Organizations today rely on data-driven decision-making, but managing massive datasets across cloud platforms can be complex. DP-3011 Implementing a Data Analytics Solution with Azure Databricks equips data professionals to prepare, analyze, and govern data at scale using Apache Spark’s distributed computing capabilities.
In this one-day training, you’ll gain hands-on experience with Delta Lake for versioning and data integrity, automate data pipelines with Delta Live Tables, and implement governance with Unity Catalog. You’ll also explore Spark for large-scale data analysis, orchestrate workflows for production deployments, and collaborate in Python and SQL notebooks to deliver high-quality analytics-ready data.
Course Objectives
By the end of this course, participants will have the confidence to prepare and analyze data in Azure Databricks while applying governance and automation best practices. You will learn to:
- Explore Azure Databricks workloads and core components
- Perform large-scale data analysis with Spark and DataFrame APIs
- Manage transactions, schema enforcement, and versioning with Delta Lake
- Build automated data pipelines using Delta Live Tables
- Implement governance using Unity Catalog and Microsoft Purview
- Deploy production workloads with Azure Databricks Workflows
Who Should Attend?
This course is ideal for data analysts and data professionals who work with large datasets and want to leverage Azure Databricks for advanced analysis and pipeline automation. It is especially valuable for those responsible for preparing data for downstream analytics, applying governance to data lakes, and collaborating in notebook-based environments.
Course Prerequisites
- Familiarity with SQL and basic Python
- Working knowledge of Azure fundamentals
- Basic understanding of data engineering or analytics workflows
Course Outline
- Explore Azure Databricks
- Perform Data Analysis with Azure Databricks
- Use Apache Spark in Azure Databricks
- Manage Data with Delta Lake
- Build Data Pipelines with Delta Live Tables
- Deploy Workloads with Azure Databricks Workflows
