Datagaps DataOps Suite
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?
- Reliability: 8.9/10 (Category avg: 8.7/10)
- Thoroughness: 7.9/10 (Category avg: 8.7/10)
- Test Feedback: 8.9/10 (Category avg: 8.8/10)
- 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?
- Seller: Datagaps
- Year Founded: 2010
- HQ Location: Herndon, US
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Twitter: @datagaps
49 Twitter followers -
LinkedIn® Page: www.linkedin.com
123 employees on LinkedIn®
Who Uses This Product?
- 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.
What Are Recent G2 Reviews of Datagaps DataOps Suite?
"Strong QA automation, but Git Sync needs work"
Rating: 4.0/5.0 stars
— Verified User in Hospital & Health Care
"User-Friendly Experience with Great Support and Smooth Integration"
Rating: 4.0/5.0 stars
— Gajendra C.

