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

 

Course Overview

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.

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

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.

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.

Prices & Delivery methods

Online Training

Duration
1 day

Price
  • US $ 675
Classroom Training

Duration
1 day

Price
  • United States: US $ 675

Click on town name or "Online Training" to book Schedule

This is an Instructor-Led Classroom course
Instructor-led Online Training:   This is an Instructor-Led Online (ILO) course. These sessions are conducted via WebEx in a VoIP environment and require an Internet Connection and headset with microphone connected to your computer or laptop. If you have any questions about our online courses, feel free to contact us via phone or Email anytime.
This is a FLEX course, which is delivered simultaneously in two modalities. Choose to attend the Instructor-Led Online (ILO) virtual session or Instructor-Led Classroom (ILT) session.

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Canada

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