Data Engineering Courses
Forward-thinking companies in every niche are exploring data, analytics and AI. As more organizations expand their capabilities in these key areas, the role of the data engineer is becoming even more important.
If you want to break into this growing field, developing your data engineering skills is a must. Databricks has a wealth of data engineering courses that can be taken through instructor-led training or self-paced learning, from the comfort of your home.
Study the foundations you’ll need to build a career, brush up on your advanced knowledge and learn the components of the Databricks Lakehouse Platform, straight from the creators of lakehouse.
Why take a data engineering course?
Data engineering courses available through the Databricks Academy are accessible from wherever you are. They will provide a solid foundation as you improve your data engineering skills.
The best courses for data engineering will give you the tools to decide your own career. With data engineering being such a broad industry, the skills you’ll learn in our courses will help set you up for success in a career that:
- Has a high average salary
- Is one of the fastest growing areas in all tech
- Enables greater opportunities
Get started with Databricks’ data engineering self-paced courses
We’ll cover a broad range of topics in our data engineering courses, which will teach you how to leverage the Databricks Lakehouse Platform for crucial day-to-day workflows — things like ingesting data, orchestrating production pipelines and more.
Designed by the expert team that started Apache Spark™ at UC Berkeley’s AMPLab, Databricks’ online courses are tailored to help you learn at your own pace.
Course information
Data Engineer Associate
Our data engineer course will benefit those from all walks of life who are looking to improve their data engineering knowledge, with a comprehensive introduction to the Databricks Lakehouse Platform. It will provide the data engineering foundations that directly support putting ETL pipelines into production.
This data engineering course will teach you the skills to program and problem solve your way to creating useful solutions. It will teach you the data engineering foundations to leverage the Databricks Lakehouse Platform and productionalize ETL pipelines.
Students will use Delta Live Tables with Spark SQL and Python to define and schedule pipelines that incrementally process new data from a variety of data sources into the lakehouse. Students will also orchestrate tasks with Databricks Workflows and promote code with Databricks Repos. In addition, participants will learn to:
- Use the Databricks Data Science and Engineering Workspace to perform common code development tasks in a data engineering workflow
- Use Spark to extract data from a variety of sources, apply common cleaning transformations, and manipulate complex data with advanced functions
- Build production data pipelines that incrementally ingest and process data through a multi-hop architecture using Delta Live Tables and orchestrate workloads using Databricks Workflow Jobs
- Configure permissions in Unity Catalog to ensure that users have proper access to databases for analytics and dashboarding
Data Engineer Professional
For seasoned data engineers on Databricks, the Databricks Academy offers courses that teach advanced data engineering concepts to take you to the next level of your career.
Advanced Data Engineering on Databricks will focus on building on existing data engineering knowledge to unlock the full potential of the lakehouse. We want to provide you with the expertise to build and design workloads that can ingest and analyze ever-growing data while minimizing refactoring and downtime. Participants will learn how to:
- Design databases and pipelines optimized for the Databricks Lakehouse Platform
- Implement efficient incremental data processing to validate and enrich data-driven business decisions and applications
- Leverage Databricks-native features for managing access to sensitive data and fulfilling right-to-be-forgotten requests
- Manage code promotion, task orchestration and production job monitoring using Databricks tools
Apache Spark
The Apache Spark™ course focuses on a more specialist area — using Delta Lake and Spark programming. This foundational course will cover some of the areas needed to get you up to speed and understand the benefits of Delta Lake.
This data engineering course is designed to give you a great understanding of the components of Spark, the Data Frame and Delta Lake to help improve your data pipelines.
Optimizing Apache Spark
For those familiar with Apache Spark programming, this is the best data engineering course to learn how to mitigate bottlenecks with the Spark UI.
This course explores five major performance problems for Apache Spark applications in production: Skew, Spill, Shuffle, Storage and Serialization. We’ll work with 1 TB+ data sets to diagnose these issues and discuss mitigation strategies.
We’ll also explore optimization techniques for data ingestion, including managing Spark-partition sizes, disk-partitioning, bucketing, Z-Ordering and more.
Students can also expect the course to cover performance concepts, including data locality, IO-caching and Spark-caching, pitfalls of broadcast joins, adaptive query execution, and dynamic partition pruning.
Finally, the course provides guidance on designing and configuring clusters for optimal performance for specific use cases, personas and cross-team security concerns.
Why does Databricks have the best data engineering courses?
Databricks provides learning paths for multiple personas and career paths, including data engineers, data analysts and ML engineers. From new learners to those seeking advanced data engineering skills, there’s a Databricks data engineering course for you.
The best courses for data engineering will give you both data and software engineering skills — so you’ll be capable of building data pipelines at scale and analyzing how they’re performing.
These new skills will be backed up by certification that helps you gain industry recognition and differentiate yourself from other data engineers.