It’s hard to imagine data warehousing without ETL (extract, transformation, and load). For decades, analysts and engineers have embraced no-code ETL solutions for increased maintainability. Does this ...

Locking down AI pipelines in Azure? A zero-trust, metadata-driven setup makes it secure, scalable and actually team-friendly.

ETL (Extraction, Transformation, and Loading) in SQL Server is especially useful when data from the source systems does not conform to the business rules. Before loading all the data captured into a ...

Microsoft first truly disrupted the ETL marketplace with the introduction of SQL Server Integration Services (SSIS) back with the release of SQL Server 2005. Microsoft has upped the ante yet again by ...

BlazingSQL builds on RAPIDS to distribute SQL query execution across GPU clusters, delivering the ETL for an all-GPU data science workflow.

How are data integration and ETL similar? From the use cases and examples presented above, it’s evident that data integration and ETL are closely related concepts.

Global software house Microsoft is making big data the focus of SQL Server 2019, set for release later this year. A key part is data virtualisation, eliminating complex ETL processes.