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 ...

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 ...

An ETL Example Consider the classic example of key transformation. The application database uses a customer_id to index into the customer table, while the CRM system has the same customer referenced ...

Learn the key differences between data integration and ETL in this guide, which provides their side-by-side comparison.

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

Use this comprehensive comparison between data ingestion and ETL to explore data ingestion and ETL and their differences.

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.