Hi, I have raised a ticket SPARK-48117 <https://issues.apache.org/jira/browse/SPARK-48117> for enhancing Spark capabilities with Materialised Views (MV). Currently both Hive and Databricks support this. I have added these potential benefits to the ticket
-* Improved Query Performance (especially for Streaming Data):* Materialized Views can significantly improve query performance, particularly for use cases involving Spark Structured Streaming. When dealing with continuous data streams, materialized views can pre-compute and store frequently accessed aggregations or transformations. Subsequent queries on the materialized view can retrieve the results much faster compared to continuously processing the entire streaming data. This is crucial for real-time analytics where low latency is essential. *Enhancing Data Management:* They offer a way to pre-aggregate or transform data, making complex queries more efficient. - *Reduced Data Movement*: Materialized Views can be materialized on specific clusters or storage locations closer to where the data will be consumed. This minimizes data movement across the network, further improving query performance and reducing overall processing time. - *Simplified Workflows:* Developers and analysts can leverage pre-defined Materialized Views that represent specific business logic or data subsets. This simplifies data access, reduces development time for queries that rely on these views, and fosters code reuse. Please have a look at the ticket and add your comments. Thanks Mich Talebzadeh, Technologist | Architect | Data Engineer | Generative AI | FinCrime London United Kingdom view my Linkedin profile https://en.everybodywiki.com/Mich_Talebzadeh Disclaimer: The information provided is correct to the best of my knowledge but of course cannot be guaranteed . It is essential to note that, as with any advice, quote "one test result is worth one-thousand expert opinions (Werner Von Braun)".
