DP-3011 | Implement a Data Analytics Solution with Azure Databricks
 
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.
 
Audience Profile
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.
 
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.

Role
Data Analyst

Course Outline
Module 1: Explore Azure Databricks
Azure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark.
Introduction
Get started with Azure Databricks
Identify Azure Databricks workloads
Understand key concepts
Data governance using Unity Catalog and Microsoft Purview
Exercise - Explore Azure Databricks
Module assessment
Summary
Module 2: Perform data analysis with Azure Databricks
Learn how to perform data analysis using Azure Databricks. Explore various data ingestion methods and how to integrate data from sources like Azure Data Lake and Azure SQL Database. This module guides you through using collaborative notebooks to perform exploratory data analysis (EDA), so you can visualize, manipulate, and examine data to uncover patterns, anomalies, and correlations.
Introduction
Ingest data with Azure Databricks
Data exploration tools in Azure Databricks
Data analysis using DataFrame APIs
Exercise - Explore data with Azure Databricks
Module assessment
Summary
Module 3: Use Apache Spark in Azure Databricks
Azure Databricks is built on Apache Spark and enables data engineers and analysts to run Spark jobs to transform, analyze and visualize data at scale.
Introduction
Get to know Spark
Create a Spark cluster
Use Spark in notebooks
Use Spark to work with data files
Visualize data
Exercise - Use Spark in Azure Databricks
Module assessment
Summary
Module 4: Manage data with Delta Lake
Delta Lake is a data management solution in Azure Databricks providing features including ACID transactions, schema enforcement, and time travel ensuring data consistency, integrity, and versioning capabilities.
Introduction
Get started with Delta Lake
Create Delta tables
Implement schema enforcement
Data versioning and time travel in Delta Lake
Data integrity with Delta Lake
Exercise - Use Delta Lake in Azure Databricks
Module assessment
Summary
Module 5: Build Lakeflow Declarative Pipelines
Building Lakeflow Declarative Pipelines enables real-time, scalable, and reliable data processing using Delta Lake's advanced features in Azure Databricks.
Introduction
Explore Lakeflow Declarative Pipelines
Data ingestion and integration
Real-time processing
Exercise - Create a Lakeflow Declarative Pipeline
Module assessment
Summary
Module 6: Deploy workloads with Lakeflow Jobs
Deploying workloads with Lakeflow Jobs involves orchestrating and automating complex data processing pipelines, machine learning workflows, and analytics tasks. In this module, you learn how to deploy workloads with Databricks Lakeflow Jobs.
Introduction
What are Lakeflow Jobs?
Understand key components of Lakeflow Jobs
Explore the benefits of Lakeflow Jobs
Deploy workloads using Lakeflow Jobs
Exercise - Create a Lakeflow Job
Module assessment
Summary

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