Synapse Q&A with PG
summary
Serverless now reads Delta tables visible to serverless when Spark writes them, but pipelines cannot write Delta (only Spark engines can), and mapping data flows can read Delta but not write it. Views versus pre-transformed gold layers depends on volume, reuse and skills — large, repeated transforms push to Spark; long nested views hit resource grants and slow queries. Synapse ML adds AutoML, MLflow and Azure OpenAI hooks; self-hosted IR private routing is unsupported; dedicated pools cost-effectiveness depends on sizing — 500 DWU ≈ one node.
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chapters (29)
- 0:00Introduction and team intro
- 1:49Synapse Espresso videos overview
- 3:40Lake house data transformation patterns
- 9:12Database designer and templates direction
- 11:36Databricks and Synapse integration
- 13:01Consistent data science experience
- 15:37Fast operational reporting methods
- 19:08Spark notebook session reuse
- 21:46Notebook type support plans
- 22:51Open table formats beyond Delta
- 23:42CI/CD in Azure DevOps plans
- 26:31Indexing in dedicated SQL pools
- 27:55Identity columns in Spark
- 28:37Reading Delta files in pipelines
- 29:39Consuming gold layer data
- 33:51Quick-fire audience questions
- 34:32Self-hosted IR private routing
- 35:37Synapse Link data lake support
- 36:20Dedicated SQL pool cost concerns
- 38:28Online scaling in Gen2
- 39:09KQL to SQL transition tips
- 40:10Spark pool Python library install
- 40:53On-prem GitLab server support
- 41:36Best data transformation tools
- 43:45CI/CD experience improvements
- 45:53UTF-8 support in dedicated pools
- 46:13Production workload under 500 DWUs
- 47:39Learning resources for beginners
- 49:04Closing and community thanks
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