Incorporate real-time insights into your application with Stream Analytics
Azure Stream Analytics is a serverless, stateful processing engine that enables real time analytics on streams of data. With it you can ingest and process large data streams just by using SQL.
Its time windowing capabilities and built-in ML can be leveraged to create operational reporting, dashboards, anomaly detection, alerts and notifications pipelines... Its geospatial capabilities unlocks scenarios like fleet management, asset tracking, geo fencing...
It really enables any scenario that requires the generation of synthetic events, or the promotion of events, based on the behavior of cohorts.
In this session we will look at how to best build event-driven apps with Azure Stream Analytic, and benefit from its tight integration with the other serverless technologies of Azure.
Stream processing in SQL with Azure Stream Analytics
You know SQL? You need to process data in real time? Azure Stream Analytics (ASA) is the service for you. But let's dig a bit deeper and understand what makes a streaming query in ASA different from a T-SQL one in SQL Server.
In this session we'll discuss:
- the temporal aspect of stream processing, in theory and how it's implemented in ASA via time skew policies, windowing and how timestamps evolve as data flows through queries,
- how best to scale ASA jobs via partitioning,
- and how dynamic typing works in ASA, to never get bitten again inserting data in a SQL database
How to best develop jobs for Azure Stream Analytics
In this session we'll discuss the best developer experience for Azure Stream Analytics:
- Developing locally with VSCode
- Unit-testing with the asa cicd npm package
- Continuous deployment and its impact on a continuously running query
- The metrics to monitor on a streaming job
- Automation via PowerShell and Az.StreamAnalytics
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