Microsoft’s Azure Synapse Analytics started off modestly enough – being a mere rebrand of Azure SQL Data Warehouse. It soon added robust data lake functionality, making it a broad analytics platform. But now, Synapse Analytics is also turning out to be the collection point where numerous Azure data services come together. Today, beyond the lake and warehouse, Synapse provides its own implementations of Azure Data Factory and Azure Data Explorer, as well as tight integration with Power BI and supported connections to Azure Machine Learning and the Azure Purview data governance platform.
It’s cool to have all that “under one roof,” but it can also be daunting. How can you become comfortable with such a broad-ranging service? Start with this session! It will help you understand the entire Synapse Analytics service at a high level and grasp the core lake and warehouse services (including the concepts underlying them) at a more granular one. Covered topics will include working with dedicated SQL pools, serverless SQL pools and Apache Spark pools; understanding the Synapse Studio IDE; working with notebooks; and learning the many ways to query the data lake with T-SQL. You’ll also learn about Synapse linked services and how they bridge Synapse to external systems. By the end of the session, Synapse should feel comfortable and familiar, and you’ll be ready to start working with it, armed with specific action items.
You will learn:
- Fundamentals of data warehouse and data lake platforms, and how to use them together
- Synapse Analytics developer tooling, including Synapse Studio and notebooks within it
- The basics of Apache Spark and open source Delta Lake technology
- How Synapse compares to competition like Snowflake, Amazon Redshift and Google BigQuery