The Power Duo: Databricks Auto Loader and Delta Live Tables

In my last two posts, I explained the benefits of using autoloader for your data pipelines with Databricks. A lot can be said that this is a very efficient way to handle incoming data batches for your ingestion process. The power of your data pipeline doesn’t stop there. Even if autoloader simplifies the process of ingesting large volumes of data into Databricks Delta Lake, we have the ability to take batch processing and allow real-time data streams instead. This is where Databricks Delta Live Tables comes in.

What are Delta Live Tables or DLTs?

Delta Live Tables or DLTs for short allows you to create data pipelines that are managed real-time. This will make ETL development easier to execute and maintain when the goal of your ETL involves data coming in with high volume and velocity. It is important to understand the difference between Delta Tables and Delta “Live” Tables before you proceed.

Delta Tables are a way to store your data as tables in Databricks while Delta Live Tables are a way to introduce how data flows within these tables. Whereas Delta Tables provide a table architecture, DLTs are a data pipeline framework that you can use when building your ETL pipeline.