Azure synapse - your data warehouse, data lake, data pipelines, analytics and much more in one service. It integrates seamlessly with Power BI, AI from cognitive services and of course Azure machine learning service, and it has similar data connectors. On top of this, you can work with telemetry data (IOT) and use Apache Spark and SQL engines, while having all the data languages of the data department; T-SQL, KQL, Python, Spark SQL, Scala, .Net, ect. Wow, that's a lot, it really is the entire data department.

So how do you get started on this journey of a unified experience to ingest, explore, prepare, transform, manage, and serve data for immediate BI and machine learning needs? We start by taking a look at a Synapse workspace, then dive into the SQL pool and the data explorer pool, so now we are ready for the data lake, before we get back up on the dry again with some Apache spark, so we can orchestrate some pipelines, before we finally visualize all this amazing data in Power BI. We will be covering a lot in a short time, only dipping our toes on many subjects, therefore we will of course include plenty of links to learning resources for longer deep dives.