Architecture patterns of Azure Cosmos DB & Azure Synapse Analytics
summary
Four live demos show end-to-end patterns that actually run: Change Feed consumed by Synapse Mapping Data Flow and landed as rows in Azure SQL, Synapse Link for Cosmos DB queried from Serverless SQL pool via OPENROWSET and visualized in Power BI, Spark or Mapping Data Flow performing regular aggregations written to ADLS Gen2 to cut scan volume and cost, and custom partitioning producing per-EventDate/EventHour folders for partition pruning. All demos execute successfully and highlight when each pattern saves time or money.
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chapters (19)
- 0:00Agenda
- 1:03Synopsis
- 1:43Azure Cosmos DB
- 2:43Azure Synapse
- 3:06Change Feed
- 3:36Mapping Data Flow
- 4:11Near RealTime Analytics
- 6:41Cost Effective Archival
- 7:23Partitioning
- 9:52Demo Section
- 10:04Cosmos DB Data Explorer
- 10:50Cosmos DB Mapping Data Flow
- 13:46Cosmos DB SQL Database Table
- 14:43Synapse Studio
- 18:51Synapse Spark Notebook
- 19:46Mapping Dataflow
- 20:56Aggregating Data
- 22:41Demo
- 26:08Summary
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