Founded in 2010, Datagaps is a US-based enterprise software company headquartered in Herndon,VA. Datagaps is the only vendor recognized by Gartner in BOTH the DataOps Tools market guide AND the Data Observability market guide — a distinction no other data testing platform holds.
WHAT WE DO
Datagaps makes data trustworthy across the entire pipeline — from data ingestion and ETL/ELT transformations, through data quality monitoring, to BI dashboard validation and AI model input testing — on a single, unified DataOps Suite platform.
THE PROBLEM WE SOLVE
Enterprise data teams lose an average of $15M annually to poor data quality (Source: Gartner). 70% of data migration failures surface post go-live. 59% of organizations cannot quantify their own BI consistency gap. Datagaps eliminates these risks with automated, end-to-end validation that detects issues before they reach production.
HOW WE'RE DIFFERENT
Unlike point tools that test one stage, Datagaps covers every layer — ETL pipelines, data quality and consistency, semantic validation, BI dashboards, and AI/ML inputs — from a single platform with shared rules, one audit trail, and one vendor. Agentic AI auto-generates tests, self-heals schema drift, summarizes report differences, and recommends smart quality rules.
OUTCOMES DELIVERED
500B+ Records validated across ETL & cloud pipelines
10M+ Automated test cases run with zero manual scripting
80% Faster test cycles vs. manual testing approach
60% Reduction in data errors detected before production
70% Reduction in ETL validation spend
200+ Native data source connectors
Trusted by 100+ enterprise customers across Financial Services, Healthcare, Life Sciences, FMCG, Retail, Higher Education
PRODUCTS AND PLATFORM
• DataOps Suite — Unified platform for end to end data validation and analytics testing powered by Agentic AI
• ETL Validator — Automated ETL/ELT testing with Agentic AI; 200+ native connectors
• BI Validator — Automated BI report validation for Power BI, Tableau, Oracle Analytics
• Data Quality Monitor — Proactive data quality with Agentic AI: Predict, Prevent, Govern
• Test Data Manager — Compliant and realistic test data generation with Agentic AI
TECHNICAL CREDENTIALS
SOC 2 Type II | US Patent (ELV architecture) | Informatica Certified | Embedded LLM (data stays in your environment) | Gartner DataOps Tools Guide | Gartner Data Observability Guide
INTEGRATIONS
200+ Snowflake, Databricks, Azure Synapse, Amazon Redshift, Google BigQuery, Salesforce, Microsoft Power BI, Tableau, Oracle Analytics, Informatica, Talend, SSIS, dbt
Average Rating: 4.5/5.0
Total Reviews: 10
How Do G2 Users Rate Datagaps DataOps Suite?
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Reliability: 8.9/10 (Category avg: 8.7/10)
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Thoroughness: 7.9/10 (Category avg: 8.7/10)
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Test Feedback: 8.9/10 (Category avg: 8.8/10)
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Has the product been a good partner in doing business?: 9.6/10 (Category avg: 9.2/10)
Who Is the Company Behind Datagaps DataOps Suite?
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Seller: Datagaps
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Year Founded: 2010
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HQ Location: Herndon, US
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Twitter: @datagaps
49 Twitter followers
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LinkedIn® Page: www.linkedin.com
123 employees on LinkedIn®
Who Uses This Product?
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Company Size: 64% Large, 18% Medium
What Do G2 Reviewers Say About Datagaps DataOps Suite?
AI-generated summary from verified user reviews
Pros
- Users value the ease of use and implementation of Datagaps DataOps Suite, making it accessible for all experience levels.
- Users appreciate the automation capabilities of Datagaps DataOps Suite, enhancing data quality and operational efficiency.
- Users value the improved data quality from Datagaps DataOps Suite, enhancing analytics and supporting data-driven decisions.
- Users value the easy integrations in Datagaps DataOps Suite, enhancing flexibility and streamlining data processing across platforms.
- Users appreciate the extensive features of Datagaps DataOps Suite, enhancing flexibility and ease of use in data management.
Cons
- Users find the initial setup complex, which can be time-consuming, but value the ongoing benefits after configuration.
- Users face dependency issues as the lack of automated job cleanup complicates workflow efficiency and data management.
- Users find the difficult setup process of Datagaps DataOps Suite time-consuming, despite its effective performance afterwards.
- Users find the lack of automation frustrating, requiring manual cleanup of long-running jobs, impacting efficiency.
- Users face a significant learning curve when transitioning to DataOps, especially with the shift from manual to automated processes.
