While extract, transform, load (ETL) has its use cases, an alternative to ETL is data virtualization, which integrates data from disparate sources, locations, and formats, without replicating or moving the data, to create a single “virtual” data layer. The virtual data layer allows users to query data from many sources through a single, unified interface. Access to sensitive data sets can be controlled from a single location. The delays inherent to ETL need not apply; data can always be up to date. Storage costs and data governance complexity are minimized. See the pro’s and con’s of data virtualization via Data Virtualization vs Data Warehouse and Data Virtualization vs. Data Movement.
SQL Server 2019 big data clusters with enhancements to PolyBase act as a virtual data layer to integrate structured and unstructured data from across the entire data estate (SQL Server, Azure SQL Database, Azure SQL Data Warehouse, Azure Cosmos DB, MySQL, PostgreSQL, MongoDB, Oracle, Teradata, HDFS, Blob Storage, Azure Data Lake Store) using familiar programming frameworks and data analysis tools:
James covers some of the reasoning behind this and the shift from using Polybase to integrate data with Hadoop + Azure Blob Storage to using SQL Server as a data virtualization engine.