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WIFI SSID:SparkAISummit | Password: UnifiedAnalytics
Mark Kromer
Sr. Azure Data Program Manager
Microsoft
ETL Made Easy with Azure
Data Factory & Azure
Databricks
#UnifiedAnalytics #SparkAISummit
Azure Data Factory
Cloud ETL Patterns with ADF
3#UnifiedAnalytics #SparkAISummit
Nightly ETL Data Loads Code-free
Slowly Changing Dimension Scenario
Load Star Schema DW Scenario
Data Lake Data Science Scenario
Azure Data Factory
Workflow Data Pipelines/Control Flow
9#UnifiedAnalytics #SparkAISummit
Azure Data Factory
Mapping Data Flows
13#UnifiedAnalytics #SparkAISummit
What is ADF Mapping Data Flow?
• Transform Data, At Scale, in the
Cloud, Zero-Code
– Cloud-first, scale-out ELT
– Code-free dataflow pipelines
• Serverless scale-out transformation
execution engine
• Maximum Productivity for Data
Engineers
– Does NOT require understanding of
Spark / Scala / Python / Java
• Resilient Data Transformation
Flows
– Built for big data scenarios with
unstructured data requirements
– Operationalize with Data Factory
scheduling, control flow and
monitoring
Code-free Data Transformation At Scale
• Does not require understanding of Spark, Big Data Execution
Engines, Clusters, Scala, Python …
• Focus on building business logic and data transformation
– Data cleansing
– Aggregation
– Data conversions
– Data prep
– Data exploration
Transformation Function Expression Language
Debug Data Flows with Data Preview and Data
Sampling
Deep Monitoring Introspection of Data Transformations
DON’T FORGET TO RATE
AND REVIEW THE SESSIONS
SEARCH SPARK + AI SUMMIT

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