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

<p>After explaining Delta Live Tables (DLTs) in Databricks and how to incorporate them into data pipelines in my previous&nbsp;<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.</p> <blockquote> <p><strong>What are Delta Live Tables again?&nbsp;</strong>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.</p> </blockquote> <p>Before I go into some practical use cases of DLTs, they&rsquo;re many advantages for using DLTs in your pipelines and data strategy:</p> <p><strong>Key Advantages of DLTs:</strong></p> <ul> <li><strong>Real-Time:&nbsp;</strong>DLTs empower you to process and analyze data as it arrives, eliminating lag from batch streaming.</li> <li><strong>Data Quality Assurance:</strong>&nbsp;With constraints and expectations, DLTs ensure the integrity and quality of your data.</li> <li><strong>Multi-Hop Architecture:</strong>&nbsp;DLTs are within the medallion or multi-hop architecture, satisfying the multi-layer approach to data pipelines.</li> </ul> <p>Here I provide two use cases for DLTs that may provide a lot of meaningful insights to your data streaming pipelines</p> <p><strong>Use Case 1:</strong>&nbsp;<em>Continuous ETL for Streaming Data Sources</em></p> <p>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:</p> <p><a href="https://matthewsalminen.medium.com/real-time-data-processing-with-delta-live-tables-use-cases-and-best-practices-for-databricks-2009a9a6fc16"><strong>Learn More</strong></a></p>