iceDQ : AI-Enabled Data Testing Platform

Data errors in pipelines, reports, and APIs silently compound if not caught before deployment into production.

iceDQ gives data and QA teams a single platform to automate data validation, reconciliation, regression, and test management across every layer of their data stack, with 150+ connectors spanning databases, cloud warehouses, flat files, APIs, and BI tools.

• Automate data validation across every stage of your ETL pipeline ensuring data meets quality standards before it reaches production.
• Reconcile source and target datasets at full volume across 150+ connectors ensuring no data loss or corruption across your ETL pipeline.
• Rerun saved workflows of data testing rules after every pipeline change, release, or data load to instantly identify passing and failing rules.
• Embed data testing as a native stage in your DataOps pipeline, ensuring data quality gates are enforced automatically before every deployment.
• Connect data testing outcomes to the tools your QA and engineering teams already use, eliminating the gap between data testing and application testing workflows.
• Utilize iceDQ’s MCP server to auto generate test cases and execute rules at scale.

Built for the modern data stack, iceDQ deploys directly on Azure and connects natively to Azure Synapse, Azure Data Lake, Azure SQL, Microsoft Fabric, Cosmos DB, Power BI, and 150+ other platforms including Snowflake, Databricks, Teradata, SAP, Salesforce, etc. — so you can test across your entire pipeline, not just one layer.

Use Cases:

• ETL/Data Pipeline Testing
• Data Migration Testing
• BI Report and Dashboard Testing
• File Testing
• Big Data Testing
• Data Lake Testing
• API Data Testing

Challenges We Solve:

• Ensure 100% Test Coverage with Full Volume Data: iceDQ automatically generates and runs rules across every table, column, and row, enabling full-volume reconciliation and validation that customers report as achieving 100% test coverage on datasets with billions of records.
• Eliminate manual test/rule development and execution: iceDQ auto-generates validation, reconciliation, and checksum rules across tables and columns with minimal setup, removing the need for manual scripting or test-case authoring.
• Accelerate regression and release cycles: By reusing standardized test cases across environments, iceDQ speeds up regression testing cycles so releases move faster with less rework.
• Re-use tests/rules across platforms and environments seamlessly: Rules built once in iceDQ can be reused across Dev, QA, and Production, and across heterogeneous platforms (databases, files, cloud, big data), standardizing testing enterprise-wide.
• Orchestrate and integrate testing into your CI/CD and DevOps processes: iceDQ integrates with test-case management and ticketing systems and supports scheduling/workflow orchestration, enabling DataOps and embedding data testing directly into CI/CD pipelines.
• Pinpoint data defects at the row & column level, reduce MTTD and MTTR: iceDQ's exception reports drill down to the exact row and column where a discrepancy occurs, cutting Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR) data defects.

Benefits of iceDQ

• Reduce cost of test and rule creation by 75%+: Test creation automation with UI, AI, MCP Server. No scripting required.
• Eliminate cost of manual execution: 100% test execution automation with APIs and scheduled task orchestration.
• Cut time to release and testing cycles by 50%+: Accelerate project delivery with workflows, rule reusability and creation/execution automation along with integration into test case management systems.
• Ensure 100% test coverage and defect prevention: Certify data, ETL, pipelines and data migration projects by ensuring full test and data coverage before releasing them into production.