - Collaboration (21 reviews) **Cons:** - Slow Performance (25 reviews) - Expensive (23 reviews) - Performance Issues (23 reviews) - Integration Issues (19 reviews) - Complex Setup (17 reviews) ### What Do G2 Reviewers Say About SAP Datasphere? *AI-generated summary from verified user reviews* **Pros:** - Users find SAP Datasphere's **ease of use** greatly enhances their productivity compared to other services. - Users appreciate the **easy integrations** of SAP Datasphere, enabling seamless data access and enhanced analytics capabilities. - Users value the **unified data management** capabilities of SAP Datasphere, enhancing data access and analytics efficiency. - Users value the **intuitive analytics capabilities** of SAP Datasphere, simplifying data integration and enhancing insights generation. - Users value the **seamless collaboration** SAP Datasphere offers, enhancing integration and data accessibility across SAP products. **Cons:** - Users are frustrated with the **slow performance** of SAP Datasphere, especially when handling large datasets and complex tasks. - Users note the **expensive pricing** of SAP Datasphere, which can be a significant drawback for new users. - Users often face **performance issues** with SAP Datasphere, particularly with speed and large dataset handling. - Users face **integration issues** with SAP Datasphere, requiring middleware solutions and causing performance challenges, especially for beginners. - Users find the **complex setup** of SAP Datasphere frustrating due to vague error messages and steep learning curve. #### What Are Recent G2 Reviews of SAP Datasphere? **"[SAP Datasphere A Powerful Platform for Data Integration and Real-Time Business Insights](https://www.g2.com/survey_responses/sap-datasphere-review-12817894)"** **Rating:** 5.0/5.0 stars *— Muzammil M.* [Read full review](https://www.g2.com/survey_responses/sap-datasphere-review-12817894) --- **"[SAP Datasphere Simplifies Multi-Source Data Consolidation and Integration](https://www.g2.com/survey_responses/sap-datasphere-review-12739150)"** **Rating:** 4.5/5.0 stars *— Nijat I.* [Read full review](https://www.g2.com/survey_responses/sap-datasphere-review-12739150) --- #### What Are G2 Users Discussing About SAP Datasphere? - [What is SAP Data Warehouse Cloud used for?](https://www.g2.com/discussions/what-is-sap-data-warehouse-cloud-used-for) - 1 comment ### 5. [Amazon Redshift](https://www.g2.com/products/amazon-redshift/reviews) 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:** 370 **How Do G2 Users Rate Amazon Redshift?** - **Ease of Use:** 8.7/10 (Category avg: 8.7/10) - **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)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd) - **Year Founded:** 2006 - **HQ Location:** Seattle, WA - **Twitter:** @awscloud (2,232,483 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (147,094 employees on LinkedIn®) - **Ownership:** NASDAQ: AMZN **Who Uses This Product?** - **Who Uses This:** Data Engineer, Senior Data Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 40% Enterprise, 39% Mid-Market #### What Are Amazon Redshift's Pros and Cons? **Pros:** - Fast Querying (5 reviews) - Integrations (5 reviews) - Ease of Use (4 reviews) - Easy Integrations (4 reviews) - Performance (4 reviews) **Cons:** - Feature Limitations (4 reviews) - Software Limitations (4 reviews) - Complexity (3 reviews) - Query Issues (3 reviews) - Query Optimization (3 reviews) ### What Do G2 Reviewers Say About Amazon Redshift? *AI-generated summary from verified user reviews* **Pros:** - Users value the **fast querying capabilities** of Amazon Redshift, enabling efficient analysis of large datasets seamlessly. - Users praise the **easy integrations** with other software, enhancing data solutions within the Amazon Redshift ecosystem. - Users find Amazon Redshift's **ease of use** exceptional, facilitating quick access and efficient data management. - Users value the **easy integrations** of Amazon Redshift, enhancing their ability to build seamless data solutions. - Users praise the **impressive speed and scalability** of Amazon Redshift, optimizing data management and query performance. **Cons:** - Users find **feature limitations** in Redshift, especially regarding advanced analytics and multi-language support for coding. - Users note significant **software limitations** with Redshift, particularly regarding cost and performance issues with complex queries. - Users find the **complexity of optimizations** in Amazon Redshift burdensome, requiring significant management and maintenance effort. - Users face **query issues** with Amazon Redshift, requiring extensive optimization and management to maintain performance. - Users find the **query optimization process cumbersome** , as it requires significant effort and specialized knowledge for efficiency. #### What Are Recent G2 Reviews of Amazon Redshift? **"[Powerful Analytics Tool with Some Flexibility Limitations](https://www.g2.com/survey_responses/amazon-redshift-review-12781722)"** **Rating:** 4.0/5.0 stars *— Atharva P.* [Read full review](https://www.g2.com/survey_responses/amazon-redshift-review-12781722) --- **"[Scalable and Efficient Cloud Data Platform](https://www.g2.com/survey_responses/amazon-redshift-review-12872150)"** **Rating:** 4.5/5.0 stars *— Swaroop W.* [Read full review](https://www.g2.com/survey_responses/amazon-redshift-review-12872150) --- #### What Are G2 Users Discussing About Amazon Redshift? - [What is Amazon Redshift used for?](https://www.g2.com/discussions/what-is-amazon-redshift-used-for) - [Is AWS redshift a database?](https://www.g2.com/discussions/is-aws-redshift-a-database) - 1 comment - [When can I use Amazon redshift?](https://www.g2.com/discussions/when-can-i-use-amazon-redshift) - 3 comments - [What are the characteristics of redshift?](https://www.g2.com/discussions/what-are-the-characteristics-of-redshift) - 2 comments - [What does Amazon redshift do?](https://www.g2.com/discussions/what-does-amazon-redshift-do) - 2 comments ### 6. [Teradata Autonomous Knowledge Platform](https://www.g2.com/products/teradata-autonomous-knowledge-platform/reviews) 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:** 357 **How Do G2 Users Rate Teradata Autonomous Knowledge Platform?** - **Ease of Use:** 8.3/10 (Category avg: 8.7/10) - **Data Governance:** 7.9/10 (Category avg: 8.4/10) - **Data Security:** 8.2/10 (Category avg: 8.8/10) - **Scalability:** 8.6/10 (Category avg: 8.5/10) **Who Is the Company Behind Teradata Autonomous Knowledge Platform?** - **Seller:** [Teradata Autonomous Knowledge Platform](https://www.g2.com/sellers/teradata-autonomous-knowledge-platform) - **Company Website:** https://www.teradata.com - **Year Founded:** 1979 - **HQ Location:** San Diego, CA - **Twitter:** @Teradata (93,113 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1466/ (9,901 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Engineer, Software Engineer - **Top Industries:** Information Technology and Services, Financial Services - **Company Size:** 68% Enterprise, 22% Mid-Market #### What Are Teradata Autonomous Knowledge Platform's Pros and Cons? **Pros:** - Performance (14 reviews) - Analytics (11 reviews) - Scalability (11 reviews) - Speed (11 reviews) - Large Datasets (9 reviews) **Cons:** - Learning Curve (9 reviews) - Steep Learning Curve (5 reviews) - Complexity (4 reviews) - Cost (3 reviews) - Expensive (3 reviews) ### What Do G2 Reviewers Say About Teradata Autonomous Knowledge Platform? *AI-generated summary from verified user reviews* **Pros:** - Users highlight the **extreme performance** of the Teradata Autonomous Knowledge Platform, especially for processing large data volumes efficiently. - Users value the **high performance query execution** in Teradata, enhancing their business analytics capabilities significantly. - Users value the **scalability** of Teradata Autonomous Knowledge Platform, enhancing data integration and operational efficiency significantly. - Users commend the **high performance and speed** of Teradata, efficiently processing large datasets without issues. - Users value the **fast processing of large datasets** with Teradata, praising its performance and stability during operations. **Cons:** - Users find the **steep learning curve** of Teradata Autonomous Knowledge Platform challenging, impacting adoption and productivity temporarily. - Users find the **steep learning curve** of Teradata Autonomous Knowledge Platform challenging, particularly for those lacking technical expertise. - Users find the **complexity** of Teradata's platform challenging, particularly for non-technical users and new adopters. - Users express concerns over the **cost management requirements** needed to avoid potential misusage and performance issues. - Users feel the **high cost** of Teradata Autonomous Knowledge Platform is a significant drawback affecting accessibility. #### What Are Recent G2 Reviews of Teradata Autonomous Knowledge Platform? **"[Teradata Vantage Fast Query Performance and Strong Analytics for Big Data](https://www.g2.com/survey_responses/teradata-autonomous-knowledge-platform-review-12821668)"** **Rating:** 5.0/5.0 stars *— Muzammil M.* [Read full review](https://www.g2.com/survey_responses/teradata-autonomous-knowledge-platform-review-12821668) --- **"[Teradata Vantage Excels at Big Data Processing and Advanced Analytics](https://www.g2.com/survey_responses/teradata-autonomous-knowledge-platform-review-12739181)"** **Rating:** 4.5/5.0 stars *— Nijat I.* [Read full review](https://www.g2.com/survey_responses/teradata-autonomous-knowledge-platform-review-12739181) --- #### What Are G2 Users Discussing About Teradata Autonomous Knowledge Platform? - [What does Teradata Data Lab do?](https://www.g2.com/discussions/what-does-teradata-data-lab-do) - [Is Teradata a premiership?](https://www.g2.com/discussions/is-teradata-a-premiership) - [What is Teradata Vantage?](https://www.g2.com/discussions/what-is-teradata-vantage) - [How much does Teradata cost?](https://www.g2.com/discussions/how-much-does-teradata-cost) - [What is Sandbox in Teradata?](https://www.g2.com/discussions/what-is-sandbox-in-teradata) ### 7. [VMware Greenplum](https://www.g2.com/products/vmware-greenplum/reviews) 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: 8.7/10) - **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](https://www.g2.com/sellers/broadcom-ab3091cd-4724-46a8-ac89-219d6bc8e166) - **Year Founded:** 1991 - **HQ Location:** San Jose, CA - **Twitter:** @broadcom (63,909 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/broadcom/ (55,094 employees on LinkedIn®) - **Ownership:** NASDAQ: CA **Who Uses This Product?** - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 49% Enterprise, 31% Mid-Market #### What Are Recent G2 Reviews of VMware Greenplum? **"[Open-Source MPP Database That Supercharges Large-Scale Analytics](https://www.g2.com/survey_responses/vmware-greenplum-review-12872814)"** **Rating:** 5.0/5.0 stars *— deepeshkumar G.* [Read full review](https://www.g2.com/survey_responses/vmware-greenplum-review-12872814) --- **"[Greenplum - BIg Data Database](https://www.g2.com/survey_responses/vmware-greenplum-review-6752415)"** **Rating:** 4.0/5.0 stars *— Naveen A.* [Read full review](https://www.g2.com/survey_responses/vmware-greenplum-review-6752415) --- #### What Are G2 Users Discussing About VMware Greenplum? - [Who uses Greenplum?](https://www.g2.com/discussions/who-uses-greenplum) - [What type of database is greenplum?](https://www.g2.com/discussions/what-type-of-database-is-greenplum) - [What is Greenplum software?](https://www.g2.com/discussions/what-is-greenplum-software) - [What is greenplum used for?](https://www.g2.com/discussions/what-is-greenplum-used-for) ### 8. [IBM watsonx.data](https://www.g2.com/products/ibm-watsonx-data/reviews) 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:** 159 **How Do G2 Users Rate IBM watsonx.data?** - **Ease of Use:** 8.2/10 (Category avg: 8.7/10) - **Data Governance:** 9.4/10 (Category avg: 8.4/10) - **Data Security:** 9.6/10 (Category avg: 8.8/10) - **Scalability:** 9.1/10 (Category avg: 8.5/10) **Who Is the Company Behind IBM watsonx.data?** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Company Website:** https://www.ibm.com - **Year Founded:** 1911 - **HQ Location:** Armonk, New York, United States - **Twitter:** @IBMSecurity (74,660 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (328,202 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Software Engineer, CEO - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 34% Small-Business, 32% Enterprise #### What Are IBM watsonx.data's Pros and Cons? **Pros:** - Ease of Use (67 reviews) - Features (47 reviews) - Data Management (41 reviews) - Integrations (33 reviews) - Analytics (31 reviews) **Cons:** - Learning Curve (38 reviews) - Complexity (25 reviews) - Expensive (20 reviews) - Difficult Setup (17 reviews) - Difficulty (17 reviews) ### 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 tasks. - Users value the **organized data integration** and intuitive interface of IBM watsonx.data, enhancing efficiency and analytics. - Users value the **organized and efficient data management** of IBM watsonx.data, enhancing analytics and reporting tasks seamlessly. - Users value the **seamless data source integration** in IBM watsonx.data, enhancing flexibility and efficiency for diverse projects. - Users value the **ability to unify data across hybrid environments** , enhancing flexibility and driving informed decision-making. **Cons:** - Users find the **learning curve steep** , making initial setup and navigation challenging for newcomers to IBM watsonx.data. - Users find the **complexity** of setting up IBM watsonx.data a barrier, especially for newcomers to IBM technologies. - Users find the **pricing steep** for IBM watsonx.data, making it less accessible for smaller businesses and projects. - Users find the **difficult setup** of IBM watsonx.data time-consuming, with a steep learning curve and complex configurations. - Users find IBM watsonx.data **difficult to navigate** , especially for beginners and those unfamiliar with AI and data analytics. #### What Are Recent G2 Reviews of IBM watsonx.data? **"[Powerful Query Performance and Governance, But a Steep Onboarding Learning Curve](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12836202)"** **Rating:** 4.0/5.0 stars *— Arkajit D.* [Read full review](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12836202) --- **"[Unified Data Management with Learning Curve](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12817742)"** **Rating:** 5.0/5.0 stars *— Anchal P.* [Read full review](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12817742) --- ### 9. [IBM Netezza Performance Server](https://www.g2.com/products/ibm-netezza-performance-server/reviews) 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: 8.7/10) - **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](https://www.g2.com/sellers/ibm) - **Year Founded:** 1911 - **HQ Location:** Armonk, New York, United States - **Twitter:** @IBMSecurity (74,660 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (328,202 employees on LinkedIn®) - **Ownership:** SWX:IBM **Who Uses This Product?** - **Top Industries:** Information Technology and Services, Banking - **Company Size:** 62% Enterprise, 27% Mid-Market #### What Are IBM Netezza Performance Server's Pros and Cons? **Pros:** - Speed (5 reviews) - Performance (4 reviews) - Ease of Use (3 reviews) - Fast Processing (3 reviews) - Efficiency (2 reviews) **Cons:** - Expensive (3 reviews) - High Maintenance Costs (2 reviews) - Integration Issues (1 reviews) - Limited Customization (1 reviews) - Slow Performance (1 reviews) ### 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 handling complex queries efficiently. - Users rave about the **exceptional speed and efficiency** of IBM Netezza Performance Server, enhancing data analysis and processing tasks. - Users value the **user-friendly interface** of IBM Netezza Performance Server, making data analysis and integration effortless. - Users praise the **exceptional processing speed** of IBM Netezza Performance Server, enhancing data analysis and query efficiency. - Users commend the **high processing speed and efficiency** of IBM Netezza Performance Server for seamless data management. **Cons:** - Users identify the **high cost** of IBM Netezza Performance Server as a significant challenge, particularly for smaller enterprises. - Users find the **high maintenance costs** of IBM Netezza Performance Server a barrier, particularly for smaller businesses. - Users find **integration issues** challenging when connecting IBM Netezza Performance Server with other software tools. - Users find the **limited customization** options frustrating, which can hinder software integration and flexibility. - Users experience **slow performance** with IBM Netezza Performance Server when handling millions of records, affecting query times. #### What Are Recent G2 Reviews of IBM Netezza Performance Server? **"[Efficiency Unleashed: A Comprehensive Review of IBM Netezza Performance Server](https://www.g2.com/survey_responses/ibm-netezza-performance-server-review-8891188)"** **Rating:** 4.0/5.0 stars *— Verified User in Food Production* [Read full review](https://www.g2.com/survey_responses/ibm-netezza-performance-server-review-8891188) --- **"[Unleashing intelligence with IBM Netezza, driving data analysis and expediting Insights.](https://www.g2.com/survey_responses/ibm-netezza-performance-server-review-9011395)"** **Rating:** 5.0/5.0 stars *— Gunavardhan R.* [Read full review](https://www.g2.com/survey_responses/ibm-netezza-performance-server-review-9011395) --- #### What Are G2 Users Discussing About IBM Netezza Performance Server? - [What is IBM Netezza Performance Server used for?](https://www.g2.com/discussions/what-is-ibm-netezza-performance-server-used-for) - [What is IBM PureData?](https://www.g2.com/discussions/what-is-ibm-puredata) - [What is netezza used for?](https://www.g2.com/discussions/what-is-netezza-used-for) - [Is Netezza end of life?](https://www.g2.com/discussions/is-netezza-end-of-life) - [What is IBM PDA?](https://www.g2.com/discussions/what-is-ibm-pda) ### 10. [IBM Db2](https://www.g2.com/products/ibm-db2/reviews) 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:** 598 **How Do G2 Users Rate IBM Db2?** - **Ease of Use:** 8.0/10 (Category avg: 8.7/10) - **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](https://www.g2.com/sellers/ibm) - **Year Founded:** 1911 - **HQ Location:** Armonk, New York, United States - **Twitter:** @IBMSecurity (74,660 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (328,202 employees on LinkedIn®) - **Ownership:** SWX:IBM **Who Uses This Product?** - **Who Uses This:** Software Engineer, Senior Software Engineer - **Top Industries:** Information Technology and Services, Banking - **Company Size:** 66% Enterprise, 21% Mid-Market #### What Are IBM Db2's Pros and Cons? **Pros:** - Performance (14 reviews) - Reliability (13 reviews) - Scalability (11 reviews) - Security (11 reviews) - Ease of Use (10 reviews) **Cons:** - Complex Setup (4 reviews) - Expensive (4 reviews) - Learning Curve (4 reviews) - Complexity (3 reviews) - Difficult Setup (3 reviews) ### What Do G2 Reviewers Say About IBM Db2? *AI-generated summary from verified user reviews* **Pros:** - Users highlight the **exceptional performance** of IBM Db2, praising its reliability and scalability for critical workloads. - Users value the **high reliability** of IBM Db2, ensuring excellent performance and stability for critical workloads. - Users appreciate the **scalability** of IBM Db2, finding it ideal for high-performance, enterprise-level applications. - Users highlight the **strong security** of IBM Db2, making it a reliable choice for critical workloads across industries. - Users appreciate the **ease of use** of IBM Db2, highlighting its seamless integration and user-friendly features. **Cons:** - Users find the **complex setup** of IBM Db2 challenging, often leading to a steep learning curve for effective use. - 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** , with a steep learning curve and challenges in documentation and support. - Users find the **difficult setup** of IBM Db2 challenging, citing complexity and steep learning curves as significant barriers. #### What Are Recent G2 Reviews of IBM Db2? **"[Rock-Solid Performance, Great Features](https://www.g2.com/survey_responses/ibm-db2-review-12540353)"** **Rating:** 5.0/5.0 stars *— Ramesh Babu C.* [Read full review](https://www.g2.com/survey_responses/ibm-db2-review-12540353) --- **"[Comprehensive Review of DB2 on IBM i (AS/400)](https://www.g2.com/survey_responses/ibm-db2-review-9863177)"** **Rating:** 4.5/5.0 stars *— Swapnil T.* [Read full review](https://www.g2.com/survey_responses/ibm-db2-review-9863177) --- #### What Are G2 Users Discussing About IBM Db2? - [What is IBM Db2 used for?](https://www.g2.com/discussions/what-is-ibm-db2-used-for) - 1 comment - [What does Ibm Dashdb do?](https://www.g2.com/discussions/what-does-ibm-dashdb-do) - 1 comment - [What database does IBM use?](https://www.g2.com/discussions/what-database-does-ibm-use) - [What is DB2 Warehouse?](https://www.g2.com/discussions/what-is-db2-warehouse) - [What is IBM dashDB?](https://www.g2.com/discussions/what-is-ibm-dashdb) ### 11. [SQL Server 2019](https://www.g2.com/products/sql-server-2019/reviews) 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: 8.7/10) - **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](https://www.g2.com/sellers/microsoft) - **Year Founded:** 1975 - **HQ Location:** Redmond, Washington - **Twitter:** @microsoft (13,091,739 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (231,632 employees on LinkedIn®) - **Ownership:** MSFT **Who Uses This Product?** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 37% Mid-Market, 35% Enterprise #### What Are SQL Server 2019's Pros and Cons? **Pros:** - Data Integration (1 reviews) - SQL Support (1 reviews) **Cons:** - Expensive (1 reviews) ### What Do G2 Reviewers Say About SQL Server 2019? *AI-generated summary from verified user reviews* **Pros:** - Users value the **smooth integration with .NET Core** in SQL Server 2019, enhancing backend development and performance. - Users value the **advanced indexing and optimized data retrieval** in SQL Server 2019 for handling complex queries effectively. **Cons:** - Users find the **high licensing costs** of SQL Server 2019 to be a barrier, especially for smaller projects. #### What Are Recent G2 Reviews of SQL Server 2019? **"[SQL Server 2019 Review](https://www.g2.com/survey_responses/sql-server-2019-review-10693637)"** **Rating:** 4.5/5.0 stars *— Kavya S.* [Read full review](https://www.g2.com/survey_responses/sql-server-2019-review-10693637) --- **"[Improved performance and scalability: an excellent experience](https://www.g2.com/survey_responses/sql-server-2019-review-12241600)"** **Rating:** 5.0/5.0 stars *— Verified User in Computer & Network Security* [Read full review](https://www.g2.com/survey_responses/sql-server-2019-review-12241600) --- #### What Are G2 Users Discussing About SQL Server 2019? - [What is SQL Server 2019 used for?](https://www.g2.com/discussions/what-is-sql-server-2019-used-for) - 1 comment - [What is SQL Server and its features?](https://www.g2.com/discussions/sql-server-2019-what-is-sql-server-and-its-features) - [Why is SQL Server 2019?](https://www.g2.com/discussions/why-is-sql-server-2019) - [Is SQL Server 2019 released?](https://www.g2.com/discussions/is-sql-server-2019-released) - [What are the new features in SQL Server 2019?](https://www.g2.com/discussions/what-are-the-new-features-in-sql-server-2019) ### 12. [ILUM](https://www.g2.com/products/ilum-ilum/reviews) 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: 8.7/10) - **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](https://www.g2.com/sellers/ilum) - **Company Website:** https://ilum.cloud/ - **Year Founded:** 2019 - **HQ Location:** Santa Fe, US - **Twitter:** @IlumCloud (19 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/ilum-cloud/ (4 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Telecommunications - **Company Size:** 52% Enterprise, 35% Mid-Market #### What Are ILUM's Pros and Cons? **Pros:** - Ease of Use (17 reviews) - Features (17 reviews) - Integrations (17 reviews) - Setup Ease (16 reviews) - Easy Integrations (15 reviews) **Cons:** - Complex Setup (9 reviews) - Difficult Setup (9 reviews) - Learning Curve (9 reviews) - UX Improvement (8 reviews) - Complexity (7 reviews) ### What Do G2 Reviewers Say About ILUM? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of ILUM, enjoying seamless integration and a smooth workflow for managing data. - Users value the **seamless integration** of ILUM with existing systems, enhancing efficiency in data management and analytics. - Users appreciate the **seamless integrations** of ILUM, enhancing data management and analytics with a unified, efficient platform. - Users value the **quick and seamless setup** of ILUM, appreciating its ease of integration and efficiency. - Users appreciate the **easy integrations** of ILUM, smoothly fitting into existing systems without major rewrites. **Cons:** - Users find the **complex setup** for advanced configurations of ILUM can be challenging for newcomers to navigate. - Users find the **difficult setup** process of ILUM challenging, requiring significant effort and experimentation to optimize configurations. - Users find the **learning curve steep** for advanced features in ILUM, requiring time to adapt and configure settings. - Users note that **UI improvements** are needed for easier access to advanced settings and configurations in ILUM. - Users find some aspects of ILUM's setup **complex and unintuitive** , requiring considerable effort to configure effectively. #### What Are Recent G2 Reviews of ILUM? **"[From Hadoop to K8s with lower TCO](https://www.g2.com/survey_responses/ilum-review-11862422)"** **Rating:** 5.0/5.0 stars *— Mark D.* [Read full review](https://www.g2.com/survey_responses/ilum-review-11862422) --- **"[Seamless Integration and Unified Features for Advanced Users](https://www.g2.com/survey_responses/ilum-review-11904273)"** **Rating:** 5.0/5.0 stars *— Jan L.* [Read full review](https://www.g2.com/survey_responses/ilum-review-11904273) --- ### 13. [Rubrik](https://www.g2.com/products/rubrik/reviews) 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:** 277 **How Do G2 Users Rate Rubrik?** - **Ease of Use:** 9.1/10 (Category avg: 8.7/10) - **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](https://www.g2.com/sellers/rubrik) - **Company Website:** https://www.rubrik.com - **Year Founded:** 2014 - **HQ Location:** Palo Alto, California - **Twitter:** @rubrikInc (44,021 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/4840301/ (5,110 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** System Administrator - **Top Industries:** Information Technology and Services, Hospital & Health Care - **Company Size:** 49% Enterprise, 34% Mid-Market #### What Are Rubrik's Pros and Cons? **Pros:** - Ease of Use (49 reviews) - Backup Solutions (32 reviews) - Reliability (28 reviews) - Backup Efficiency (26 reviews) - User Interface (26 reviews) **Cons:** - Expensive (17 reviews) - Limited Features (11 reviews) - Complexity (8 reviews) - Cost Management (8 reviews) - Missing Features (8 reviews) ### What Do G2 Reviewers Say About Rubrik? *AI-generated summary from verified user reviews* **Pros:** - Users find Rubrik's **ease of use** and intuitive setup significantly streamline backup and recovery management. - Users value how Rubrik’s backup solutions provide **ease of implementation** and efficient management without impacting applications. - Users appreciate the **reliability** of Rubrik, ensuring dependable backups and robust data protection with ease of use. - Users appreciate the **backup efficiency** of Rubrik, enabling seamless server and database protection with minimal impact. - Users love Rubrik's **user-friendly interface** , making navigation and recovery processes efficient and straightforward. **Cons:** - Users find Rubrik to be **expensive** , yet acknowledge that its value often justifies the high cost. - Users find the **limited features** of Rubrik restricting their backup and reporting capabilities, impacting overall functionality. - Users find the **complexity of advanced features** and integrations with Microsoft 365 data somewhat overwhelming and cumbersome. - Users express concern over the **cost management** of Rubrik, particularly during yearly renewal processes. - Users find **missing features** like limited database support and inadequate search options in Rubrik, hindering functionality. #### What Are Recent G2 Reviews of Rubrik? **"[Reliable, hands-free backup that actually gives you peace of mind](https://www.g2.com/survey_responses/rubrik-review-13070838)"** **Rating:** 5.0/5.0 stars *— Shalu G.* [Read full review](https://www.g2.com/survey_responses/rubrik-review-13070838) --- **"[Evaluating Rubrik for Enterprise Data Protection](https://www.g2.com/survey_responses/rubrik-review-13103366)"** **Rating:** 4.5/5.0 stars *— Rayanne A.* [Read full review](https://www.g2.com/survey_responses/rubrik-review-13103366) --- #### What Are G2 Users Discussing About Rubrik? - [What operating system does rubrik use?](https://www.g2.com/discussions/what-operating-system-does-rubrik-use) - [How is rubrik different?](https://www.g2.com/discussions/how-is-rubrik-different) - [What makes rubrik unique?](https://www.g2.com/discussions/what-makes-rubrik-unique) - [What is rubrik used for?](https://www.g2.com/discussions/what-is-rubrik-used-for) ### 14. [Starburst](https://www.g2.com/products/starburst/reviews) 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:** 4.4/5.0 **Total Reviews:** 96 **How Do G2 Users Rate Starburst?** - **Ease of Use:** 9.0/10 (Category avg: 8.7/10) - **Data Governance:** 7.5/10 (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](https://www.g2.com/sellers/starburst) - **Company Website:** https://www.starburst.io/ - **Year Founded:** 2017 - **HQ Location:** Boston, MA - **Twitter:** @starburstdata (3,454 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/starburstdata/ (539 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Information Technology and Services, Financial Services - **Company Size:** 47% Enterprise, 32% Small-Business #### What Are Starburst's Pros and Cons? **Pros:** - Fast Querying (20 reviews) - Query Efficiency (18 reviews) - Integrations (17 reviews) - Ease of Use (15 reviews) - Performance (14 reviews) **Cons:** - Query Issues (12 reviews) - Complexity (11 reviews) - Learning Curve (10 reviews) - Slow Performance (10 reviews) - Performance Issues (9 reviews) ### What Do G2 Reviewers Say About Starburst? *AI-generated summary from verified user reviews* **Pros:** - Users highlight the **fast querying** capabilities of Starburst, enabling quick data access and analysis across various sources. - Users appreciate the **query efficiency** of Starburst, allowing quick access to diverse data sources effortlessly. - Users value the **seamless integration** with various data sources, significantly enhancing data access and analysis efficiency. - Users appreciate the **ease of use** of Starburst, enabling efficient data access and real-time analysis from various sources. - Users commend Starburst for its **superior performance** , rapidly retrieving accurate data and enhancing overall productivity. **Cons:** - Users often experience **query issues** with Starburst, finding it less efficient for complex queries compared to traditional SQL editors. - Users find the **initial setup complexity** of Starburst challenging, especially with configuration and optimizing queries. - Users note a **steep learning curve** when setting up Starburst, making onboarding and optimization challenging for new users. - Users often experience **slow performance** on Starburst, especially during peak usage and with complex queries. - Users report **performance issues** with Starburst, especially when executing complex queries, causing significant slowdowns. #### What Are Recent G2 Reviews of Starburst? **"[Starburst Enterprise Review](https://www.g2.com/survey_responses/starburst-review-10604384)"** **Rating:** 4.0/5.0 stars *— Rajiv B.* [Read full review](https://www.g2.com/survey_responses/starburst-review-10604384) --- **"[Federated SQL + Solid API Automation on Managed Elastic Compute](https://www.g2.com/survey_responses/starburst-review-13088399)"** **Rating:** 4.5/5.0 stars *— Verified User in Insurance* [Read full review](https://www.g2.com/survey_responses/starburst-review-13088399) --- #### What Are G2 Users Discussing About Starburst? - [What does Starburst do?](https://www.g2.com/discussions/what-does-starburst-do) - [What is Starburst Presto?](https://www.g2.com/discussions/what-is-starburst-presto) - [What is Starburst tech?](https://www.g2.com/discussions/what-is-starburst-tech) - [What does Starburst data do?](https://www.g2.com/discussions/what-does-starburst-data-do) ### 15. [Dremio](https://www.g2.com/products/dremio/reviews) 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: 8.7/10) - **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](https://www.g2.com/sellers/dremio) - **Year Founded:** 2015 - **HQ Location:** Santa Clara, California - **Twitter:** @dremio (5,112 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/dremio/ (370 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Financial Services, Information Technology and Services - **Company Size:** 49% Enterprise, 39% Mid-Market #### What Are Dremio's Pros and Cons? **Pros:** - Ease of Use (13 reviews) - Integrations (10 reviews) - Performance (7 reviews) - SQL Support (7 reviews) - Data Management (6 reviews) **Cons:** - Difficulty (5 reviews) - Poor Customer Support (5 reviews) - Learning Curve (4 reviews) - Difficult Setup (3 reviews) - Poor Documentation (3 reviews) ### What Do G2 Reviewers Say About Dremio? *AI-generated summary from verified user reviews* **Pros:** - Users find Dremio's **ease of use** exceptional, enabling quick data sharing and seamless integration with multiple tools. - Users appreciate the **seamless integrations** of Dremio with tools like Power BI and Tableau for efficient data management. - Users value the **exceptional performance** of Dremio for accelerating query speed and facilitating efficient data workflows. - Users value the **SQL support** in Dremio, enhancing data connectivity and integration with various analytics platforms. - Users appreciate the **advanced data management capabilities** of Dremio, enhancing data collection and analysis across various platforms. **Cons:** - Users find the **initial setup complicated** and experience a steep learning curve that hampers their productivity. - Users report that **customer support can be slow** , leading to delays in resolving issues and frustration. - Users note a **steep learning curve** with Dremio, making setup and feature understanding challenging for beginners. - Users find the **difficult setup** of Dremio challenging and time-consuming, often requiring external resources for assistance. - Users feel that the **documentation is poor** , often forcing them to seek help outside the provided resources. #### What Are Recent G2 Reviews of Dremio? **"[Flexible SQL for Handling Data from Many Sources](https://www.g2.com/survey_responses/dremio-review-12709566)"** **Rating:** 4.5/5.0 stars *— Tariel (Tato) B.* [Read full review](https://www.g2.com/survey_responses/dremio-review-12709566) --- **"[Evaluating Dremio for Enterprise Data Analytics](https://www.g2.com/survey_responses/dremio-review-13102983)"** **Rating:** 4.5/5.0 stars *— Guilherme A.* [Read full review](https://www.g2.com/survey_responses/dremio-review-13102983) --- #### What Are G2 Users Discussing About Dremio? - [What is Dremio used for?](https://www.g2.com/discussions/what-is-dremio-used-for) - [What is drill in Hadoop?](https://www.g2.com/discussions/what-is-drill-in-hadoop) - [What is a Dremio reflection?](https://www.g2.com/discussions/what-is-a-dremio-reflection) - 1 comment - [Is Dremio free?](https://www.g2.com/discussions/is-dremio-free) - [What is Dremio software?](https://www.g2.com/discussions/what-is-dremio-software) ### 16. [SAP Business Data Cloud](https://www.g2.com/products/sap-business-data-cloud/reviews) SAP Business Data Cloud is a fully managed software-as-a-service (SaaS) solution that unifies and governs SAP data and connects with third-party data. As an evolution of the company's data, planning, and analytics solutions, SAP Business Data Cloud brings together SAP Datasphere, SAP Analytics Cloud, and SAP Business Warehouse with a unified experience that delivers insights across all lines of business. In addition, SAP Databricks is natively available in Business Data Cloud - bringing the power of Databricks Data Intelligence Platform capabilities to the product. SAP Business Data Cloud connects data by leveraging business data fabric principles, making it easier to discover, share, govern, and model this data. It includes SAP Databricks as a first-party data service. The platform combines prebuilt applications and data products across all lines of business. It provides fully managed, curated data products across all lines of business and eliminate the costs of data extracts. Users can build on SAP’s curated data products with their domain expertise, and deliver Intelligent Applications through the Business Data Cloud ecosystem. These intelligent applications are adaptive, AI-powered applications that learn from your data, understand business context, and act on your behalf to transform business outcomes. **Average Rating:** 4.2/5.0 **Total Reviews:** 74 **How Do G2 Users Rate SAP Business Data Cloud?** - **Ease of Use:** 8.1/10 (Category avg: 8.7/10) **Who Is the Company Behind SAP Business Data Cloud?** - **Seller:** [SAP](https://www.g2.com/sellers/sap) - **Company Website:** https://www.sap.com/ - **Year Founded:** 1972 - **HQ Location:** Walldorf - **Twitter:** @SAP (297,052 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/sap/ (141,955 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 39% Enterprise, 29% Small-Business #### What Are SAP Business Data Cloud's Pros and Cons? **Pros:** - Ease of Use (32 reviews) - Features (32 reviews) - Integration Capabilities (31 reviews) - Data Discovery (30 reviews) - Integrations (27 reviews) **Cons:** - Complexity (30 reviews) - Difficult Learning (25 reviews) - Integration Issues (25 reviews) - Expensive (23 reviews) - Learning Curve (18 reviews) ### What Do G2 Reviewers Say About SAP Business Data Cloud? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of SAP Business Data Cloud, benefiting from streamlined access to trusted data. - Users value the **seamless data integration** and robust governance of SAP Business Data Cloud, enhancing organizational efficiency. - Users value the **integration capabilities** of SAP Business Data Cloud, effectively unifying data from diverse sources for enhanced efficiency. - Users value the **single, trusted view of data** offered by SAP Business Data Cloud, enhancing efficiency and organization. - Users value the **seamless integration** capabilities of SAP Business Data Cloud, enhancing data management and decision-making efficiency. **Cons:** - Users find the **setup complexity** of SAP Business Data Cloud daunting, especially for hybrid environments and data integration. - Users face a **difficult learning curve** due to the complexity and advanced features of SAP Business Data Cloud. - Users face **integration issues** with SAP Business Data Cloud, making setup and configuration challenging in hybrid environments. - Users highlight the **high costs** associated with SAP Business Data Cloud for large-scale implementations, affecting budget considerations. - Users note a **steep learning curve** , making it challenging for new users to navigate SAP Business Data Cloud effectively. #### What Are Recent G2 Reviews of SAP Business Data Cloud? **"[Unify SAP and non-SAP data](https://www.g2.com/survey_responses/sap-business-data-cloud-review-12851557)"** **Rating:** 4.0/5.0 stars *— Maria Francesca I.* [Read full review](https://www.g2.com/survey_responses/sap-business-data-cloud-review-12851557) --- **"[Centralized Data Management with User-Friendly UI](https://www.g2.com/survey_responses/sap-business-data-cloud-review-12609585)"** **Rating:** 4.0/5.0 stars *— Tejas Kumar V.* [Read full review](https://www.g2.com/survey_responses/sap-business-data-cloud-review-12609585) --- ### 17. [Rocket Vertica](https://www.g2.com/products/rocket-vertica/reviews) Vertica is the unified analytics platform, based on a massively scalable architecture with a broad set of analytical functions spanning event and time series, pattern matching, geospatial, and built-in machine learning capability. Vertica enables data analytics teams to easily apply these powerful functions to large and demanding analytical workloads, arming them and their customers with predictive business insights. Vertica provides a unified analytics platform across major public clouds and on-premises data centers, and integrates data in cloud object storage and HDFS without forcing any data movement. Available as a SaaS option, or as a customer-managed platform, Vertica helps teams combine growing data siloes for a more complete view of available data. Vertica features separation of compute and storage, so teams can spin up storage and compute resources as needed, then spin down afterwards to reduce costs. **Average Rating:** 4.3/5.0 **Total Reviews:** 195 **How Do G2 Users Rate Rocket Vertica?** - **Ease of Use:** 8.5/10 (Category avg: 8.7/10) - **Data Governance:** 8.3/10 (Category avg: 8.4/10) - **Data Security:** 8.5/10 (Category avg: 8.8/10) - **Scalability:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Rocket Vertica?** - **Seller:** [Rocket Software](https://www.g2.com/sellers/rocket-software) - **Year Founded:** 1990 - **HQ Location:** Waltham, MA - **Twitter:** @Rocket (3,532 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/10127/ (4,347 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Senior Software Engineer, Data Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 44% Enterprise, 39% Mid-Market #### What Are Recent G2 Reviews of Rocket Vertica? **"[Streamlining work flow with OpenText Vertica](https://www.g2.com/survey_responses/rocket-vertica-review-9248542)"** **Rating:** 4.0/5.0 stars *— mandi N.* [Read full review](https://www.g2.com/survey_responses/rocket-vertica-review-9248542) --- **"[I have almost 2 years of experience in data warehousing and ETL.](https://www.g2.com/survey_responses/rocket-vertica-review-6848692)"** **Rating:** 4.0/5.0 stars *— Abhishek K.* [Read full review](https://www.g2.com/survey_responses/rocket-vertica-review-6848692) --- #### What Are G2 Users Discussing About Rocket Vertica? - [What is Vertica used for?](https://www.g2.com/discussions/what-is-vertica-used-for) - [What is Micro Focus vertica?](https://www.g2.com/discussions/what-is-micro-focus-vertica) - [Is vertica open source?](https://www.g2.com/discussions/is-vertica-open-source) - 1 comment - [Is vertica a data warehouse?](https://www.g2.com/discussions/is-vertica-a-data-warehouse) - 1 comment - [Who uses Vertica?](https://www.g2.com/discussions/who-uses-vertica) ### 18. [Oracle Exadata Cloud Service](https://www.g2.com/products/oracle-exadata-cloud-service/reviews) Offer a fast, reliable, and cost-effective platform for data warehousing and business intelligence that is easy to scale to meet the complex reporting. **Average Rating:** 4.4/5.0 **Total Reviews:** 40 **How Do G2 Users Rate Oracle Exadata Cloud Service?** - **Ease of Use:** 7.7/10 (Category avg: 8.7/10) - **Data Governance:** 9.0/10 (Category avg: 8.4/10) - **Data Security:** 9.0/10 (Category avg: 8.8/10) - **Scalability:** 9.2/10 (Category avg: 8.5/10) **Who Is the Company Behind Oracle Exadata Cloud Service?** - **Seller:** [Oracle](https://www.g2.com/sellers/oracle) - **Year Founded:** 1977 - **HQ Location:** Austin, TX - **Twitter:** @Oracle (827,997 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1028/ (208,078 employees on LinkedIn®) - **Ownership:** NYSE:ORCL **Who Uses This Product?** - **Top Industries:** Banking, Government Administration - **Company Size:** 73% Enterprise, 23% Mid-Market #### What Are Recent G2 Reviews of Oracle Exadata Cloud Service? **"[Oracle exdata](https://www.g2.com/survey_responses/oracle-exadata-cloud-service-review-7650103)"** **Rating:** 5.0/5.0 stars *— Aslam M.* [Read full review](https://www.g2.com/survey_responses/oracle-exadata-cloud-service-review-7650103) --- **"[Very good product](https://www.g2.com/survey_responses/oracle-exadata-cloud-service-review-8635449)"** **Rating:** 5.0/5.0 stars *— sampath k.* [Read full review](https://www.g2.com/survey_responses/oracle-exadata-cloud-service-review-8635449) --- #### What Are G2 Users Discussing About Oracle Exadata Cloud Service? - [Which system configuration is offered by Oracle Exadata cloud services?](https://www.g2.com/discussions/which-system-configuration-is-offered-by-oracle-exadata-cloud-services) - [What are the advantages of Oracle Exadata?](https://www.g2.com/discussions/what-are-the-advantages-of-oracle-exadata) - [What are the features of Oracle cloud?](https://www.g2.com/discussions/oracle-exadata-cloud-service-what-are-the-features-of-oracle-cloud) - [What is Oracle database Exadata cloud service?](https://www.g2.com/discussions/what-is-oracle-database-exadata-cloud-service) ### 19. [IBM InfoSphere Information Server](https://www.g2.com/products/ibm-infosphere-information-server/reviews) Better understand your data and cleanse, monitor, transform and deliver it. Build confidence in your data Delivers clean, consistent and timely information for your data warehouses or big data projects and applications. Create a flexible governance strategy Helps you adapt a data governance strategy to suit your organizational objectives, while shaping business information in unique ways to meet your needs. Modernize and consolidate your systems Enables you to consolidate applications, retire outdated databases and modernize your infrastructure, as well as automate business processes for improved cost savings. Connect business and IT Provides a unified platform that enables collaboration, which can help you bridge the gap between business and IT and align objectives. **Average Rating:** 4.1/5.0 **Total Reviews:** 22 **How Do G2 Users Rate IBM InfoSphere Information Server?** - **Ease of Use:** 7.2/10 (Category avg: 8.7/10) - **Data Governance:** 8.3/10 (Category avg: 8.4/10) - **Data Security:** 8.3/10 (Category avg: 8.8/10) - **Scalability:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind IBM InfoSphere Information Server?** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Year Founded:** 1911 - **HQ Location:** Armonk, New York, United States - **Twitter:** @IBMSecurity (74,660 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (328,202 employees on LinkedIn®) - **Ownership:** SWX:IBM **Who Uses This Product?** - **Top Industries:** Financial Services, Information Technology and Services - **Company Size:** 96% Enterprise, 26% Mid-Market #### What Are Recent G2 Reviews of IBM InfoSphere Information Server? **"[IBM InfoSphere Information Server](https://www.g2.com/survey_responses/ibm-infosphere-information-server-review-8899456)"** **Rating:** 5.0/5.0 stars *— Verified User in Information Services* [Read full review](https://www.g2.com/survey_responses/ibm-infosphere-information-server-review-8899456) --- **"[Group lead Quality control](https://www.g2.com/survey_responses/ibm-infosphere-information-server-review-9165160)"** **Rating:** 5.0/5.0 stars *— Yeshwanth K.* [Read full review](https://www.g2.com/survey_responses/ibm-infosphere-information-server-review-9165160) --- #### What Are G2 Users Discussing About IBM InfoSphere Information Server? - [What is IBM InfoSphere Information Server used for?](https://www.g2.com/discussions/what-is-ibm-infosphere-information-server-used-for) ### 20. [Yellowbrick](https://www.g2.com/products/yellowbrick-data-yellowbrick/reviews) Yellowbrick is a high-performance, cloud-native data platform designed for hybrid multi-cloud and on-premises environments. It supports a wide variety of workloads, including traditional data warehousing, real-time streaming analytics, application analytics, and AI/ML workloads. Yellowbrick's architecture leverages the power of Kubernetes to deliver scalability, elasticity, and operational simplicity through SQL or a web interface, abstracting any user Kubernetes management. It provides unmatched speed and efficiency in SQL analytics, powered by the Direct Data Accelerator® and supports simultaneous querying and data loading with no impact on performance. **Average Rating:** 4.9/5.0 **Total Reviews:** 14 **How Do G2 Users Rate Yellowbrick?** - **Ease of Use:** 9.4/10 (Category avg: 8.7/10) - **Data Governance:** 9.4/10 (Category avg: 8.4/10) - **Data Security:** 9.4/10 (Category avg: 8.8/10) - **Scalability:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Yellowbrick?** - **Seller:** [Yellowbrick Data](https://www.g2.com/sellers/yellowbrick-data) - **Year Founded:** 2014 - **HQ Location:** Mountain View, US - **Twitter:** @YellowbrickData (6,853 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/yellowbrickdata (87 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 71% Enterprise, 29% Mid-Market #### What Are Recent G2 Reviews of Yellowbrick? **"[Excelent option to have a DWH engine with low cost and efficency](https://www.g2.com/survey_responses/yellowbrick-review-9791429)"** **Rating:** 5.0/5.0 stars *— Oswaldo V.* [Read full review](https://www.g2.com/survey_responses/yellowbrick-review-9791429) --- **"[An outstanding enterprise-class data platform](https://www.g2.com/survey_responses/yellowbrick-review-9820242)"** **Rating:** 5.0/5.0 stars *— Verified User in Consulting* [Read full review](https://www.g2.com/survey_responses/yellowbrick-review-9820242) --- #### What Are G2 Users Discussing About Yellowbrick? - [What is Yellowbrick used for?](https://www.g2.com/discussions/what-is-yellowbrick-used-for) - 1 comment ### 21. [SAP BW/4HANA](https://www.g2.com/products/sap-bw-4hana/reviews) SAP BW/4HANA is a next-generation data warehouse solution. It is specifically designed to use the advanced in-memory capabilities of the SAP HANA platform. For example, SAP BW/HANA can integrate many different data sources to provide a single, logical view of all the data. This could include data contained in SAP and non-SAP applications running on-premise or in the cloud, and data lakes, such as those contained in the Apache Hadoop open-source software framework. With SAP BW/4HANA, IT organizations can become the hero, providing business users with real-time analytics, tailored analytical applications, and intelligent automated support for business processes based on data from SAP and non-SAP line-of-business applications. **Average Rating:** 4.0/5.0 **Total Reviews:** 15 **How Do G2 Users Rate SAP BW/4HANA?** - **Ease of Use:** 7.9/10 (Category avg: 8.7/10) - **Data Governance:** 8.3/10 (Category avg: 8.4/10) - **Data Security:** 8.3/10 (Category avg: 8.8/10) - **Scalability:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind SAP BW/4HANA?** - **Seller:** [SAP](https://www.g2.com/sellers/sap) - **Year Founded:** 1972 - **HQ Location:** Walldorf - **Twitter:** @SAP (297,052 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/sap/ (141,955 employees on LinkedIn®) - **Ownership:** NYSE:SAP **Who Uses This Product?** - **Company Size:** 68% Enterprise, 16% Mid-Market #### What Are Recent G2 Reviews of SAP BW/4HANA? **"[SAP BW/4HANA - a great data warehouse for customers with SAP footprint on ERP side](https://www.g2.com/survey_responses/sap-bw-4hana-review-6818100)"** **Rating:** 4.5/5.0 stars *— Verified User in Information Technology and Services* [Read full review](https://www.g2.com/survey_responses/sap-bw-4hana-review-6818100) --- **"[SAP BW4/HANA: A Powerful Data Warehouse Platform with Some Drawbacks](https://www.g2.com/survey_responses/sap-bw-4hana-review-8625983)"** **Rating:** 4.5/5.0 stars *— Verified User in Automotive* [Read full review](https://www.g2.com/survey_responses/sap-bw-4hana-review-8625983) --- ### 22. [Zap Data Hub](https://www.g2.com/products/zap-data-hub/reviews) Zap Data Hub is a data warehouse automation solution that streamlines the extraction, loading and transformation (ELT) of ERP and business data into a centralized, governed warehouse for reporting and analytics. Zap Data Hub is used by finance, operations and IT teams who need a faster, more structured way to integrate ERP data from platforms such as Microsoft Dynamics 365, SAP Business One, Sage and SYSPRO alongside other business sources like CRM, payroll and inventory systems. It automates the heavy lifting involved in data integration and preparation, allowing businesses to build a trusted data foundation without extensive coding or manual processes. By automatically mapping, transforming and loading data into a warehouse, Zap eliminates reliance on spreadsheets, manual extracts and disconnected reporting. It creates a governed semantic model that ensures consistent metrics across tools like the Power BI integration, Excel add-in and browser-based reporting. Zap can be deployed in the cloud or on-premises, with support for Microsoft Fabric. Key features and value points • End-to-end data warehouse automation that structures and governs data from ERP and other business systems • Pre-built ERP connectors and models that accelerate deployment and reduce implementation effort • Governed semantic model that ensures consistent, trusted reporting across business units and analytics tools • Reporting support through the Excel add-in, Power BI integration and browser-based options • Deployment flexibility offering cloud-based or on-premises options • Future-ready architecture that integrates with Microsoft Fabric and supports evolving analytics needs Zap Data Hub is suited to organizations that want to automate their reporting data foundations, improve governance and drive business insights without the complexity of manual data engineering. **Average Rating:** 4.3/5.0 **Total Reviews:** 44 **How Do G2 Users Rate Zap Data Hub?** - **Ease of Use:** 8.0/10 (Category avg: 8.7/10) - **Data Governance:** 7.6/10 (Category avg: 8.4/10) - **Data Security:** 7.7/10 (Category avg: 8.8/10) - **Scalability:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Zap Data Hub?** - **Seller:** [ZAP](https://www.g2.com/sellers/zap) - **Year Founded:** 2001 - **HQ Location:** Brisbane, Australia - **Twitter:** @ZAP_Data (1,552 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/61528/ (91 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Oil & Energy, Computer Software - **Company Size:** 61% Mid-Market, 28% Enterprise #### What Are Zap Data Hub's Pros and Cons? **Pros:** - Ease of Use (9 reviews) - Integrations (8 reviews) - Customer Support (6 reviews) - Reporting (6 reviews) - Analytics (5 reviews) **Cons:** - Learning Curve (3 reviews) - Complexity (2 reviews) - Import Issues (2 reviews) - Limitations (2 reviews) - Steep Learning Curve (2 reviews) ### What Do G2 Reviewers Say About Zap Data Hub? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of Zap Data Hub, enjoying its intuitive interface and seamless implementation. - Users value the **easy integration of multiple data sources** , enhancing operational efficiency across teams. - Users commend the **excellent customer support** from the Zap Team, ensuring timely and effective assistance when needed. - Users value the **clean and seamless reporting functionality** of Zap Data Hub, enhancing efficiency and integration with ERP. - Users commend the **intuitive analytics** of Zap Data Hub, streamlining reporting and decision-making for rapid business growth. **Cons:** - Users find the **steep learning curve** challenging, requiring technical support for effective use of Zap Data Hub. - Users face **complexity with connections and calculations** in Zap Data Hub, which can hinder efficiency and user experience. - Users experience **import issues** with Excel sources, impacting connectivity and processing of large files in Zap Data Hub. - Users face **connection issues with Excel data sources** , making integration and report exporting more challenging. - Users note a **steep learning curve** with Zap Data Hub, making it challenging for occasional users to excel. #### What Are Recent G2 Reviews of Zap Data Hub? **"[Slick, Fast, and Intuitive BI Tool with Outstanding Support](https://www.g2.com/survey_responses/zap-data-hub-review-11984060)"** **Rating:** 5.0/5.0 stars *— Verified User in Gambling & Casinos* [Read full review](https://www.g2.com/survey_responses/zap-data-hub-review-11984060) --- **"[Powerful BI Reporting That Delivers Results](https://www.g2.com/survey_responses/zap-data-hub-review-11960396)"** **Rating:** 4.0/5.0 stars *— chris.hilton@agroliquid.com H.* [Read full review](https://www.g2.com/survey_responses/zap-data-hub-review-11960396) --- ### 23. [Hive](https://www.g2.com/products/hive/reviews) Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. At the same time this language also allows traditional map/reduce programmers to plug in their custom mappers and reducers when it is inconvenient or inefficient to express this logic in HiveQL. **Average Rating:** 4.2/5.0 **Total Reviews:** 57 **How Do G2 Users Rate Hive?** - **Ease of Use:** 8.4/10 (Category avg: 8.7/10) - **Data Governance:** 9.0/10 (Category avg: 8.4/10) - **Data Security:** 8.8/10 (Category avg: 8.8/10) - **Scalability:** 7.9/10 (Category avg: 8.5/10) **Who Is the Company Behind Hive?** - **Seller:** [The Apache Software Foundation](https://www.g2.com/sellers/the-apache-software-foundation) - **Year Founded:** 1999 - **HQ Location:** Wakefield, MA - **Twitter:** @TheASF (66,168 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/215982/ (2,470 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Engineer - **Top Industries:** Internet, Computer Software - **Company Size:** 55% Enterprise, 35% Mid-Market #### What Are Recent G2 Reviews of Hive? **"[Robust app intended to reflect how you work day to day](https://www.g2.com/survey_responses/hive-review-8355428)"** **Rating:** 4.5/5.0 stars *— Ruth P.* [Read full review](https://www.g2.com/survey_responses/hive-review-8355428) --- **"[User friendly and great tool for executing projects](https://www.g2.com/survey_responses/hive-review-9543871)"** **Rating:** 5.0/5.0 stars *— Nishmitha G.* [Read full review](https://www.g2.com/survey_responses/hive-review-9543871) --- #### What Are G2 Users Discussing About Hive? - [What is Hive workflow?](https://www.g2.com/discussions/what-is-hive-workflow) - [What is the Hive platform?](https://www.g2.com/discussions/what-is-the-hive-platform) - [How do I use Hive software?](https://www.g2.com/discussions/how-do-i-use-hive-software) - [What does hive software do?](https://www.g2.com/discussions/what-does-hive-software-do) ## What Is Data Warehouse Solutions? [IT Infrastructure Software](https://www.g2.com/categories/it-infrastructure) ## What Software Categories Are Similar to Data Warehouse Solutions? - [Data Science and Machine Learning Platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms) - [Big Data Analytics Software](https://www.g2.com/categories/big-data-analytics) - [Big Data Processing And Distribution Systems](https://www.g2.com/categories/big-data-processing-and-distribution) - [ETL Tools](https://www.g2.com/categories/etl-tools) - [Big Data Integration Platforms](https://www.g2.com/categories/big-data-integration-platforms) - [Data Governance Tools](https://www.g2.com/categories/data-governance-tools) - [DataOps Platforms](https://www.g2.com/categories/dataops-platforms) --- ## How Do You Choose the Right Data Warehouse Solutions? ### What You Should Know About Data Warehouse Solutions ### What are Data Warehouse Solutions? Data warehouse technology is used as a storage mechanism that pulls data from multiple disparate data sources into one single data store in an organized and efficient way to enable analytics and reporting for better decision-making. It is different from traditional database technology which is only capable of recording data. Data warehouse solutions are designed with integration and analysis in mind; and not like other databases that are designed to be queried in a variety of ways. This helps users without knowledge of SQL or other common querying languages to extract information from storage. A data warehouse acts as a single data repository that is an analytical and reporting database used to store historical data pulled from various disparate data sources. It also enables data retrieval through complex queries using online analytical processing (OLAP). Most data warehouse technology comes with features for data cleansing and normalization, so data can be stored in a variety of forms. This allows data from sales, marketing, research, and other departments to be stored in their natural forms but cleansed for comparative analysis. #### What Types of Data Warehouse Solutions Exist? Data warehouse solutions enable users to gain critical insights into their data through improved seamless self-service business intelligence (BI) capabilities. Though the purpose of the software remains the same, it differs in the mode of deployment and architecture. A data warehouse solution can be deployed both on the cloud and on-premises.  **Cloud data warehouse ** With cloud data warehouses, businesses can scale horizontally to hold increased storage and compute requirements. A data warehouse deployed on the cloud provides an improved infrastructure that lets companies focus more on delivering better and faster insights rather than managing a full house of servers on premises. These solutions provide cost control as organizations pay for what they use. **On-premises or license data warehouse ** An on-premises data warehouse software lets organizations buy one time, deploy in-house, and enable control over their hardware and software infrastructure. This deployment solution requires a consultant to help with installation and ongoing support. One advantage of on-premises data warehouse solutions is that it gives complete control and access over the data within an organization, helping minimize security risks. ### What are the Common Features of Data Warehouse Solutions? Data warehouses help organizations execute an effective data strategy, they feed structured and standardized data into BI tools which provide data professionals with high-level insights for decision-making. The following are some core features of data warehouse software:  **Data source connections:** Data warehouses typically rely on a range of data sources. The data can come from disparate sources, such as spreadsheets, banking systems, and software that ranges from SQL servers and relational databases to legacy systems. This feature helps users pull data that they hope to use during the decision-making process. **Data mart:** Data warehouses are organized into individual subsections. These segmented storage locations within the data warehouse are typically relevant to an individual team or department. Data warehouse solutions enable users to create data marts within them. **Scaling:** Scaling allows the data warehouse to expand storage capacity and functionality while maintaining balanced workloads. This helps facilitate the growing demand for requests and expanding sets of information. **Autoscaling**** :** While many tools allow administrators to control scaling storage, autoscaling features help to reduce the manual aspects. This is done with automation tools or bots that scale services and data automatically or on demand. **Data sharing:** Data sharing features offer collaborative functionality for sharing queries and data sets. These can be edited or maintained between users and potentially sent to customers or business partners. **Data discovery**** :** Search tools provide the ability to search vast, global data sets to find relevant information. This allows users self-service access and navigation to multiple datasets. **Data modeling**** :** Data modeling tools help users structure and edit data in a manner that enables quick and accurate insight extraction. They also help translate raw data into a more digestible format. **Compliance**** :** Compliance features monitor assets and enforce security policies. This also helps to audit assets to support compliance with personally identifiable information (PII), General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), and other regulatory standards. **Data staging:** Data staging areas are used to normalize and structure information. These transitional storage areas are often used during extract, transform, and load (ETL) processes where information is transformed, consolidated, aligned, and eventually exported. **Presentation tools:** Once data has been cleansed and normalized within the staging area, it will be transferred to data marts for access from users. They may be exported at that point or paired with BI tools for further visualization and data analysis. **Integration tools:** Integration tools are used both in the collection of information from its various data sources, as well as dispensing information after it has been normalized or modeled. These tools help facilitate the input of information and utilize the data being stored within a data warehouse **.** **Data transformation:** This feature enables functions like data cleansing, data deduplication, data validation, summarization, and more. Data transformation is needed to convert the data into a format that can be used by BI tools to extract actionable insights in a seamless manner. **Real-time** **analytics:** Real-time analytics features provide information in its most recent state and update users as soon as it changes. This will prevent the need to continually update data sets and simplifies the use of streaming data. Other features of data warehouse software: [AI/ML Integration](https://www.g2.com/categories/data-warehouse/f/ai-ml-integration) and [Data Lake Integrations](https://www.g2.com/categories/data-warehouse/f/data-lake-integration). ### What are the Benefits of Data Warehouse Solutions? Data warehouses pull data from multiple disparate sources across departments within an organization. This data flows from various CRM systems, financial systems, ERP software, and more in real time. They act as decision support systems that are designed to store historical data, further processed and transformed to make it available for decision makers to gain meaningful and valuable insights. These solutions provide a single source of truth for all the data within an organization to make data-driven decisions. **Improved BI:** Organizations majorly use data warehouses to support their analytics and BI requirements. Data warehouses facilitate centralized data storage in a quick and easy-to-access manner which further benefits BI implementations through effective analytics and better business decision making. Thus, these solutions help gain fast, accurate, and relevant insights into their data. **Increased return on investment (ROI):** Organizations achieve an increase in revenue due to cost savings. Deploying data warehouse solutions helps organizations consolidate data from multiple disparate sources in a specific high-quality format at one single repository, making it easily available to access and analyze better. Data warehousing solutions also help improve operational efficiency and productivity. **Provides competitive advantage:** Data within data warehouses is pulled from multiple disparate sources from within an organization and stored in a standardized format, ready to be analyzed. This allows quick and easy access to data and helps save a lot of time in deriving insights. They enable data professionals to identify and evaluate key threats and opportunities through effective business data analysis. **Improves operational workflow:** Data in a data warehouse is often transformed and cleaned before being loaded into it. This ensures that the data being used is good in quality and the insights generated from the data can be trusted to be accurate. This can improve the operational efficiency of businesses. ### Who Uses Data Warehouse Solutions? Data warehousing solutions focus on data relevant to business analytics and organize and optimize it to enable efficient analysis. This software provides an easy interface for business analysts. **Data analysts and data scientists:** These employees use data warehouses to get a centralized view of data across an organization to gain valuable insights in terms of being able to answer questions required for strategic decision making.  #### Software Related to Data Warehouse Solutions Related solutions that can be used together with data warehouses include: **Databases:** Databases consist of a large family of tools used to store information digitally. There are a wide variety of databases such as [relational databases software](https://www.g2.com/categories/relational-databases), [object-oriented databases software](https://www.g2.com/categories/object-oriented-databases), and [graph databases](https://www.g2.com/categories/graph-databases). They can be used to store virtually any kind of data set, depending on their nature, but vary greatly between one another. [ETL tools](https://www.g2.com/categories/etl-tools) **:** ETL is the most common way using which data is extracted from a data warehouse. These tools have long been used to facilitate the use of heterogeneous information sources and transform them into presentation-ready data formats. [Big data processing and distribution software](https://www.g2.com/categories/big-data-processing-and-distribution) **:** Big data processing and distribution software often work in tandem with data warehouses to process and distribute vast sums of information prior to storage. These tools help improve the warehouse’s scalability and processing power, which improves exploration compared to ETL tools. [Analytics platforms](https://www.g2.com/categories/analytics-platforms) **:** To implement an effective and efficient analytics system, companies require well-structured and designed data warehouses. Data warehouses can be explained as solutions for data integration which further enable reporting and analytics. Data warehouses are an essential component of analytics systems; therefore a poorly-designed data warehouse can lead to lower value from the insights generated and further impact business decision-making measures. Analytics tools are associated with data warehousing in the form of reporting and analysis of information. ### Challenges with Data Warehouse Solutions Software solutions can come with their own set of challenges. **On-premises data warehouse solutions:** On-premises data warehouse solutions require managing and maintenance of hardware and software infrastructure and services in-house. Organizations require dedicated teams to implement these solutions. On-premises data warehouses cannot upscale on demand. Thus, scaling up to meet changing requirements will move organizations to replace systems. **Data quality:** Data comes in data warehouses from multiple sources within organizations. Inconsistent data like duplicates, and missing information can lead to encountering errors. Poor or error-prone data quality can result in inaccurate reports and insights, which can lead to poor decision-making.   ### How to Buy Data Warehouse Solutions #### Requirements Gathering (RFI/RFP) for Data Warehouse Software If a company is just starting out and looking to purchase the first data warehouse solution, or maybe an organization needs to update a legacy system--wherever a business is in its buying process, g2.com can help select the best data warehouse software for the business. The particular business pain points might be related to unstructured and disparate data sources that must be analyzed well to use it for decision-making. If the company has amassed a lot of data, the need is to look for a solution that can help organize and structure that data to create a centralized view for analysis. Users should think about the pain points and jot them down; these should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees who will need to use this software, as this drives the number of licenses they are likely to buy. Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more. Depending on the scope of the deployment, it might be helpful to produce an RFI, a one-page list with a few bullet points describing what is needed from a data warehouse software. #### Compare Data Warehouse Solutions Products **Create a long list** From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor. **Create a short list** From the long list of vendors, it is helpful to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions. **Conduct demos** To ensure the comparison is thoroughgoing, the user should demo each solution on the shortlist with the same use case and datasets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition.  #### Selection of Data Warehouse Solutions **Choose a selection team** Before getting started, it's crucial to create a winning team that will work together throughout the entire process, from identifying pain points to implementation. The software selection team should consist of members of the organization who have the right interest, skills, and time to participate in this process. A good starting point is to aim for three to five people who fill roles such as the main decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator, or security administrator. In smaller companies, the vendor selection team may be smaller, with fewer participants multitasking and taking on more responsibilities. **Negotiation** Just because something is written on a company’s pricing page, does not mean it is gospel (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others. **Final decision** After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board. ### What Does Data Warehouse Solutions Cost? Data warehouse solutions are often sold as standalone products. They can be integrated with other BI and analytics tools. These typically come in two types of pricing models—flat rate and on demand._  _ ### Implementation of Data Warehouse Solutions **How are Data Warehouse Solutions Implemented?** An organization could either decide to buy a commercial data warehouse or build an in-house data warehouse. Either way requires proper planning in terms of architecture and aligning the data warehouse project to the company goals because the end purpose is to obtain valuable insights for business leaders for strategic decision-making. Data warehouse implementation can be done in the following ways: enterprise data warehouse, operational data store, and data mart. **Operational data store:** An operational database (ODS) is designed to handle current operational data. The insights derived from this data primarily support the improvement of operational processes. **Enterprise data warehouse (EDW):** This is a centralized data repository that collects enterprise data from multiple sources across the enterprise and makes it available for analysis to provide actionable insights. **Data mart:** It can be considered as a subset of a data warehouse. It is focused on a specific division of business like sales, marketing, and finance. Data marts deliver data in small sets or partitions to provide easy and efficient access. **Who is Responsible for Data Warehouse Solution Implementation?** The deployment of a data warehouse requires the participation of multiple stakeholders. Some of them are as follows: **C-suite executives:** These sets of people help users understand the long-term goals and strategies of an organization with regard to the data projects. They play a major role in scoping the data projects along with the project managers and the data team to help them understand what kind of data can be valuable to the organization for decision making.  **Project managers:** They are responsible for overseeing the overall project in terms of budget, schedules, deadlines, and project roadblocks. The project manager is assigned with the task to communicate the progress of the project to the senior management. **IT team:** These teams consist of business analysts, technical architects, ETL experts, and specialists. This team plays a role in supporting the data projects helping execute activities like developing the data warehouse, connecting data sources, executing ETL processes, and more. They may be required to support the system if it’s an on-premises deployment. **What Does the Implementation Process Look Like for Data Warehouse Solutions?** The implementation process of a data warehouse solution can be broken down into the following steps: **Gathering and defining requirements:** This step involves understanding the organization’s long-term business strategies and goals. It also covers various other criteria in terms of the kind of analysis and reporting required, as well as hardware, software, testing, implementation, and training of users. This step involves multiple stakeholders starting from the C-suite decisions, data, and analytics team, IT support, and the data governance team. **Data warehouse environment:** As the next step, users must decide which deployment model is suitable: on-premises, public or private cloud, or hybrid cloud. Public cloud is considered one of the least expensive models as the cloud provider takes care of managing and maintenance of the infrastructure hardware requirements. **Data modeling:** One of the crucial steps in data warehouse implementation is deciding on the data model. Every data source has a specific data scheme, picking up a single schema that is a fit for all is required.  **Connecting data sources through ETL process:** This step includes data extraction from multiple disparate sources, transforming it through converting the data from the source schema to the assigned destination schema and further loading it into the data warehouses. Transformation of the data also includes a couple of other actions that can be performed on the dataset like validation, enrichment, and other data health measures. **Integration to BI and analytics tools:** Once a data warehouse system is set up, the next step involves integrating the BI tool being used by the organization with the warehouse data. This facilitates reporting and analytics which leads to delivering faster and easy insights for better decision making. **Testing and validating the system:** This step includes the end-to-end testing of the entire data warehouse system. The system can be tested on various sets of parameters like data quality and integrity checks, the performance of the system, and analyzing whether it fulfills the end-user requirements in terms of reporting and analytics. ### Data Warehouse Solutions Trends **Shifting to cloud data warehousing solutions** Organizations are increasingly adopting cloud data warehouses to achieve improved scalability and performance. This shift helps them focus more on managing their business activities than managing a server block. Cloud data warehouse solutions also let organizations access easy real-time data from multiple sources, enabling them to gain better insights quickly. Companies can also achieve cost-effectiveness with data warehouses deployed on the cloud because it’s less expensive to scale a cloud data warehouse than one deployed on-premises. Also, buyers end up paying for the resources that they use, which further improves operational efficiency. **Moving towards DWaaS** Organizations are moving towards data warehouse as a service (DWaaS) as it lets buyers take advantage of eliminating hardware and software procurement, configuration, and maintenance work as a third party is responsible for these. Starting from data warehouse administration to setting up a data warehouse team, the providers are responsible for it. --- ## What Are the Most Common Questions About Data Warehouse Solutions? ### What are the key features to look for in a Data Warehouse solution? Key features to look for in a Data Warehouse solution include scalability, which allows for handling increasing data volumes; robust security measures to protect sensitive information; real-time data processing capabilities for timely insights; user-friendly interfaces for ease of use; and strong integration options with various data sources. Additionally, support for advanced analytics and machine learning can enhance data utilization, while cost-effectiveness remains a crucial consideration for budget-conscious organizations. ### How do Data Warehouse pricing models typically work? Data Warehouse pricing models typically include subscription-based, pay-as-you-go, and tiered pricing structures. Subscription models often charge a monthly or annual fee based on storage capacity or user count, while pay-as-you-go allows users to pay for the actual resources consumed. Tiered pricing offers different levels of service at varying price points, catering to different business needs. For instance, products like Snowflake and Amazon Redshift are noted for their flexible pricing options, allowing businesses to scale costs according to usage. ### What integrations should I consider for my Data Warehouse? When considering integrations for your Data Warehouse, prioritize those that enhance data ingestion, transformation, and visualization. Key integrations to explore include Amazon Redshift, Snowflake, Google BigQuery, and Microsoft Azure Synapse Analytics. Users frequently highlight the importance of seamless connections with ETL tools like Talend and Apache NiFi, as well as BI tools such as Tableau and Looker, which facilitate effective data analysis and reporting. Additionally, consider integration capabilities with cloud storage solutions like AWS S3 and Google Cloud Storage for efficient data management. ### How scalable are most Data Warehouse solutions for growing businesses? Most Data Warehouse solutions are highly scalable, with products like Snowflake, Amazon Redshift, and Google BigQuery receiving positive feedback for their ability to handle increasing data volumes and user loads. Users report that Snowflake excels in elasticity, allowing businesses to scale compute and storage independently. Amazon Redshift is noted for its robust performance in scaling for large datasets, while Google BigQuery is praised for its serverless architecture, enabling seamless scaling without infrastructure management. Overall, these solutions are well-suited for growing businesses needing flexible and scalable data management. ### What are common use cases for Data Warehouses in different industries? Common use cases for data warehouses across industries include retail for customer behavior analysis, finance for risk management and compliance reporting, healthcare for patient data integration and analytics, and manufacturing for supply chain optimization. Users frequently highlight platforms like Snowflake, Amazon Redshift, Google BigQuery, and Microsoft Azure Synapse Analytics for their scalability and performance in handling large datasets, enabling real-time insights and reporting capabilities tailored to industry-specific needs. ### How does user experience vary across different Data Warehouse platforms? User experience across different Data Warehouse platforms varies significantly. For instance, Snowflake users rate ease of use at 8.9/10, highlighting its intuitive interface, while Amazon Redshift scores 8.2/10, with some users noting a steeper learning curve. Google BigQuery receives an 8.5/10 for its performance and scalability, but users mention challenges with complex queries. Microsoft Azure Synapse Analytics has a user satisfaction score of 8.0/10, with feedback indicating a need for better documentation. Overall, Snowflake leads in user experience, followed by BigQuery and Redshift. ### What level of customer support is standard for Data Warehouse providers? Standard customer support for Data Warehouse providers typically includes 24/7 availability, with most vendors offering multiple channels such as email, phone, and live chat. For instance, Snowflake and Amazon Redshift are noted for their responsive support teams, while Google BigQuery users highlight the availability of extensive documentation and community forums. Additionally, many providers offer dedicated account management for enterprise clients, ensuring tailored support. Overall, user reviews indicate that the quality of customer support can significantly influence satisfaction, with many users valuing prompt and knowledgeable assistance. ### How do Data Warehouses handle data security and compliance requirements? Data Warehouses prioritize data security and compliance through features like encryption, access controls, and audit logs. For instance, Snowflake offers robust security measures including end-to-end encryption and role-based access control, while Amazon Redshift provides compliance with standards such as HIPAA and PCI DSS. Google BigQuery emphasizes data governance with fine-grained access controls and data masking capabilities. Users frequently highlight the importance of these security features in their reviews, indicating that compliance with regulations is a critical factor in their selection process. ### What are the typical implementation timelines for Data Warehouse solutions? Implementation timelines for Data Warehouse solutions typically range from 3 to 6 months, depending on the complexity and scale of the deployment. For instance, products like Snowflake and Amazon Redshift often report shorter timelines due to their cloud-native architectures, while more traditional solutions like Microsoft SQL Server may take longer due to on-premises setup requirements. User feedback indicates that factors such as data migration, integration with existing systems, and team expertise significantly influence these timelines. ### How do Data Warehouses differ in performance and speed? Data warehouses differ in performance and speed primarily based on architecture, data processing capabilities, and scalability. For instance, Snowflake is noted for its high concurrency and automatic scaling, which enhances performance during peak loads. Amazon Redshift offers fast query performance through columnar storage and parallel processing, while Google BigQuery excels in handling large datasets with its serverless architecture, allowing for rapid data analysis. Users often report that these features significantly impact their data retrieval speeds and overall efficiency, with Snowflake receiving high ratings for performance consistency. ### What are the most common challenges faced during Data Warehouse implementation? Common challenges during Data Warehouse implementation include data integration issues, with 45% of users citing difficulties in consolidating data from various sources. Additionally, 38% report performance problems, particularly with query speed and data processing. User training and change management are also significant hurdles, affecting 32% of implementations, as teams struggle to adapt to new systems. Lastly, 29% of users mention high costs associated with setup and maintenance as a critical challenge. ### How can I evaluate the ROI of a Data Warehouse investment? To evaluate the ROI of a Data Warehouse investment, consider factors such as improved data accessibility, enhanced decision-making speed, and cost savings from operational efficiencies. User reviews highlight that platforms like Snowflake and Amazon Redshift significantly reduce data retrieval times, leading to faster insights. Additionally, users report that effective data integration capabilities in tools like Google BigQuery and Microsoft Azure Synapse Analytics contribute to reduced manual reporting efforts, translating to labor cost savings. Assessing these benefits against the total cost of ownership will provide a clearer ROI picture.