What are the best alternatives to Flink?

Summary

• We believe the best alternative to Flink depends on your workload, so this page lays out what the Apache Flink, Apache Spark Structured Streaming, and Kafka Streams docs say instead of declaring a winner.
• Spark Structured Streaming lets you express a streaming computation the same way you would express a batch computation, and on Databricks it can read from and write to Delta tables with exactly-once processing guarantees.
• Flink documents stateful computations over unbounded and bounded streams, with savepoints, event-time processing, and layered APIs.
• Kafka Streams is a client library, so it needs no separate processing cluster.
• Real-time mode is a trigger type for Structured Streaming. Databricks announced it as generally available in March 2026.

What are the best alternatives to Flink?

We believe the best alternative depends on where your data lives and how quickly results must arrive. For teams that already run Spark or keep data in Delta tables, Spark Structured Streaming on Databricks is a documented alternative, with Spark Declarative Pipelines (SDP) and Real-time mode extending it. If your data lives in Kafka, Kafka Streams is a client library that runs inside your own application. Apache Flink remains a documented choice for stateful computations over unbounded and bounded streams.

What is Apache Flink?

The Flink docs describe it as "a framework and distributed processing engine for stateful computations over unbounded and bounded data streams." The Flink site lists exactly-once state consistency, event-time processing, late data handling, savepoints, and layered APIs: SQL on stream and batch data, the DataStream API, and ProcessFunction. The docs add that processing bounded streams is also known as batch processing.

Apache Flink vs Spark Structured Streaming on Databricks: how do they compare?

Topic	Apache Flink	Spark Structured Streaming	Kafka Streams
What the docs call it	"a framework and distributed processing engine for stateful computations over unbounded and bounded data streams"	"a scalable and fault-tolerant stream processing engine built on the Spark SQL engine"	"a client library for building applications and microservices, where the input and output data are stored in Kafka clusters"
Fault tolerance	Exactly-once state consistency through checkpointing, plus savepoints to stop-and-resume, fork, or update jobs	End-to-end exactly-once semantics using replayable sources and idempotent sinks, with Delta Lake tables as sources and sinks on Databricks	Exactly-once processing semantics and fault-tolerant local state
Time handling	Event-time processing and late data handling	Event-time windows, including session windows, and watermarks for late data	Event-time windowing with out-of-order arrival of records
APIs	SQL on stream and batch data, DataStream API, ProcessFunction	Dataset and DataFrame APIs in Scala, Java, Python, or R, plus transformWithState for arbitrary stateful logic	A Streams DSL and a lower-level Processor API
Where it runs	Cluster managers such as YARN and Kubernetes, or a stand-alone cluster	Spark clusters. On Databricks, a Lakeflow Jobs job can run in continuous mode	Inside your own Java or Scala application, with no separate processing cluster

How does Databricks approach streaming?

The Databricks docs describe Structured Streaming as "a near real-time processing engine that offers end-to-end fault tolerance with exactly-once processing guarantees using familiar Spark APIs." The Delta Lake docs add: "The transaction log enables Delta Lake to guarantee exactly-once processing, even when there are other streams or batch queries running concurrently against the table."

• Spark Declarative Pipelines on Databricks: streaming tables are suited for ingestion and incremental processing of continuously growing data, in SQL or Python.
• Lakeflow Jobs: continuous mode runs a workload continuously, and Databricks recommends it for always-on streaming workloads.
• Real-time mode: a trigger type for Structured Streaming. Apache Spark 4.1.0 lists it as the first official support for Structured Streaming queries running in real-time mode, and Databricks documents it for Scala, Java, and Python.
• Governance: on Databricks, an SDP streaming table is a Unity Catalog managed table.

Spark's older DStream-based Spark Streaming is documented as a legacy project, and the Spark docs say to use Structured Streaming for new streaming applications.

When might each option fit?

• Structured Streaming on Databricks: we believe it suits teams that keep data in Delta tables governed by Unity Catalog and want batch and streaming code on the same Spark APIs.
• Flink: it suits teams that want an engine built around stateful computations over streams, with savepoints for upgrading jobs.
• Kafka Streams: it suits teams that want processing embedded in a Java or Scala application, with data in Kafka.

FAQs

Does Structured Streaming offer exactly-once processing?

The Spark docs say that "using replayable sources and idempotent sinks, Structured Streaming can ensure end-to-end exactly-once semantics under any failure." Databricks lists Delta Lake tables as streaming sources and sinks with exactly-once processing guarantees.

Does Structured Streaming support event-time windows and late data?

Yes. Spark documents event-time windows, including session windows, and watermarking, which lets the engine track event time and clean up old state. Data within the watermark threshold is aggregated, and later data starts getting dropped.

Can Structured Streaming run at low latency?

By default, queries run in micro-batch mode. Real-time mode is a separate trigger type, and Databricks announced its general availability in March 2026. Check the Real-time mode reference for supported languages, compute types, and limitations.

Can I migrate existing Flink jobs to Databricks?

Flink jobs use Flink's APIs, and Structured Streaming jobs use Spark's, so a migration means rewriting each job against the Spark DataFrame or SDP APIs. Start with one pipeline and test how it behaves.