Automate Data Testing with iceDQ

Test Data Centric Projects at Scale

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Data errors in pipelines, reports, and APIs silently compound into costly decisions. 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 200+ connectors spanning databases, cloud warehouses, flat files, APIs, and BI tools.

ETL Data validation Testing

Automate data validation across every stage of your ETL pipeline ensuring data meets quality standards before it reaches production.

  • Completeness Testing:Detect unexpected null, blank, or missing values in critical fields before data reaches downstream consumers.
  • Format and Pattern Validation: Enforce data type, format, and pattern rules across source and target to catch structural mismatches early.
  • Range and Threshold Checks: Validate numeric and date values fall within acceptable business-defined boundaries.
  • Business Rule Validation: Enforce domain-specific rules such as value ranges, mandatory fields, and data classifications to ensure data integrity
  • Referential Integrity Testing: Validate that foreign key values in a dataset exist in the referenced master or lookup table, ensuring no orphan records.

ETL Data validation Testing

Automate data validation across every stage of your ETL pipeline ensuring data meets quality standards before it reaches production.

  • Completeness Testing:Detect unexpected null, blank, or missing values in critical fields before data reaches downstream consumers.
  • Format and Pattern Validation: Enforce data type, format, and pattern rules across source and target to catch structural mismatches early.
  • Range and Threshold Checks: Validate numeric and date values fall within acceptable business-defined boundaries.
  • Business Rule Validation: Enforce domain-specific rules such as value ranges, mandatory fields, and data classifications to ensure data integrity
  • Referential Integrity Testing: Validate that foreign key values in a dataset exist in the referenced master or lookup table, ensuring no orphan records.

ETL Data Reconciliation Testing

Reconcile source and target datasets at full volume across 200 + connectors ensuring no data loss or corruption across your ETL pipeline.

  • Source to Target Reconciliation: Compare data across heterogeneous platforms and systems verifying record counts, column values, and schema alignment between source and target.
  • Transformation Testing: Verify row-level transformations such as calculations, aggregations, and conversions between source and target.
  • Checksum Reconciliation: Reconcile aggregate sums, counts, averages, min, and max values across platforms and systems.
  • Medallion Layer Reconciliation: Validate data consistency across bronze, silver, and gold layers ensuring accuracy and completeness at every transformation stage.

Regression Testing

Rerun saved workflows of data testing rules after every pipeline change, release, or data load to instantly identify what is passing and failing.

  • Workflow Based Test Suites: Group data testing rules of your choice into workflows aligned to a pipeline, domain, or release cycle and execute them together in a single run.
  • Orchestrate and Schedule: Orchestrate rules in a sequence across source, target, and medallion layers and schedule automated executions aligned to your pipeline or reporting cycle.
  • Regression on Pipeline Changes: Rerun existing workflows after pipeline code or transformation logic changes to detect unintended impact before issues reach downstream consumers.
Apply data checks on Power BI semantic and report layer data in iceDQ — validate business logic, formats, and calculations

CICD Integration

Embed data testing as a native stage in your DataOps pipeline, ensuring data quality gates are enforced automatically before every deployment.

  • DataOps Enablement: Trigger iceDQ workflows directly from any CI/CD platform including Jenkins, GitHub Actions, Azure DevOps, and more via out of the box REST API.
  • Exit Code Based Pipeline Control: iceDQ returns pass or fail exit codes after every workflow execution, automatically stopping pipeline progression when data quality thresholds are not met.
  • Environment Promotion Testing:Run data testing workflows across dev, QA, and production environments to validate data consistency at every stage before promoting a release.

Test Management Integration

Connect data testing outcomes to the tools your QA and engineering teams already use, eliminating the gap between data testing and application testing workflows

  • Automated Defect Tracking: Integrate iceDQ with any test management platform including Jira, Azure DevOps, and more to automatically create, update, and close defect tickets based on rule results.
  • Consolidated Test Reporting: Get a unified view of data testing results alongside application defects, giving QA teams a single pane of glass for all testing outcomes.
  • Audit Ready Test Evidence: Maintain a complete history of every test run, rule result, and workflow execution to support audit, compliance, and sign off requirements.

PRODUCT HIGHLIGHTS

Governance Risk and Compliance - iceDQ AI Agent: Triple Arrow - iceDQ Automatically generate data testing rules from ETL code, mapping documents, or plain English descriptions
Revenue - iceDQ Low code-no code: Triple Arrow - iceDQ Build data testing rules using pre-built checks for completeness, range, length, datatype, pattern, conditional logic, and more without writing it manually
Productivity - iceDQ Performance and scalability: Triple Arrow - iceDQ Reconcile millions of records in minutes, not hours, with iceDQ's in-memory rules engine and no restriction on the number of records or columns that can be processed.
Productivity - iceDQ 200+ connectors: Triple Arrow - iceDQ iceDQ natively supports reconciliation across files, API(s), databases, applications, BI reports, SAP, CRM, cloud vs on-premises, Kafka and many more
Productivity - iceDQ API First: Triple Arrow - iceDQ Invoke any iceDQ rule, workflow, or schedule programmatically via out of the box REST API
Productivity - iceDQ Exception reporting: Triple Arrow - iceDQ Every reconciliation run produces an exception report with mismatches at record and column level showing source value and target value for each discrepancy
Productivity - iceDQ Files & Semi structured data: Triple Arrow - iceDQ Test structured files such as CSV and Excel as well as semi structured formats including JSON, XML, and Parquet directly
Productivity - iceDQ Notifications: Triple Arrow - iceDQ Send automated alerts and notifications via email, Slack, Microsoft Teams, when rules or workflow fail or succeeds
Productivity - iceDQ Reporting & Dashboards: Triple Arrow - iceDQ Monitor data testing health across pipelines, domains, and environments through real time dashboards showing pass, fail, and trend metrics for every workflow execution.
Productivity - iceDQ UDF & Parameterization: Triple Arrow - iceDQ Extend iceDQ's testing capabilities with user defined functions and reusable parameters to handle complex business logic and dynamic test configurations at scale.
Productivity - iceDQ Advanced scripting: Triple Arrow - iceDQ Write custom test logic in Groovy or Java for complex scenarios that go beyond pre-built rule types.
ETL/Data Pipeline Testing
  • iceDQ automates testing of the ETL processes in QA environment before deploying them in production.
  • Certify the data processes written with Python, Informatica, Azure data factory, DBT, Talend or any other vendor platform.
  • Test without sampling on full volume of data in minutes with a highly iceDQ’s scalable engine.
  • Smart AI Agents, MCP server and Out of the box rule templates for testing various ETL patterns such as Type II dimensions, Referential Integrity, etc.
 Data Migration Testing
  • iceDQ with 200+ connectors can connect to on premise and cloud databases to systems.
  • Reconcile the initial load, incremental loads between legacy the migrated systems.
  • iceDQ certifies migrations at full volume spanning heterogeneous source and target.
  • iceDQ AI Agent bulk generates migration testing rules from mapping documents or natural language prompts, dramatically reducing the time to build test coverage across hundreds of tables and objects.
BI Report & Dashboard Testing
  • Provide trusted BI reports to business users by certifying their data by comparing source database.
  • Reconcile and compare reports with semantic layers, other reports, files or database.
  • iceDQ connects natively to Power BI via XMLA and Report connectors, Qlik Sense apps, Tableau, and other BI platforms to validate each data visualization.
  • Enjoy the power of smart AI agents to create and automate testing.
File Testing
  • iceDQ tests structured and semi structured files including CSV, Excel, JSON, XML, and Parquet.
  • Compare file data with staging database at full volume without pre-processing.
  • Use out of the box rule templates for completeness, format validation, duplicate detection, and referential integrity checks.
  • Employ AI Agents to automatically generate file testing rules from plain English descriptions, making it easy to build test coverage for any new file feed without engineering effort.
Big Data/ Lakehouse Testing
  • iceDQ tests Lakehouse with billions of records without relying on sampling or database compute.
  • iceDQ's in-memory engine validates data across bronze, silver, and gold layers at scale.
  • Test Databricks, Snowflake, and other platforms at full volume with no restriction on records or columns.
  • Utilize advanced matching including fuzzy compare and conditional compare handles complex transformation scenarios, while granular exception reports surface defects at record and column level across every medallion layer.
API Data Testing
  • Don’t forget to test data delivered by APIs for modern data pipelines as A single API returning incorrect, incomplete, or malformed data can silently corrupt every downstream system
  • iceDQ connects to REST APIs via out of the box connectors and validates the API data for structures, format and against business rules.
  • iceDQ can also reconcile API response data directly against source databases, files, or other APIs to ensure data consistency across every integration point.
  • Generate granular exception reports pinpoint exactly which API responses contain defects at record and field level, giving teams full visibility into data quality across every API driven pipeline before issues reach production.

Automate your iceDQ & Data Testing with iceDQ today

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FAQs: Power BI testing with iceDQ

What types of data sources does iceDQ support for data testing?

iceDQ supports 200+ connectors spanning databases, cloud warehouses, flat files, APIs, BI platforms, applications, SAP, CRM, Kafka, and more across cloud and on-premises environments.

Is iceDQ compatible with my existing ETL platform?

Yes. iceDQ is ETL tool agnostic and works with any ETL platform. It validates the data output produced by the ETL regardless of whether the pipeline is built on Informatica, Azure Data Factory, Talend, dbt, SSIS, or any other platform.

Can iceDQ test data across platforms?

Yes. iceDQ connects to on-premises systems like Oracle, SAP, and SQL Server as well as cloud platforms like Snowflake, Databricks, and Azure Synapse, enabling data testing across heterogeneous platforms within the same test execution.

Does iceDQ support full volume testing without sampling?

Yes. iceDQ runs full volume testing across every row and column without sampling, ensuring no defect goes undetected regardless of data volume.

Can iceDQ test structured and semi structured files?

Yes. iceDQ tests structured files including CSV and Excel as well as semi structured formats including JSON, XML, and Parquet directly without pre-processing or transformation.

Does iceDQ support API data testing?

Yes. iceDQ connects to REST APIs via out of the box connectors and validates API responses for completeness, business rule compliance, and reconciliation against source databases.

Can iceDQ validate BI report data against source systems?

Yes. iceDQ connects natively to Power BI via XMLA and Report connectors, Qlik Sense, Tableau, and other BI platforms to validate report data directly against underlying source systems at full volume.

How does iceDQ handle complex transformation testing?

iceDQ supports SQL expressions, Groovy scripting, and User Defined Functions to handle nearly 100% of testing scenarios. The AI Agent further accelerates rule creation from mapping documents or natural language prompts.

Can iceDQ send alerts and notifications when a test fails?

Yes. iceDQ sends automated alerts via Email, Slack and Microsoft Teams when a rule or workflow fails, enabling teams to act before issues reach downstream consumers.

Does iceDQ support AI based rule generation?

Yes. iceDQ’s AI Agent generates data testing rules from natural language prompts, mapping documents, or schema auto-discovery, enabling teams to build test coverage across hundreds of tables without manual authoring.

Does iceDQ support regression testing?

Yes. iceDQ workflows can be rerun after every pipeline change, data load, or release to detect unintended impact on data quality, with scheduled execution and instant pass or fail visibility across every rule in the workflow.

Can iceDQ integrate with test management platforms like Jira and ServiceNow?

Yes. iceDQ integrates with Jira, ServiceNow, and other test management platforms to automatically create, update, and close defect tickets based on rule results,