Real-Time Data Processing with Delta Live Tables: Use Cases and Best Practices for Databricks

After explaining Delta Live Tables (DLTs) in Databricks and how to incorporate them into data pipelines in my previous <a href="https://medium.com/@matthewsalminen/the-power-duo-databricks-auto-loader-and-delta-live-tables-e6b6bc0d2982" rel="noopener">post</a>, I wanted to take a deeper dive into some specific use cases of Delta Live Tables. <blockquote> What are Delta Live Tables again? Delta Live Tables, often abbreviated as DLTs, are used to manage real-time data pipelines. They can handle large volumes of data ingestion making them ideal for quick insights and analysis. </blockquote> Before I go into some practical use cases of DLTs, they’re many advantages for using DLTs in your pipelines and data strategy: Key Advantages of DLTs: <ul> <li>Real-Time: DLTs empower you to process and analyze data as it arrives, eliminating lag from batch streaming.</li> <li>Data Quality Assurance: With constraints and expectations, DLTs ensure the integrity and quality of your data.</li> <li>Multi-Hop Architecture: DLTs are within the medallion or multi-hop architecture, satisfying the multi-layer approach to data pipelines.</li> </ul> Here I provide two use cases for DLTs that may provide a lot of meaningful insights to your data streaming pipelines Use Case 1: Continuous ETL for Streaming Data Sources DLTs can be used for processing and transforming streaming data sources. This will allow you to handle high-velocity data streams, ensuring data quality and reliability. Here is a brief example with python, albeit in Databricks the need to initialize a spark session is not required but I have included for non-databricks users: <a href="https://matthewsalminen.medium.com/real-time-data-processing-with-delta-live-tables-use-cases-and-best-practices-for-databricks-2009a9a6fc16">Learn More</a>