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

Course Content

• Explore Azure Databricks
• Perform data analysis with Azure Databricks
• Use Apache Spark in Azure Databricks
• Manage data with Delta Lake
• Build Lakeflow Declarative Pipelines
• Deploy workloads with Lakeflow Jobs

Prerequisites

Before starting this learning path, you 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.

Who Should Attend

This course is designed for data professionals who want to strengthen their skills in building and managing data solutions on Azure Databricks. It’s a good fit if you’re a data engineer, data analyst, or developer with some prior experience in Python, SQL, and basic cloud concepts, and you’re looking to move beyond small-scale analysis into scalable, production-ready data processing. Whether your goal is to modernize analytics workflows, optimize pipelines, or better manage and govern data at scale, this learning path will equip you with the practical skills to succeed.