Delta Lake is an open-source storage layer that’s included in Azure Databricks. It supports structured and unstructured data, ACID transactions, and batch and stream processing. This course provides an overview of Delta Lake, including some history of earlier data solutions and why you might choose Delta Lake instead. You'll learn how to use and optimize Delta Lake for your own workloads.
Learning Objectives
- Understand what Delta Lake is and what it's used for
- Learn how to optimize Delta Lake
Intended Audience
This course is intended for anyone who wants to learn how to use Delta Lake on Azure Databricks.
Prerequisites
To get the most from this course, you should already have some knowledge of Apache Spark and Azure Databricks. If you’re not familiar with those, then you should take our Running Spark on Azure Databricks course. It would also be helpful to have some experience with SQL.
Welcome to “Introduction to Delta Lake on Azure Databricks”. I’m Guy Hummel, and I’m a certified Azure Data Engineer.
In this course, I’ll give you an overview of Delta Lake, then I’ll show you how to use it, and finally, I’ll explain how to optimize it.
To get the most from this course, you should already have some knowledge of Apache Spark and Azure Databricks. If you’re not familiar with those, then you should take our “Running Spark on Azure Databricks” course. It would also be helpful to have some experience with SQL.
We’d love to get your feedback on this course, so please give it a rating when you’re finished. Thanks!
Guy launched his first training website in 1995 and he's been helping people learn IT technologies ever since. He has been a sysadmin, instructor, sales engineer, IT manager, and entrepreneur. In his most recent venture, he founded and led a cloud-based training infrastructure company that provided virtual labs for some of the largest software vendors in the world. Guy’s passion is making complex technology easy to understand. His activities outside of work have included riding an elephant and skydiving (although not at the same time).