MODULE 1: Introduction to Azure Synapse Analytics
	 
	Learn about the features and capabilities of Azure Synapse Analytics - a cloud-based platform for big data processing and analysis.
	 
- 		Introduction
 - 		What is Azure Synapse Analytics
 - 		How Azure Synapse Analytics works
 - 		When to use Azure Synapse Analytics
 - 		Exercise - Explore Azure Synapse Analytics
 - 		Knowledge check
 - 		Summary
 
	 
	MODULE 2: Use Azure Synapse serverless SQL pool to query files in a data lake
	 
	With Azure Synapse serverless SQL pool, you can leverage your SQL skills to explore and analyze data in files, without the need to load the data into a relational database.
	 
- 		Introduction
 - 		Understand Azure Synapse serverless SQL pool capabilities and use cases
 - 		Query files using a serverless SQL pool
 - 		Create external database objects
 - 		Exercise - Query files using a serverless SQL pool
 - 		Knowledge check
 - 		Summary
 
	 
	MODULE 3: Analyze data with Apache Spark in Azure Synapse Analytics
	 
	Apache Spark is a core technology for large-scale data analytics. Learn how to use Spark in Azure Synapse Analytics to analyze and visualize data in a data lake.
	 
- 		Introduction
 - 		Get to know Apache Spark
 - 		Use Spark in Azure Synapse Analytics
 - 		Analyze data with Spark
 - 		Visualize data with Spark
 - 		Exercise - Analyze data with Spark
 - 		Knowledge check
 - 		Summary
 
	 
	MODULE 4:Use Delta Lake in Azure Synapse Analytics
	 
	Delta Lake is an open source relational storage area for Spark that you can use to implement a data lakehouse architecture in Azure Synapse Analytics.
	 
- 		Introduction
 - 		Understand Delta Lake
 - 		Create Delta Lake tables
 - 		Create catalog tables
 - 		Use Delta Lake with streaming data
 - 		Use Delta Lake in a SQL pool
 - 		Exercise - Use Delta Lake in Azure Synapse Analytics
 - 		Knowledge check
 - 		Summary
 
	 
	MODULE 5: Analyze data in a relational data warehouse
	 
	Relational data warehouses are a core element of most enterprise Business Intelligence (BI) solutions, and are used as the basis for data models, reports, and analysis.
	 
- 		Introduction
 - 		Design a data warehouse schema
 - 		Create data warehouse tables
 - 		Load data warehouse tables
 - 		Query a data warehouse
 - 		Exercise - Explore a data warehouse
 - 		Knowledge check
 - 		Summary