Explore and transform data in a lakehouse

 Transform and load data

Most data requires transformations before loading into tables. You might ingest raw data directly into a lakehouse and then further transform and load into tables. Regardless of your ETL design, you can transform and load data simply using the same tools to ingest data. Transformed data can then be loaded as a file or a Delta table.

  • Notebooks are favored by data engineers familiar with different programming languages including PySpark, SQL, and Scala.
  • Dataflows Gen2 are excellent for developers familiar with Power BI or Excel since they use the PowerQuery interface.
  • Pipelines provide a visual interface to perform and orchestrate ETL processes. Pipelines can be as simple or as complex as you need.


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