Uploaded April 2026 | Updated September 2026, 2 weeks ago
In this technical walkthrough, Frank demonstrates the significant latency gains achieved by switching from traditional Micro-Batch Mode (MBM) to Real-Time Mode (RTM) in Spark Structured Streaming. Frank uses a demo created by Neil Patel.
The demo begins by deploying a Declarative Automation Bundle (DAB) in the workspace to configure the necessary environment, including volumes and automated jobs. Frank first establishes a baseline using MBM, where the data shows a P95 latency of over 1,000ms. By simply updating the trigger configuration to Real-Time Mode, the processing latency drops dramatically, bringing the P95 latency down to just 50ms.
The video concludes with a deep dive into the underlying code. This comparison serves as a guide for data engineers seeking ultra-low, sub-second latency in their Databricks streaming pipelines.
In this technical walkthrough, Frank demonstrates the significant latency gains achieved by switching from traditional Micro-Batch Mode (MBM) to Real-Time Mode (RTM) in Spark Structured Streaming. Frank uses a demo created by Neil Patel.
The demo begins by deploying a Declarative Automation Bundle (DAB) in the workspace to configure the necessary environment, including volumes and automated jobs. Frank first establishes a baseline using MBM, where the data shows a P95 latency of over 1,000ms. By simply updating the trigger configuration to Real-Time Mode, the processing latency drops dramatically, bringing the P95 latency down to just 50ms.
The video concludes with a deep dive into the underlying code. This comparison serves as a guide for data engineers seeking ultra-low, sub-second latency in their Databricks streaming pipelines.
