Flink is for true 'sub-second' streaming with complex state. Spark (Structured Streaming) is the choice for unified batch/stream processing with existing Spark talent.
I've shipped pipelines on both Apache Flink and Spark Structured Streaming and the real comparison isn't just "native streaming vs micro-batch." It's about which ecosystem you already live in and how strict your latency bar really is. Flink is the specialist — if you need true event-by-event, stateful, low-latency processing, it's the right tool and nothing else comes close. Spark Structured Streaming is the generalist — if you already run Spark for batch, your team knows DataFrames, and you can tolerate 1–10 second latency, using Spark for streaming too is pragmatic.
At a fintech team I worked with, we needed to detect fraudulent transactions in under 200ms with complex session-based rules. Spark Structured Streaming's micro-batch floor (~500ms best case in most setups) was a hard stop. We built the pipeline on Flink — DataStream API, event-time processing with watermarks, keyed state for per-user rolling risk scores. p99 latency came in at 80ms, exactly-once guarantees held through broker failovers. Flink was non-negotiable for this workload.
At a different team doing hourly aggregations for executive dashboards, we were about to build it on Flink out of habit and caught ourselves: the latency requirement was "refresh every 5 minutes" and the team had deep Spark/Databricks skills. Spark Structured Streaming landed the same logic in half the code, ran on the same Databricks cluster as the batch jobs, and our on-call runbook was shorter. Flink would have been technically better and operationally wasteful. Match the tool to the requirement, not to your resume.
Choose Apache Flink if:
Let the latency requirement decide first. Under 500ms p99 — Flink. Over a few seconds — Spark Structured Streaming unless you have a specific Flink reason (complex state, watermarks, out-of-order handling). Between 500ms and a few seconds, tiebreaker is team knowledge: Flink if you have or can acquire streaming expertise; Spark if you already run Databricks or Spark batch. Don't default to Flink because it sounds cooler; don't default to Spark Streaming because it's what you know — the wrong choice in either direction has real operational cost.
The core architectural difference is native streaming vs micro-batch. Flink processes events one at a time as they arrive, enabling true sub-second latency and sophisticated event-time semantics. Spark Structured Streaming collects events into small batches (typically 200ms–several seconds) and runs a Spark query over each batch. Flink is better for low-latency stateful workloads; Spark is better for "seconds latency" workloads where the unified batch/stream programming model and ecosystem integration matter more than peak latency.
It depends on what you mean by real-time. For "seconds-of-latency" use cases (dashboards, aggregations, slow metrics), yes — Spark Structured Streaming works well with typical latencies in the 1–10 second range. For true "sub-second" or "milliseconds-of-latency" use cases (fraud detection, ad bidding), Spark's micro-batch floor is limiting. Spark has a Continuous Processing mode that achieves ~1ms latency but with significant feature restrictions and limited production usage. For serious low-latency work, Flink is the mainstream choice.
Spark Structured Streaming, for most teams. The API is the same DataFrame/SQL API as batch Spark, so batch Spark developers can build streaming jobs immediately. Flink has multiple APIs (DataStream, Table, SQL) with different abstractions and semantics (especially around event time, watermarks, keyed state). Flink's learning curve is steeper, the documentation is less forgiving, and finding StackOverflow answers is harder. Once mastered, Flink is more expressive for complex streaming logic — but that's a multi-quarter investment.
Delta Lake has first-class integration with Spark Structured Streaming — both as a source (read a Delta table as a stream of changes via readStream) and as a sink (writeStream to append micro-batches to a Delta table). This is one of the big reasons teams on Databricks use Spark Streaming over Flink: the end-to-end Spark → Delta → Spark Structured Streaming story is seamless. Flink can write to Delta via community connectors, but the integration is less polished and the connector surface area is smaller.
Both can write to Snowflake — Spark via the Snowflake Spark connector (batch writes per micro-batch), Flink via the Snowflake sink connector. A common modern pattern is streaming ingestion into Snowflake via Snowpipe Streaming (their high-throughput ingestion API) fed from Kafka directly, which avoids running Flink/Spark just for ingestion. For true streaming transformations (not just ingestion), you'd use Flink or Spark Structured Streaming as the processor and Snowflake as the analytical sink.
There are several: AWS managed Flink (formerly Kinesis Data Analytics for Apache Flink), Ververica Platform (from the Flink founders, now part of Alibaba), Confluent Cloud Flink, and Databricks is adding Flink support too. Managed services dramatically reduce Flink's operational overhead — you write the job, they run the infrastructure. For new Flink adoption in 2026, start with a managed service unless you have a specific reason to self-host.
Yes, and it's a reasonable pattern. Use Spark Structured Streaming for the 80% of streaming jobs with seconds-latency requirements (often co-located with batch Spark jobs on the same cluster), and use Flink for the 20% of jobs that need sub-second latency or sophisticated stateful logic. The operational cost of running both frameworks is real (two platforms to monitor, two sets of expertise), but so is the cost of forcing the wrong tool on every workload. Decide based on workload count and expected growth.