ETL Automation and Validation Process

Discover how a leading French personal care company revolutionized its ETL automation and validation processes with DatagapsDataOpsSuite. Facing challenges like data type mismatches, performance issues, and complex data quality checks, they leveraged Spark for parallel processing and implemented custom transformations. This approach significantly reduced migration and testing times, achieving 100% test coverage and a 30% overall reduction in TCO.  

Whats inside

Automation of ETL and Validation: Minimize manual intervention and errors with automated ETL processes.  

Enhanced Performance with Spark: Efficiently handle large data volumes and overcome performance bottlenecks.  

Comprehensive Data Transformation: Simplify data preparation with pre-built and custom transformations, ensuring accurate data for analysis.  

Improved Data Quality and Integrity: Maintain high data accuracy with robust data quality and referential integrity rules.  

 

Download Datasheet
Download Datasheet
Download Datasheet
Download Datasheet
Download Datasheet

Data Quality

Automate testing of Business Intelligence applications by making use of the metadata available from the BI tools such as Tableau, OBIEE, and Business Objects.

Synthetic Data

Automate testing of Business Intelligence applications by making use of the metadata available from the BI tools such as Tableau, OBIEE, and Business Objects.

ETL Testing

Automate testing of Business Intelligence applications by making use of the metadata available from the BI tools such as Tableau, OBIEE, and Business Objects.

BI Validation

Automate testing of Business Intelligence applications by making use of the metadata available from the BI tools such as Tableau, OBIEE, and Business Objects.
Products

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DataOps Suite

End-to-End Data Testing Automation

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ETL Validator

Automate your Data Reconciliation & ETL/ELT testing

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BI Validator

Automate functional regression & performance testing of BI reports

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DQ Monitor

Monitor quality of data being Ingested or at rest using DQ rules & AI

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Test Data Manager

Maintain data privacy by generating realistic synthetic data using AI

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