Implement a data engineering solution with Azure Databricks

  • Learning Path
  • 8 Modules

At a glance

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.

Prerequisites

None

Modules in this learning path

Perform incremental processing with spark structured streaming

You explore different features and tools to help you understand and work with incremental processing with spark structured streaming.

Implement streaming architecture patterns with Delta Live Tables

You explore different features and tools to help you develop architecture patterns with Azure Databricks Delta Live Tables.

Optimize performance with Spark and Delta Live Tables

Learn how to Optimize performance with Spark and Delta Live Tables in Azure Databricks.

Implement CI/CD workflows in Azure Databricks

Learn how to implement CI/CD workflows in Azure Databricks to automate the integration and delivery of code changes.

Automate workloads with Azure Databricks Jobs

Learn how to orchestrate and schedule data workflows with Azure Databricks Jobs. Define and monitor complex pipelines, integrate with tools like Azure Data Factory and Azure DevOps, and reduce manual intervention, leading to improved efficiency, faster insights, and adaptability to business needs.

Manage data privacy and governance with Azure Databricks

In this module, you explore different features and approaches to help you secure and manage your data within Azure Databricks using tools, such as Unity Catalog.

Use SQL Warehouses in Azure Databricks

Azure Databricks provides SQL Warehouses that enable data analysts to work with data using familiar relational SQL queries.

Run Azure Databricks Notebooks with Azure Data Factory

Using pipelines in Azure Data Factory to run notebooks in Azure Databricks enables you to automate data engineering processes at cloud scale.