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 <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>
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<p><strong>What are Delta Live Tables again? </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>
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<p>Before I go into some practical use cases of DLTs, they’re many advantages for using DLTs in your pipelines and data strategy:</p>
<p><strong>Key Advantages of DLTs:</strong></p>
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<li><strong>Real-Time: </strong>DLTs empower you to process and analyze data as it arrives, eliminating lag from batch streaming.</li>
<li><strong>Data Quality Assurance:</strong> With constraints and expectations, DLTs ensure the integrity and quality of your data.</li>
<li><strong>Multi-Hop Architecture:</strong> DLTs are within the medallion or multi-hop architecture, satisfying the multi-layer approach to data pipelines.</li>
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<p>Here I provide two use cases for DLTs that may provide a lot of meaningful insights to your data streaming pipelines</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>Read More</strong></a></p>