#1 Databricks Testing Automation Tool

★★★★★

5.0

Capterra

★★★★★

4.7/5

Why Risk Your Lakehouse Migration with Manual Testing?

iceDQ automates end-to-end Databricks testing across Bronze, Silver, and Gold layers with native support for Delta Tables, Delta Live Tables, and Delta Lake. Validate schema, row counts, and data reconciliation from any source - Azure Synapse, SQL Server, Oracle, SAP, Teradata, and more - to Databricks, with no sampling and no manual effort.

Databricks Migration Testing Automation Tool

Trusted by Fortune 500 companies

Why Choose iceDQ?

End-to-end Databricks testing automation designed for migration, validation, and cross-layer data reliability.

Cross-Layer Databricks Testing

Validate data quality and integrity across every layer of your Databricks Medallion architecture - Bronze, Silver, and Gold - reconciling record counts, transformation logic, and business rules at each hop with full-volume testing and no sampling.

Source-to-Databricks Migration Testing

Migrate from any source - Azure Synapse, SQL Server, Oracle, SAP, Teradata, or on-premise databases - to Databricks with automated reconciliation at every phase. Row counts, schema validation, transformation testing, and go-live certification built in.

Schema Drift Detection and Delta Table Validation

Catch column additions, type changes, and removals across Delta table versions before they break pipelines. Validate Delta Table completeness, detect duplicates after merge operations, and verify incremental loads add correct records without loss.

CI/CD and DataOps Integration

Trigger automated Databricks regression testing in your CI/CD pipeline using API-first design. Validation rules travel from Dev to Staging to Production with no rebuild - catching data failures before they propagate through your Lakehouse.

AI-Driven Auto-Rule Generation for Databricks

Automatically generate validation and reconciliation rules across thousands of Delta tables and columns in hours using iceDQ's AI rules engine. Natural language to validation rules via AI agents - covering completeness, schema, transformation logic, and business rules.

Reusable Test Suites Across Medallion Layers

Reuse Databricks test cases across Dev, QA, UAT, and Production Medallion environments to standardize validation and accelerate regression testing cycles with every pipeline release and schema change.

Migration to Databricks - Validated at Every Stage

iceDQ de-risks Databricks migration projects with automated testing and reconciliation at every phase - from source profiling to go-live certification.

Validation Rule

Source Profiling

Profile and test source data quality before migration. Document anomalies to prevent carry-over into Delta Lake.

Validation Rule

Schema and DDL Check

Validate schema compatibility between source and Databricks target - data types, nullability, and constraints.

Reconciliation Rule

Data Movement Recon

Row count, hash-based, and value-level reconciliation to verify data landed correctly in Delta Lake Bronze layer.

Reconciliation Rule

Transformation Testing

Verify all transformation logic produces expected results in Silver and Gold layers before cutover.

All Rule Types

Go-Live Certification

Post-migration certification - completeness, accuracy, and business logic sign-off before go-live.

Databricks-Specific Testing Capabilities

Delta Table Completeness

Cross-Layer Reconciliation

Incremental Load Validation

Schema Drift Detection

Duplicate Detection on Delta

Business Rule Conformity

Out-of-Box Checks

Accelerate Big Data Lake Testing with Prebuilt Data Reliability Checks

Custom

Complex conditions using custom expressions

Completeness

Validates for NULLs, spaces, or empty values

Contains

Verifies attribute contains only specified values

Datatype

Checks if value can be cast to a specific type

Range

Ensures values fall within a specified range

Date

Validates strings against selected date formats

Pattern

Matches values against a regular expression

Duplicate

Detects duplicates across one or more attributes

Length

Checks the length of each attribute value

Reconciliation

Cross-system record matching and validation

Features

Easy, Low-Code/No-Code Testing

Automate Databricks test generation with minimal effort
Powerful scripting for complex Delta table validation scenarios, with rule-based validation and reconciliation

High-Performance, Scalable Testing

Achieve million-record-per-second Databricks testing speeds
Flexible deployment on-prem or in the cloud with parallel and cluster processing

Seamless Connectivity and Integration

Connect to over 150 databases, cloud platforms, cloud systems, and file sources
Integrate seamlessly with test case management and ticketing systems

Accelerate DataOps with API-First Design

Fully compatible with CI/CD pipelines
Automate Databricks regression testing and enable end-to-end validation for DataOps

Benefits

See the transformation iceDQ delivers across real Databricks projects

Databricks Objects Validated

3,000

↓

5,000

67% more coverage

Test Automation Level

10% - 20%

↓

95%

~5x improvement

Databricks Test Coverage

Less than 80%

↓

100%

Full coverage achieved

️

Migration Testing Timeline

24 Months

↓

5 Months

79% faster delivery

Testing Team Size

10 People

↓

5 People

50% team reduction

Databricks Regression Cycles

3 Months

↓

1 Month

3x faster cycles

Trusted by Industry Leaders

iceDQ validated our Azure Synapse to Databricks migration end to end. We achieved an 87% reduction in execution costs post-migration - and had the data to prove it was correct before go-live.

Data Engineering Team, Cencora

We deployed 9,000+ reconciliation rules across our Databricks and SAP environments with full ServiceNow integration. iceDQ gave us the coverage we needed at enterprise scale.

Head of Quality Assurance,
PepsiCo

iceDQ tested our full 4-layer Azure Databricks pipeline - Raw, Clean, Advanced, and Mart - with 20+ custom rules. We had complete confidence in every layer before production release.

Data Platform Team, QBE Insurance

We have standardized iceDQ for all our cloud migration projects. It has become the foundation of our data validation process, ensuring accuracy and consistency across every environment we deploy.

Senior Director of Advanced Analytics, Albertsons

BMC was able to achieve 100% test coverage after iceDQ implementation. This level of coverage was simply not possible with our previous manual approach and gave us confidence in every release.

Director of Business Analytics, BMC Software

RuleGen utility helped Pfizer reduce the duration of IT testing from 24 months to 2 months. The automation capabilities transformed how we approach data validation at scale.

Head of Data Governance, Pfizer

Built-In Functionalities

Parameterization

Rules Wizard

Databricks Migration Validation

Data Monitoring

Built-In Scheduler

️User-Defined Function

Flat File Testing

️SAP HANA Migration Testing

Reporting and Analytics

Security - LDAP and SSO

Query Designer

Regression Testing

Salesforce Migration Testing

Alerts and Notifications

️Integrated Key Vault

Ready to Migrate to Databricks with Confidence?

Try it for yourself today

Book a Demo

Frequently Asked Questions

What types of Databricks testing can iceDQ automate?

iceDQ automates the full spectrum of Databricks testing including Delta table validation, cross-layer reconciliation (Bronze to Silver to Gold), schema drift detection, incremental load validation, duplicate detection on Delta tables, transformation testing, migration testing from any source to Databricks, and production monitoring. It supports Azure Databricks, Databricks on AWS, and GCP Databricks environments with 150+ native connectors.

How does iceDQ validate data across the Medallion architecture?

iceDQ performs full attribute-level reconciliation at every Medallion layer - Bronze, Silver, and Gold. It validates that data landed correctly in Bronze from source systems, that Silver transformations produced expected results, and that Gold layer data matches business rules and is ready for BI consumption. Every check runs against 100% of records, not samples, with detailed exception reports showing exactly which records failed at which layer.

How does iceDQ handle Databricks migration testing from source to Delta Lake?

iceDQ covers all five phases of Databricks migration testing - source profiling before migration, schema and DDL compatibility validation, data movement reconciliation to verify records landed correctly in Bronze, transformation testing in Silver and Gold layers, and final go-live certification with completeness, accuracy, and business logic sign-off. Migration sources include Azure Synapse, SQL Server, Oracle, SAP, Teradata, DB2, flat files, and more.

Can iceDQ detect schema drift and validate incremental loads in Delta tables?

Yes. iceDQ monitors Delta table schemas across versions, alerting on column additions, type changes, and removals before they break downstream pipelines. For incremental loads, it verifies each Delta load adds the correct records without duplication or loss - running row count reconciliation, duplicate detection, and value-level validation on every incremental batch.

How does iceDQ integrate with Databricks CI/CD and DataOps pipelines?

iceDQ is built API-first with native integrations for Azure DevOps, Jenkins, GitHub Actions, and Databricks Workflows. Validation rules travel from Dev to Staging to Production with no rebuild required. Test results push directly to JIRA, Azure Test Plans, ServiceNow, and HP ALM - giving your team full traceability across every Databricks deployment and pipeline release.

How quickly can iceDQ auto-generate validation rules for Databricks?

iceDQ's AI-driven auto-rule generation scans Delta table schemas and generates validation rules across thousands of tables and columns in hours. Rules cover completeness, data types, referential integrity, transformation logic, duplicates, and cross-layer reconciliation. Natural language rule creation via AI agents is also available - enabling your team to describe validation requirements in plain English and generate rules automatically.

How quickly can we deploy iceDQ for our Databricks environment?

Most organizations complete a proof of concept within 2-4 weeks and full deployment within 30 days. Every iceDQ customer receives a dedicated Forward Deployed Engineer (FDE) for 3 months at no additional cost - who configures the platform to your specific Databricks stack, builds initial test suites across your Medallion layers, and gets your team validating pipelines fast.