Built a complete data integration solution with Microsoft Fabric Dataflow Gen1!
Quote from bsdinsight on 9 April 2025, 20:17🚀 Built a complete data integration solution with Microsoft Fabric Dataflow Gen1!
I recently had the opportunity to work hands-on with Fabric Dataflow Gen1, where I designed and implemented a full pipeline to integrate, transform, and load data from multiple sources into a lakehouse for reporting. Here’s a breakdown of the process I followed:
🔧 Step-by-step implementation:
Created a new data pipeline in the Fabric workspace.
Added a Dataflow activity to the pipeline.
Set up a connection to the Lakehouse as my data source.
Within the Dataflow:
Searched and connected to the appropriate Lakehouse from different workspaces.
Loaded data from three sources: one file and two tables from different lakehouses.
Used Diagram View to visually structure and track data transformation steps.
Appended all three datasets into a single query for unified reporting.
Resolved schema mismatches:
Renamed inconsistent column headers.
Standardized data types.
Removed the top erroneous row.
Cleansed duplicate records.
Switched to Power Query Script View to inspect the M-code logic behind the transformation.
Defined the Lakehouse destination and configured schema and settings manually.
Published the Dataflow and confirmed successful data ingestion.
Created an auto-generated report via SQL Analytical Endpoint using the default semantic model.
Demonstrated custom column creation, useful for SCD Type 1 implementations.
📊 Dataflow Gen1 vs Gen2 – A Quick Comparison:
Engine: Power Query vs Apache Spark
Storage: ADLS Gen2 vs OneLake
Performance: Moderate vs High (distributed)
Governance: Basic vs Enterprise-grade
✨ This project reinforced my understanding of Microsoft Fabric’s integration capabilities and the transformation power of Power Query within Dataflows. Excited to explore more with Gen2 and optimize performance using Spark engines and advanced orchestration.
🚀 Built a complete data integration solution with Microsoft Fabric Dataflow Gen1!
I recently had the opportunity to work hands-on with Fabric Dataflow Gen1, where I designed and implemented a full pipeline to integrate, transform, and load data from multiple sources into a lakehouse for reporting. Here’s a breakdown of the process I followed:
🔧 Step-by-step implementation:
Created a new data pipeline in the Fabric workspace.
Added a Dataflow activity to the pipeline.
Set up a connection to the Lakehouse as my data source.
Within the Dataflow:
Searched and connected to the appropriate Lakehouse from different workspaces.
Loaded data from three sources: one file and two tables from different lakehouses.
Used Diagram View to visually structure and track data transformation steps.
Appended all three datasets into a single query for unified reporting.
Resolved schema mismatches:
Renamed inconsistent column headers.
Standardized data types.
Removed the top erroneous row.
Cleansed duplicate records.
Switched to Power Query Script View to inspect the M-code logic behind the transformation.
Defined the Lakehouse destination and configured schema and settings manually.
Published the Dataflow and confirmed successful data ingestion.
Created an auto-generated report via SQL Analytical Endpoint using the default semantic model.
Demonstrated custom column creation, useful for SCD Type 1 implementations.
📊 Dataflow Gen1 vs Gen2 – A Quick Comparison:
Engine: Power Query vs Apache Spark
Storage: ADLS Gen2 vs OneLake
Performance: Moderate vs High (distributed)
Governance: Basic vs Enterprise-grade
✨ This project reinforced my understanding of Microsoft Fabric’s integration capabilities and the transformation power of Power Query within Dataflows. Excited to explore more with Gen2 and optimize performance using Spark engines and advanced orchestration.
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