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Articles by Vijayan Prabhakaran
Examining Versionless Apache Spark™: AI-powered upgrades and seamless stability for 2 billion workloads
Upgrading Apache Spark™ has never been easy. Every major version brings performance improvements, bug fixes, and new features, but getting there is painful. Most Spark users know the drill. Workloads break, APIs change, and developers can spend weeks fixing jobs just to catch up. This results in new features, performance improvements, and bug and security fixes taking significantly longer to adopt. At Databricks, we wanted to remove this friction entirely.
Announcing General Availability of Predictive Optimization
We're excited to announce the General Availability of Databricks Predictive Optimization. This capability intelligently optimizes your table data layouts for faster queries and reduced storage costs. Predictive Optimization harnesses Unity Catalog and is powered by the Data Intelligence Engine to determine the best optimizations to perform on your data and run those operations automatically on serverless infrastructure.
Introducing Predictive Optimization: Faster Queries, Cheaper Storage, No Sweat
We’re excited to announce the Public Preview of Databricks Predictive Optimization. This capability intelligently optimizes your table data layouts for improved performance and cost-efficiency. Predictive Optimization leverages Unity Catalog and Lakehouse AI to determine the best optimizations to perform on your data, and then runs those operations on purpose-built serverless infrastructure.
How Databricks improved query performance by up to 2.2x by automatically optimizing file sizes
Optimizing table file sizes has long been a necessary but complicated task for data engineers. Getting to the right file size for your tables unlocks significant performance improvements, but has traditionally required in-depth expertise and significant time investment. Recently, we announced Predictive I/O for Databricks SQL, which makes point lookups faster and more cost-effective.
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