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ADF Mapping Data Flows Level 300
Azure Data Factory can now use Mapping Data Flows to orchestrate ETL workloads. Mapping Data Flows allow users to visually design transformations on data from disparate sources and load the results into Azure SQL Data Warehouse for analytics. The key benefits of Mapping Data Flows are that they provide a visual interface for building expressions to cleanse and join data with auto-complete assistance and live previews of expression results.
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ADF Mapping Data Flows Level 300
- 1. Azure Data Factory MappingData Flows Level 300
- 4. Modern Data WarehousePattern Today Applications Dashboards Business/custom apps (structured) Logs, files, and media (unstructured) r Ingest storage Azure Storage/ Data Lake Store Data Loading Azure Data Factory Load flat files into data lake on a schedule Data processing Read data from files using DBFS Orchestration Azure Data Factory Extract and transform relational data Azure Databricks Serving storage Azure SQL DW Load processed data into tables optimized for analytics Clean and join with stored data Load to SQL DW Databases
- 5. Modern Data WarehousePattern with ADF Mapping Data Flows Applications Dashboards Business/custom apps (structured) Logs, files, and media (unstructured) r Azure Storage/ Data Lake Store Azure Data Factory Load files into data lake on a schedule Azure Data Factory Extract and transform relational data Azure SQL DW Load processed data into tables optimized for analytics Clean and join disparate data Databases Azure Databricks
- 7. Mapping Data Flowcommon scenarios
- 15. List of columns beingmodified All available functions, fields, parameters … Build expressions here with full auto- complete and syntax checking View results of your expression in the data preview pane with live, interactive results
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