Native Execution Engine available at no additional cost!
We’re thrilled to announce that the Native Execution Engine is now available at no additional cost, unlocking next-level performance and efficiency for your workloads.
What’s New?
The Native Execution Engine now supports Fabric Runtime 1.3, which includes Apache Spark 3.5 and Delta Lake 3.2. This upgrade enhances Microsoft Fabric’s Data Engineering and Data Science workflows, offering boosts in performance and flexibility.
Easy Activation at No Cost
You can enable the Native Execution Engine in just a few clicks—either at the environment level or for individual notebooks or jobs. Once enabled at the environment level, all associated jobs and notebooks will automatically inherit the configuration. Simply navigate to the “Acceleration” tab to get started, making activation seamless and hassle-free.
Compatible with Apache Spark APIs
One of the most exciting aspects of the Native Execution Engine is its full compatibility with Apache Spark APIs. Whether you’re using PySpark, Scala, R, or Spark SQL, you can continue working with your existing code without any modifications. Just activate the engine and enjoy the performance improvements immediately.
Optimized for Complex Workloads
The Native Execution Engine shines in several key areas, particularly when working with Parquet and Delta formats, as well as handling complex transformations and aggregations. Its columnar processing and vectorization capabilities make it ideal for computationally intensive queries, offering significant speed-ups for heavy workloads.
Why Enable It Now?
By enabling the Native Execution Engine, you’ll see performance improvements without any additional costs. Faster job execution means you’ll get more done while paying less for the same amount of work—essentially giving you better efficiency and value for your workloads.
Try It Out
We encourage you to check out documentation and try out the Native Execution Engine today and let us know your thoughts. You can enable it at no extra cost and immediately start benefiting from faster job execution.
For a closer look, check out the demo and see these features in action:
