When we start implementing a data lake using Microsoft Fabric, we might be tempted to start creating pipelines and notebooks right away, without thinking about design principles. However, there’s one design principle I’d like you to consider from the beginning: DRY. DRY is an acronym that stands for Don’t Repeat Yourself. In this blog post, …
Coming from the ‘old school’ world of SSIS and SQL Server, and later Azure Data Factory and Azure SQL Database, I have always built my ETL orchestration processes using some kind of pipelines. In Fabric, we also have pipelines (the successor of ADF), but, we can now also create notebook orchestration using NotebookUtils and runMultiple(). …
In Microsoft Fabric data engineering, we use Spark to apply transformations to our data. Just like you would write T-SQL to transform data in SQL Server, you would write PySpark or SparkSQL to transform data in a Fabric lakehouse. There are a lot of parallels between Spark en SQL in the way you can process …