Explore and transform data in a lakehouse
After loading data into the lakehouse, you can use various tools and techniques to explore and transform it, including:
Apache Spark: Each Fabric lakehouse can use Spark pools through Notebooks or Spark Job Definitions to process data in files and tables in the lakehouse using Scala, PySpark, or Spark SQL.
Notebooks: Interactive coding interfaces in which you can use code to read, transform, and write data directly to the lakehouse as tables and/or files.
Spark job definitions: On-demand or scheduled scripts that use the Spark engine to process data in the lakehouse.
SQL analytic endpoint: Each lakehouse includes a SQL analytic endpoint through which you can run Transact-SQL statements to query, filter, aggregate, and otherwise explore data in lakehouse tables.
Dataflows (Gen2): In addition to using a dataflow to ingest data into the lakehouse, you can create a dataflow to perform subsequent transformations through Power Query, and optionally land transformed data back to the lakehouse.
Data pipelines: Orchestrate complex data transformation logic that operates on data in the lakehouse through a sequence of activities (such as dataflows, Spark jobs, and other control flow logic).
Analyze and visualize data in a lakehouse
The data in your lakehouse tables is included in a semantic model that defines a relational model for your data. You can edit this semantic model (or create other semantic models), defining custom measures, hierarchies, aggregations, and other elements of a semantic model. You can then use the semantic model as the source for a Power BI report that enables you to visualize and analyze the data.
By combining the data visualization capabilities of Power BI with the centralized storage and tabular schema of a data lakehouse, you can implement an end-to-end analytics solution on a single platform.