Apache Spark Streaming Real-Time Mode - Latency Demo
Key Takeaways
Demonstrates Apache Spark Streaming Real-Time Mode for low-latency data processing with significant latency gains
Original Description
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.
Watch on YouTube ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
Related Reads
📰
📰
📰
📰
I Built My Second ETL Pipeline. This Time, I Started Thinking Like a Data Engineer
Towards Data Science
JuiceFS Sync for PB-Scale Data Transfers: Resumable Sync, Encryption, and Bandwidth Control
Dev.to AI
How Airflow is using AI to make data engineering more resilient, not more complex
Medium · AI
What Can We Do When Memory Becomes the New Bottleneck in Data Engineering?
Towards Data Science
🎓
Tutor Explanation
DeepCamp AI