Azure Data Factory: Data Wrangling
Power Query in ADF
Updated Public Preview Q1 CY21
What is Data Wrangling?
 Code-free data exploration and data prep
 Operationalize Power Query as an activity by
translating M script in ADF data flow script
 Execute Power Query as a pipeline activity using the
ADF data flow serverless, scaled-out, ADF-managed
Apache Spark engine
 Essentially acts as a data-first entry point to building
ADF data flows
ADF Data Wrangling Use Cases
 Data Engineer is building an ETL process in ADF uses PQ to explore data using data profiling
 Business Analyst is a PQ desktop user and wishes to operationalize their M query in a data
pipeline that sinks data in the Lake
 Data Engineer needs to prep data for modeling and ETL by using a data-first approach. Creates a
PQ wrangling activity and adds it to pipeline.
 Trimming strings
 Data type conversions
 Rename columns
 Remove columns
 Value prop
 “Data Wrangling in ADF”, not “Power Query lift-and-shift”
ADF Data Wrangling Roadmap – PQ Activity
 Continue to add more M functions to fold into Spark
 Add more native connectors that work in both ADF & Power Query
 Enable V-Net in Power Query Online data wrangling experience in ADF
 Launch PQ activity in Synapse Pipelines
 Enable interactive monitoring similar to Copy and Data Flow
Additional
resources
Documentation
List of tutorial videos
Expression language reference
Performance guide
ADF twitter
ADF tech community blog

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Azure Data Factory Data Wrangling with Power Query

  • 1. Azure Data Factory: Data Wrangling Power Query in ADF Updated Public Preview Q1 CY21
  • 2. What is Data Wrangling?  Code-free data exploration and data prep  Operationalize Power Query as an activity by translating M script in ADF data flow script  Execute Power Query as a pipeline activity using the ADF data flow serverless, scaled-out, ADF-managed Apache Spark engine  Essentially acts as a data-first entry point to building ADF data flows
  • 3. ADF Data Wrangling Use Cases  Data Engineer is building an ETL process in ADF uses PQ to explore data using data profiling  Business Analyst is a PQ desktop user and wishes to operationalize their M query in a data pipeline that sinks data in the Lake  Data Engineer needs to prep data for modeling and ETL by using a data-first approach. Creates a PQ wrangling activity and adds it to pipeline.  Trimming strings  Data type conversions  Rename columns  Remove columns  Value prop  “Data Wrangling in ADF”, not “Power Query lift-and-shift”
  • 4. ADF Data Wrangling Roadmap – PQ Activity  Continue to add more M functions to fold into Spark  Add more native connectors that work in both ADF & Power Query  Enable V-Net in Power Query Online data wrangling experience in ADF  Launch PQ activity in Synapse Pipelines  Enable interactive monitoring similar to Copy and Data Flow
  • 5. Additional resources Documentation List of tutorial videos Expression language reference Performance guide ADF twitter ADF tech community blog