#1 Big Data Lake Testing Automation Tool

★★★★★

5.0

Capterra

★★★★★

4.7/5

Petabytes of Data. Every Record Certified.

iceDQ automates big data lake testing to catch errors before they reach downstream systems. It validates transformations, tests billions of records, and reconciles data across Databricks, Snowflake, AWS S3, Azure Data Lake, Google Cloud Storage, and Hadoop - without sampling or manual intervention. Deliver trusted, certified data at scale.

Trusted by Fortune 500 companies

Why Choose iceDQ?

End-to-end big data lake testing automation designed for petabyte-scale validation and reconciliation.

Cross-Platform Data Lake Testing

Connect and validate data across Databricks, Snowflake, AWS S3, Azure Data Lake, Google Cloud Storage, Hadoop, and on-premise systems using iceDQ's 150+ ready-to-use connectors - in any combination of source and target.

Full-Volume Validation and Reconciliation

iceDQ tests every row, every column, every run - not 5-10% samples. Perform full attribute-level reconciliation between source systems and your data lake at million-record-per-second speeds, detecting missing records, transformation errors, and schema violations across billions of records.

Catch Data Lake Edge Cases at Scale

Design complex test scenarios to detect rare data anomalies, schema drift, late-arriving data, duplicate records, and ingestion failures that traditional sampling methods miss - across petabytes of raw, curated, and processed data.

CI/CD and DataOps Integration

Trigger automated data lake regression testing in your CI/CD pipeline using API-first design. Connect with Jenkins, Git, Azure DevOps, and Databricks Workflows to catch data failures on every pipeline deployment before they propagate downstream.

Auto-Rule Generation Across Petabyte Scale

Automatically generate validation rules across thousands of data lake tables and files in hours using iceDQ's AI rules engine - covering completeness, schema, transformation logic, duplicates, and reconciliation with minimal manual setup.

Reusable Test Suites Across Lake Layers

Reuse data lake test cases across raw, curated, and consumption layers in Dev, QA, and production environments to standardize validation and accelerate regression testing with every ingestion pipeline change.

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 big data lake test generation with minimal effort
Powerful scripting for complex data lake validation scenarios, with rule-based validation and reconciliation

High-Performance, Scalable Testing

Achieve million-record-per-second testing speeds across petabyte-scale data lakes
Flexible deployment on-prem or in the cloud with parallel and cluster processing

Seamless Connectivity and Integration

Connect to over 150 data lake platforms, databases, 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 data lake regression testing and enable end-to-end validation for DataOps

Benefits

See the transformation iceDQ delivers across real data lake projects

Data Lake Objects Validated

3,000

↓

5,000

67% more coverage

Test Automation Level

10% - 20%

↓

95%

~5x improvement

Data Lake Test Coverage

Less than 80%

↓

100%

Full coverage achieved

️

Testing Timeline

24 Months

↓

5 Months

79% faster delivery

Testing Team Size

10 People

↓

5 People

50% team reduction

Data Lake Regression Cycles

3 Months

↓

1 Month

3x faster cycles

Trusted by Industry Leaders

We have standardized iceDQ for all our cloud migration projects, ensuring data integrity and consistency across every environment.

Senior Director of Advanced Analytics, Albertsons

We probably saved 5,000 hours and $500,000 on the Data Migration Project by automating validation that was previously done manually.

Head of Quality Assurance,
PepsiCo

BMC was able to achieve 100% test coverage after iceDQ implementation, something that was not possible with our previous approach.

Director of Business Analytics, BMC Software

RuleGen utility helped Pfizer reduce the duration of IT testing from 24 months to 2 months.

Head of Data Governance,
Pfizer

iceDQ has enabled testers to keep up with the pace of developers and reduced the testing time by half.

Director of Quality Assurance,
HealthFirst

Not only did we achieve near perfect quality, but we also saved time and money on the project.

Director of Quality Engineering, Cencora

Built-In Functionalities

Parameterization

Rules Wizard

Big Data Lake 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 Certify Your Data Lake at Scale?

Try it for yourself today

Book a Demo

Frequently Asked Questions

What data lake platforms and cloud environments can iceDQ test?

iceDQ automates testing across all major data lake platforms including Databricks Delta Lake, Snowflake, AWS S3, Azure Data Lake Storage, Google Cloud Storage, Hadoop HDFS, and Apache Hive. It supports on-premises, public cloud, private cloud, and hybrid environments with 150+ native connectors - validating data in any combination of source and lake platform.

How does iceDQ perform full-volume testing instead of sampling?

iceDQ uses a high-performance in-memory and Spark-based engine that validates 100% of records - every row, every column, every run - at million-record-per-second speeds. Unlike sampling-based approaches that test 5-10% and miss the other 90%, iceDQ performs full attribute-level comparison across billions of records in a single run, detecting missing records, transformation errors, duplicates, null violations, and schema violations across your entire data lake.

How does iceDQ validate schema and data during data lake ingestion?

iceDQ validates schema compatibility between source systems and your data lake before and during ingestion - checking data types, nullability, column completeness, and format patterns. It detects schema drift, catches late-arriving data issues, validates incremental loads for correctness, and reconciles row counts and attribute values between source and lake at every ingestion stage.

Can iceDQ reconcile data between source systems and the data lake?

Yes. iceDQ performs full source-to-lake reconciliation at the attribute level - comparing every record between your source systems (databases, ERP, CRM, flat files, APIs) and every layer of your data lake (raw, curated, consumption). It validates row counts, calculated fields, aggregations, and business rules, providing detailed mismatch reports showing exactly which records failed and why.

How does iceDQ support data lake regression testing in CI/CD pipelines?

iceDQ is built API-first with native integrations for Jenkins, Azure Pipelines, GitHub Actions, Databricks Workflows, and Git. Data lake regression test suites run automatically on every ingestion pipeline deployment - catching schema changes, transformation regressions, and data failures before they propagate downstream. Test results push directly to JIRA, Azure Test Plans, ServiceNow, and HP ALM for full traceability.

How quickly can iceDQ auto-generate validation rules for data lake tables?

iceDQ's AI-driven auto-rule generation scans source and data lake schemas and generates validation rules across thousands of tables and files in hours. Rules cover completeness, data types, schema conformity, referential integrity, transformation logic, duplicates, and reconciliation - and can be reviewed, refined, and reused across raw, curated, and consumption layers.

How quickly can we deploy iceDQ for our data lake testing 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 data lake stack, builds initial test suites, and gets your team validating data at scale fast.