What technology has Databricks introduced to accelerate query-based workloads?

The appropriate choice is Photon, which is a query execution engine introduced by Databricks to enhance the performance of data processing and analytics workloads. Photon is designed to operate in a columnar format, leveraging advanced hardware and CPU optimizations to speed up query execution. This technology allows for efficient processing of data, making it particularly beneficial for complex analytical queries that require high throughput and low latency.

In the context of query acceleration, Photon optimizes operations such as joins, aggregations, and filters, which are common in big data scenarios. Its integration with Apache Spark and the Delta Lake architecture enables it to significantly accelerate query performance, making it ideal for users who require rapid insights from large datasets.

Other options mention significant features and technologies within the Databricks ecosystem but serve different purposes. The Delta Engine, for instance, refers to performance optimizations for structured data management, while the Apollo Framework outlines a set of tools and practices for model lifecycle management. Unity Catalog relates to data governance and access control across data assets rather than direct query acceleration.