Streamlining ELT Processes: Optimizing your Delta Tables with Autoloader and SQL in Databricks

Image Source: https://en.wikipedia.org/wiki/Databricks

Efficient ELT (Extract, Load, Transform) processes are the backbone of data engineering, ensuring that data is reliably and promptly available for analysis. Using ELT vs ETL in Databricks allows you to load your data into Delta Tables first and then continue your transformations after. A key challenge is handling and processing large volumes of data, especially in dynamic environments where data is continuously generated. Databricks offers tools to streamline these processes, particularly through the use of Autoloader for batch streaming and Delta Lake for managing data. Here, we’ll explore how to optimize SQL queries specifically focusing on using Autoloader for batch streaming and then unioning all the Delta tables from multiple Autoloader jobs.

Understanding Autoloader and Delta Lake

Image Source: https://github.com/delta-io/delta

What is Autoloader and Delta Lake?

Autoloader simplifies and automates the process of incremental data ingestion in Databricks. It can efficiently process new data files as they arrive in cloud storage, making it ideal for both streaming and batch scenarios…