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

This course explores how to use Databricks and Apache Spark on Azure to take data projects from exploration to production. You ll learn how to ingest, transform, and analyze large-scale datasets with Spark DataFrames, Spark SQL, and PySpark, while also building confidence in managing distributed data processing. Along the way, you ll get hands-on with the Databricks workspace navigating clusters and creating and optimizing Delta tables. You ll also dive into data engineering practices, including designing ETL pipelines, handling schema evolution, and enforcing data quality. The course then moves into orchestration, showing you how to automate and manage workloads with Lakeflow Jobs and pipelines. To round things out, you ll explore governance and security capabilities such as Unity Catalog and Purview integration, ensuring you can work with data in a secure, well-managed, and production-ready environment.

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.

Before taking this course, learners should already be comfortable with the fundamentals of Python and SQL. This includes being able to write simple Python scripts and work with common data structures, as well as writing SQL queries to filter, join, and aggregate data. A basic understanding of common file formats such as CSV, JSON, or Parquet will also help when working with datasets. In addition, familiarity with the Azure portal and core services like Azure Storage is important, along with a general awareness of data concepts such as batch versus streaming processing and structured versus unstructured data. While not mandatory, prior exposure to big data frameworks like Spark, and experience working with Jupyter notebooks, can make the transition to Databricks smoother.