Data engineering with Azure Databricks

Data Engineer

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.



Modules in this learning path

Discover the capabilities of Azure Databricks and the Apache Spark notebook for processing huge files. Understand the Azure Databricks platform and identify the types of tasks well-suited for Apache Spark.

Understand the architecture of an Azure Databricks Spark Cluster and Spark Jobs.

Work with large amounts of data from multiple sources in different raw formats. Azure Databricks supports day-to-day data-handling functions, such as reads, writes, and queries.

Your data processing in Azure Databricks is accomplished by defining DataFrames to read and process the Data. Learn how to perform data transformations in DataFrames and execute actions to display the transformed data.

Understand the difference between a transform and an action, lazy and eager evaluations, Wide and Narrow transformations, and other optimizations in Azure Databricks.

Use the DataFrame Column Class Azure Databricks to apply column-level transformations, such as sorts, filters and aggregations.

Use advanced DataFrame functions operations to manipulate data, apply aggregates, and perform date and time operations in Azure Databricks.

Understand the Azure Databricks platform components and best practices for securing your workspace through Databricks native features and by integrating with Azure services.

Learn how to use Delta Lake to create, append, and upsert data to Apache Spark tables, taking advantage of built-in reliability and optimizations.

Learn how Structured Streaming helps you process streaming data in real time, and how you can aggregate data over windows of time.

Use Delta Lakes as an optimization layer on top of blob storage to ensure reliability and low latency within unified Streaming + Batch data pipelines.

Azure Data Factory helps you create workflows that orchestrate data movement and transformation at scale. Integrate Azure Databricks into your production pipelines by calling notebooks and libraries.

CI/CID isn't just for developers. Learn how to put Azure Databricks notebooks under version control in an Azure DevOps repo and build deployment pipelines to manage your release process.

Azure Databricks is just one of many powerful data services in Azure. Learn how to integrate with other services, such as Azure Synapse Analytics and Azure Cosmos DB as part of your data architecture.

Learn best practices for workspace administration, security, tools, integration, databricks runtime, HA/DR, and clusters in Azure Databricks.