Implementing a Data Analytics Solution with Azure Databricks (DP-3011)
This learning path helps prepare you for Exam DP-203: Data Engineering on Microsoft Azure. Learn how to harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run large data engineering workloads in the cloud.
WHAT YOU'LL LEARN
Students will learn to,
- Explore Azure Databricks
- Use Apache Spark in Azure Databricks
- Use Delta Lake in Azure Databricks
- Use SQL Warehouses in Azure Databricks
- Run Azure Databricks Notebooks with Azure Data Factory
WHO SHOULD ATTEND?
Students willing to prepare for Exam DP-203: Data Engineering on Microsoft Azure.
COURSE OUTLINE
Module 1 : Explore Azure Databricks
- Provision an Azure Databricks workspace.
- Identify core workloads and personas for Azure Databricks.
- Describe key concepts of an Azure Databricks solution.
Module 2 : Use Apache Spark in Azure Databricks
- Describe key elements of the Apache Spark architecture.
- Create and configure a Spark cluster.
- Describe use cases for Spark.
- Use Spark to process and analyze data stored in files.
- Use Spark to visualize data.
Module 3 : Use Delta Lake in Azure Databricks
- Describe core features and capabilities of Delta Lake.
- Create and use Delta Lake tables in Azure Databricks.
- Create Spark catalog tables for Delta Lake data.
- Use Delta Lake tables for streaming data.
Module 4 : Use SQL Warehouses in Azure Databricks
- Create and configure SQL Warehouses in Azure Databricks.
- Create databases and tables.
- Create queries and dashboards.
Module 5 : Run Azure Databricks Notebooks with Azure Data Factory
- Describe how Azure Databricks notebooks can be run in a pipeline.
- Create an Azure Data Factory linked service for Azure Databricks.
- Use a Notebook activity in a pipeline.
- Pass parameters to a notebook.
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