Apache Spark introduces Real-Time Mode (RTM) for Structured Streaming, enabling sub-second latency without requiring a second engine like Apache Flink.
- •RTM uses three core innovations: continuous data flow, pipeline scheduling, and streaming shuffle to achieve millisecond-level event processing
- •Benchmarks show Spark RTM delivers latency up to 92% faster than Apache Flink on feature computation workloads including stateless transforms, stream-table joins, and GroupBy aggregations
- •Teams can switch between batch and ultra-low-latency streaming with a single-line code change using .trigger(RealTimeTrigger.apply())
- •RTM eliminates "logic drift" by allowing the same Spark API for both ML model training and live inference, removing the need for a separate Flink codebase
- •Early adopters include a digital asset platform achieving fraud detection feature updates in under 200ms and DraftKings powering real-time sports betting fraud detection
Databricks