Which two actions are recommendable for populating data stores with Delta tables optimized for performance?

The recommended actions for populating data stores with Delta tables optimized for performance include establishing a lakehouse architecture and utilizing dataflows. A lakehouse combines the features of data lakes and data warehouses, enabling better management of big data and support for analytics workloads. By integrating dataflows within the lakehouse, users can efficiently manage ETL (Extract, Transform, Load) processes, automate data ingestion, and ensure that data is continuously updated, leading to improved performance in querying Delta tables.

Dataflows facilitate the orchestration of data movement and transformations, which is crucial for maintaining an optimized Delta table environment. Leveraging a lakehouse framework ensures that performance enhancements such as efficient caching, better schema management, and advanced optimization strategies can be effectively utilized.

Other choices, like creating partitioned tables or using specific data formats, focus on specific aspects of performance optimization but do not provide the broader architectural approach and seamless data integration offered by a lakehouse combined with dataflows.