001 Identifying Azure Services for Stream Processing
002 Process Time Series Data
003 Demo Create a Stream Processing Solution by Using Stream Analytics and Azure Event Hubs
003 Direct link to the load generation script on GitHub.txt
005 Instructions for installing Azure Event Hubs Library into a Databricks cluster.txt
005 Process Data by Using Spark Structured Streaming
006 Demo Create Windowed Aggregates
008 Introducing Schema Drift
008 More on Input Validation in Azure Stream Analytics.txt
008 More on the Event Hub Schema Registry.txt
009 Demo Handle Schema Drift
010 Process within One Partition
011 Process Data across Partitions
012 Optimize Pipelines for Analytical or Transactional Purposes
014 More about Streaming Units on Microsoft Learn.txt
014 More about Throughput Units on Microsoft Learn.txt
014 Scale Resources
015 Demo Create Tests for Data Pipelines
016 Configure Checkpoints and Watermarking during Processing
017 Handling Late-Arriving Data
018 Handle Interruptions
019 Demo Configure Exception Handling
021 Demo Upsert Data
022 Replay Archived Stream Data
023 Section Recap
load-generator.ps1.txt