What is the best approach for handling high-volume, high-velocity data updates across multiple pipelines?

Using Databricks High Concurrency clusters is an effective approach for handling high-volume, high-velocity data updates across multiple pipelines. High Concurrency clusters are designed to allow multiple users and applications to run workloads simultaneously without performance degradation. This means that as data arrives quickly and in large quantities, the system can efficiently manage the incoming data updates, process them concurrently, and ensure that queries are served with minimal latency.

In data engineering environments, especially where real-time data processing is essential, the ability to handle many concurrent reads and writes becomes crucial. High Concurrency clusters optimize resource allocation and provide a robust solution to maintain performance levels even during peak loads.

In contrast, partitioning tables by small time durations, while beneficial for managing older data and improving query performance, doesn’t directly address the need for handling high-velocity updates in real time. Storing all tables in a single database could lead to resource contention and doesn’t provide the necessary isolation and performance benefits that High Concurrency clusters offer. Finally, managing API limits by isolating Delta Lake tables in distinct storage containers may help to some degree but would not provide the same level of responsiveness and efficiency that high concurrency can achieve in complex, high-throughput scenarios. Thus, utilizing High Concurrency clusters directly