A Data Engineer's Guide to Azure Synapse

There has been an explosion of interest in Azure Synapse Analytics as everyone races to get to grips with the all-in-one data analytics platform. But when opening up the box, you find it's a lot more complex that it's made out to be, with several different powerful compute engines, each with their own idiosyncrasies! Why do we have different flavours of each engine? When should you use Spark pools over SQL? What's the most cost effective approach for different scenarios? What types of users should be using each service? The answers to these questions aren't always met with clarity!

This training day breaks down the Synapse workspace into it's component parts and provides a foundation of knowledge for each piece. During the day, we will cover:

- Fundamentals of building a Lake-based analytical platform - how you structure a lake, what file format to choose, what kinds of data work it's suited for
- How the SQL Pools work, patterns for optimising performance and cost and how we can use our SQL endpoints to integrate with other services
- The Synapse Spark engine, demonstrating how you can write dynamic workflows in Python or bring your existing SQL logic to spark directly.
- Data Explorer pools and how you can use it for deep exploration of logs, time series and other fast-moving unstructured data sources
- Synapse Integrations, how you can take your workspace and integrate directly with tools such as Azure Purview, CosmosDB and the wider Dataverse

There is a huge amount to cover, but you'll be guided by Data Platform MVP Simon Whiteley & veteran analytics consultant Zach Stagers, both of whom have deep knowledge across the whole of this wide and sprawling tech stack.

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