At the PASS Summit this year, I attended a session by Michael Rys. In this session he introduced the concept of LETS as an approach to process data in the data lake. If you are familiar with data lake, then you will be familiar of having to apply a schema to the data held within. The LETS approach is purpose design for schematization.
Where ETL stands for Extract, Transform, Load or ELT stands for Extract, Load, Transform – LETS stands for Load, Extract, Transform, Store.
Data are Loaded into the data lake
Data are Extracted and schematized
Data are Transformed in rowsets
Data are Stored in a location, such as the Catalog in Azure Data Lake Analytics, Azure Data Warehouse, Azure Analysis Services, for analysis purposes.
I really like this approach as it makes sense for how data are handled in the data lake. It’s something that I will be advocating and using, and I hope you do too!