Implementing a Data Analytics Solution with Azure Databricks (DP-3011)

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.

Module 1 : Explore Azure Databricks

• Provision an Azure Databricks workspace
• Identify core workloads for Azure Databricks
• Use Data Governance tools Unity Catalog and Microsoft Purview
• Describe key concepts of an Azure Databricks solution

Module 2 : Perform data analysis with Azure Databricks

• Ingest data using Azure Databricks.
• Using the different data exploration tools in Azure Databricks.
• Analyze data with DataFrame APIs.

Module 3 : 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 4 : Manage data with Delta Lake

• What Delta Lake is
• How to manage ACID transactions using Delta Lake
• How to use schema versioning and time travel in Delta Lake
• How to maintain data integrity with Delta Lake

Module 5 : Build data pipelines with Delta Live Tables

• Describe Delta Live Tables
• Ingest data into Delta Live Tables
• Use Data Pipelines for real time data processing

Module 6 : Deploy workloads with Azure Databricks Workflows

• What Azure Databricks Workflows are
• The key components and benefits of Azure Databricks Workflows
• How to deploy workloads using Azure Databricks Workflows

Students willing to Implement a Data lakehouse Analytics Solution with Azure Databricks