Spark's Real-Time Mode operates as an alternate trigger on Structured Streaming queries, letting teams shift workloads away from micro-batching without rewriting code. In benchmark testing, the engine matched or surpassed Apache Flink across both stateless and stateful low-latency workloads. Enterprise adopters like MakeMyTrip have leveraged the technology to avoid running multiple engines while cutting p99 latency from over a minute to roughly 500 milliseconds. Technical requirements for the feature include an update output mode combined with Kafka or row-level foreach sinks.
Key points
Production adopters including Coinbase, DraftKings, and MakeMyTrip use the new capability…: Production adopters including Coinbase, DraftKings, and MakeMyTrip use the new capability to simplify operational overhead.
Workloads can transition between micro-batch and real-time tiers without code rewrites us…: Workloads can transition between micro-batch and real-time tiers without code rewrites using an alternate trigger on Structured Streaming queries.
Stateful operators currently available on Databricks are planned for release to open-sour…: Stateful operators currently available on Databricks are planned for release to open-source users in Apache Spark 4.3.
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How Spark Real-Time Mode changes the streaming decision, with latency benchmarks, a feature comparison and a guide to choosing an engine.
For most teams, Apache Spark™ is the better choice. Spark runs batch ETL, SQL analytics, machine learning and streaming on one engine, and Real-Time Mode now brings Spark streaming to millisecond, end-to-end latency. Apache Flink is a stream-native engine that fits specialized event-processing workloads, but choosing it usually means running a second engine next to Spark.
The latency gap used to be the main reason teams added Flink. With Real-Time Mode, that reason has largely gone away, and the real question becomes how many engines you want to build, operate and govern.
Spark is a unified engine for batch, streaming, SQL and AI, and Flink is a specialized stream processor. Both are open source Apache Software Foundation projects with exactly-once processing, stateful operators and SQL APIs. The difference is scope.
Spark started as a batch engine and added Structured Streaming, which treats a stream as a table that keeps growing. The same DataFrame and SQL code runs as a…
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