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