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
Learn more at Databricks Academy
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
Learn more at Databricks Academy
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.
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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.
Learn more at Databricks Academy
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.
The goal of data engineering is to make large and complex data accessible for others to interpret. When data — both structured and unstructured — enters a company’s systems, data engineers are the first people to get their hands on it.
Databricks data engineering courses will ensure that you are able to process this data by building effective pipelines to manage this raw data and convert it into usable information. You’ll learn to use the Databricks Lakehouse Platform to eliminate silos and get the most from your data.
The Apache Spark course takes you through the technology foundation of Delta Lake, an open source storage layer that brings reliability to data lakes by building a lakehouse architecture on top of them. Delta Lake sits on top of Apache Spark and adds reliability so your analytics and machine learning initiatives have easy access to quality, reliable data.
This data engineering course offered by Databricks helps you build highly scalable production data pipelines, using SQL and Python to extract, transform and load data into tables and views in the lakehouse.
You’ll learn how these tables correspond to different quality levels, progressively adding structure to the data. For example, tables for data ingestion, transformation/feature engineering, and machine learning training or prediction — referred to collectively as a multi-hop architecture.
You may already know how to use Lambda architecture, but the Databricks big data engineering courses will show you a different technique. In Lambda, records are processed by a batch system and streaming system in parallel. The results are combined during query time to provide a complete answer.
Medallion architecture, meanwhile, logically organizes data in the lakehouse into layers, with the structure and quality improving by the layer. Our online data engineering courses cover the major bottlenecks and explore how Medallion architecture can be implemented.
Since data engineering is all about preparing data so it’s ready for data scientists to use, it makes sense to take a holistic view of the entire data analytics realm. Databricks data engineering courses will help you take data integrity to the next level with Delta Lake.
