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Datagaps

Datagaps is an AI-powered DataOps platform for automated data testing, validation, and quality monitoring - trusted by Fortune 500 companies across healthcare, financial services, life sciences, higher education and retail. The DataOps Suite includes ETL Validator, BI Validator, Data Quality Monitor, and Test Data Manager. Recognized as a Specialist in Data Pipeline Test Automation by Gartner. Supports 200+ data sources across Snowflake, Databricks, Azure Synapse, AWS Redshift, Google BigQuery,

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Authority
46 reach 5 · trust 5 · quality 21 · freshness 15
Tier
listed
llms.txt
https://datagaps.com/llms.txt
Sections
20
Links
114
Size
37540 bytes
Popularity rank
#797228
Registered
2012-02-12
Country
— Publisher location unknown; CDN address excluded
Country evidence
No published address evidence collected yet
Categories
Data and analytics
For agents
Documentation: developer or product docs worth reading, nothing to call
First seen
2026-10-19T10:44:53.627Z
Last checked
2026-10-21T16:27:26.694Z
Badge
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Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-21T16:27:23.264Z. Cached copy · Source on datagaps.com

# Datagaps

Datagaps is an AI-powered DataOps platform for automated data testing, validation, and quality monitoring - trusted by Fortune 500 companies across healthcare, financial services, life sciences, higher education and retail. The DataOps Suite includes ETL Validator, BI Validator, Data Quality Monitor, and Test Data Manager. Recognized as a Specialist in Data Pipeline Test Automation by Gartner. Supports 200+ data sources across Snowflake, Databricks, Azure Synapse, AWS Redshift, Google BigQuery, Power BI, Tableau, Oracle Analytics, Salesforce, and Collibra. Free 14-day trial available. No credit card required. Founded 2010. Headquartered in Herndon, Virginia. Serving 100+ enterprise customers globally, with documented outcomes including 500B+ records validated across ETL and cloud pipelines, 10M+ automated test cases run with zero manual scripting, 80% faster test cycles vs. manual testing, 60% reduction in data errors detected before production, and 70% reduction in ETL validation spend.

## Homepage

- [Datagaps | Gen AI-Powered Automated Cloud Data Testing](https://www.datagaps.com/): Intelligent data validation and analytics testing platform with Agentic AI - combines ETL Validator, BI Validator, Data Quality Monitor, and Test Data Manager in one unified DataOps Suite. Recognized by Gartner. Trusted by 100+ Leading Enterprises.

## Product Pages

- [DataOps Suite | End-to-End Data Validation Platform](https://www.datagaps.com/dataops-suite/): Agentic AI-powered, end-to-end data validation platform unifying ETL Validator, BI Validator, Data Quality Monitor, and Test Data Manager for continuous validation, adaptive reconciliation, and reliable insights — delivering 80% faster test cycles, 70% reduction in ETL validation spend, and 60% reduction in data errors across customers like NYU, ZS Associates, and Broadcom.
- [ETL Validator](https://www.datagaps.com/data-validation-etl-testing-tools/): Automate ETL and data pipeline testing with Datagaps ETL Validator - validate transformations, source-to-target reconciliation, and schema accuracy across 200+ data sources. No sampling. Full record coverage.
- [BI Validator](https://www.datagaps.com/bi-testing-tools/): Automate BI report and dashboard testing with Datagaps BI Validator - validates Power BI, Tableau, and Oracle Analytics reports against SQL source data. No-code. No sampling. 100% dashboard coverage.
- [Data Quality Monitor](https://www.datagaps.com/data-quality-monitoring-tools/): AI Agent-driven data quality platform for continuous monitoring, rule-based validation, ML-powered anomaly detection, and data profiling across pipelines. Recognized by Gartner, with integrations into Unity Catalog and Collibra.
- [Test Data Manager](https://www.datagaps.com/test-data-manager/): Compliant and realistic test data generation with Agentic AI - creates synthetic test data with PII masking, data subsetting, and referential integrity for safe, realistic testing across all environments.
- [Da
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