Which principle is essential for both batch and streaming data processing in Databricks?

ACID transactions are crucial when dealing with both batch and streaming data processing in Databricks. ACID stands for Atomicity, Consistency, Isolation, and Durability. These principles ensure that all transactions are processed reliably, thus maintaining data integrity.

In the context of Databricks, ACID transactions help manage concurrent data operations effectively. For batch processing, this means that multiple jobs can be executed without compromising the consistency of the data. In streaming scenarios, ACID transactions provide a mechanism to ensure that updates to the data stream can be safely committed, avoiding issues like data loss or corruption, especially during system failures or unexpected interruptions.

Overall, the implementation of ACID transactions allows Databricks to manage complex data workflows smoothly, ensuring that both real-time and historical data analyses yield accurate results. This capability is fundamental to maintaining trust in data processing pipelines across various applications, making it a central principle in Databricks' architecture for handling both batch and streaming data.