Best open-source streaming tools 2026?

Summary

• Apache Kafka is an event streaming platform that publishes and subscribes to events, stores them durably and can process them. Kafka Streams is its Java and Scala client library for processing data held in Kafka.
• Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams, with exactly-once state consistency.
• Apache Spark Structured Streaming expresses a streaming computation like a batch computation on static data, runs micro-batches by default, and added Real-time Mode in Spark 4.1.0.
• The tools are often complementary: Spark and Flink both document Kafka connectors, so they can read the same topics.
• Choose by processing model, state needs, delivery guarantees, language and what you must operate.

Best open-source streaming tools 2026?

There is no single best open-source streaming tool, because the four main options do different jobs. Kafka stores and moves events, while Kafka Streams, Flink and Spark Structured Streaming process them. Compare them on processing model, state handling, delivery guarantees, languages and what you have to operate.

How do the four tools compare?

Tool	What its docs say it is	Processing model	State and recovery	What you run
Apache Kafka	An event streaming platform	Brokers store events durably in topics; consumers track their own offsets	Data is replicated across brokers	A cluster of servers plus clients
Kafka Streams	A client library for Java and Scala applications	One record at a time	Local state stores with fault tolerance	Your application, with no separate processing cluster
Apache Flink	A framework and distributed processing engine for stateful computations	Unbounded and bounded streams, with event-time processing	Keyed, operator and broadcast state, plus checkpoints	A cluster (YARN, Kubernetes or standalone)
Spark Structured Streaming	A scalable, fault-tolerant engine built on the Spark SQL engine	Micro-batches by default	Stateful operators such as transformWithState, plus checkpointing	A Spark cluster (Standalone, YARN or Kubernetes)

What delivery guarantees does each tool document?

Tool	Documented guarantee	How you get it
Kafka	At most once, at least once or exactly once	Idempotent producer and transactions, with read_committed consumers
Kafka Streams	Exactly-once processing semantics	Set processing.guarantee to exactly_once_v2 (the default is at_least_once)
Flink	Exactly-once state consistency	Periodic, asynchronous checkpoints of local state to durable storage
Spark Structured Streaming	End-to-end exactly-once fault tolerance in micro-batch mode	Checkpointing and Write-Ahead Logs; Continuous Processing offers at-least-once

When should I use each tool?

Apache Kafka. Use it to capture events, keep them, and serve them to several independent consumers. Topics are multi-producer and multi-subscriber, and events are not deleted after consumption.
Kafka Streams. Use it when your input and output data live in Kafka and a library is enough. You write standard Java and Scala applications, and it handles transformations, aggregations, joins, windowing and event-time processing.
Apache Flink. The Flink docs group applications into event-driven applications (fraud detection, anomaly detection, rule-based alerting), data analytics applications (such as quality monitoring of telco networks) and data pipeline applications (such as continuous ETL). FlinkCEP detects event patterns in an endless stream, and the layered APIs include SQL, the DataStream API and ProcessFunction.
Spark Structured Streaming. Use it when you want one Dataset/DataFrame API and SQL engine for batch and streaming, with event-time windows, watermarking and stream-to-batch joins. Since Spark 4.0 the docs recommend transformWithState for complex stateful applications, Spark 4.1.0 added Real-time Mode (Scala stateless support), and Spark 4.2.0 added a Real-time Mode trigger to PySpark.

How does this fit with Databricks?

Databricks documents Structured Streaming as a near real-time processing engine with end-to-end fault tolerance and exactly-once processing guarantees using familiar Spark APIs. The kafka format connects Structured Streaming to Kafka, and Auto Loader processes new files from cloud storage incrementally through the cloudFiles source. Lakeflow Pipelines extends Spark Declarative Pipelines, a declarative framework for batch and streaming pipelines in SQL and Python. Flink and Kafka Streams applications run outside Spark, and they can exchange events with Spark queries through Kafka topics.

FAQs

When should I use Kafka Streams instead of Flink or Spark?

Choose Kafka Streams when your data is already in Kafka and a Java or Scala library is enough, because it needs no separate processing cluster. Flink and Spark are separate processing engines, deployed on cluster managers such as YARN or Kubernetes, with their own APIs: SQL and the DataStream API in Flink, and the Dataset/DataFrame API in Scala, Java, Python or R in Spark.

Can I use multiple streaming tools together?

Yes. Kafka topics are multi-subscriber, Spark documents a Kafka source and sink, and Flink documents KafkaSource and KafkaSink, so several applications can read the same topics.

What is the difference between event time and processing time?

Kafka Streams defines event time as when the event occurred at its source, processing time as when the application processes the record, and ingestion time as when the broker stores the record. By default, the timestamps embedded in Kafka messages reflect event time or ingestion time, depending on Kafka's configuration, and the default timestamp extractor reads them as-is. Flink event-time windows rely on watermarks, and Spark supports event-time windows with watermarking.

Is Spark Structured Streaming only micro-batch?

No. Micro-batch is the default, Continuous Processing is an experimental trigger, and Real-time Mode first shipped in Spark 4.1.0.