In this course, the student will learn how to implement and manage data engineering workloads on Microsoft Azure, using Azure services such as Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Stream Analytics, Azure Databricks, and others. The course focuses on common data engineering tasks such as orchestrating data transfer and transformation pipelines, working with data files in a data lake, creating and loading relational data warehouses, capturing and aggregating streams of real-time data, and tracking data assets and lineage.

Associated Certification:

• Microsoft Certified: Azure Data Engineer Associate (DP-203T00–A)

• Explore compute and storage options for data engineering workloads in Azure
• Design and Implement the serving layer
• Understand data engineering considerations
• Run interactive queries using serverless SQL pools
• Explore, transform, and load data into the Data Warehouse using Apache Spark
• Perform data Exploration and Transformation in Azure Databricks
• Ingest and load Data into the Data Warehouse
• Transform Data with Azure Data Factory or Azure Synapse Pipelines
• Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines
• Optimize Query Performance with Dedicated SQL Pools in Azure Synapse
• Analyze and Optimize Data Warehouse Storage
• Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
• Perform end-to-end security with Azure Synapse Analytics
• Perform real-time Stream Processing with Stream Analytics
• Create a Stream Processing Solution with Event Hubs and Azure Databricks
• Build reports using Power BI integration with Azure Synpase Analytics
• Perform Integrated Machine Learning Processes in Azure Synapse Analytics

• Introduction to data engineering on Azure
• Introduction to Azure Data Lake Storage Gen2
• Introduction to Azure Synapse Analytics
• Use Azure Synapse serverless SQL pool to query files in a data lake
• Use Azure Synapse serverless SQL pools to transform data in a data lake
• Create a lake database in Azure Synapse Analytics
• Analyze data with Apache Spark in Azure Synapse Analytics
• Transform data with Spark in Azure Synapse Analytics
• Use Delta Lake in Azure Synapse Analytics
• Analyze data in a relational data warehouse
• Load data into a relational data warehouse
• Build a data pipeline in Azure Synapse Analytics
• Use Spark Notebooks in an Azure Synapse Pipeline
• Plan hybrid transactional and analytical processing using Azure Synapse Analytics
• Implement Azure Synapse Link with Azure Cosmos DB
• Implement Azure Synapse Link for SQL
• Get started with Azure Stream Analytics
• Ingest streaming data using Azure Stream Analytics and Azure Synapse Analytics
• Visualize real-time data with Azure Stream Analytics and Power BI
• Introduction to Microsoft Purview
• Integrate Microsoft Purview and Azure Synapse Analytics
• Explore Azure Databricks
• Use Apache Spark in Azure Databricks
• Run Azure Databricks Notebooks with Azure Data Factory

The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course includes data analysts and data scientists who work with analytical solutions built on Microsoft Azure.

Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions.

Specifically completing:
AZ-900 – Azure Fundamentals
DP-900 – Microsoft Azure Data Fundamentals