Apache Flink
Also known as: Flink
Apache Flink is an open-source engine for processing continuous streams of data as events arrive, rather than in scheduled batches.
Companies use it for fraud detection, live dashboards, recommendations, and alerting. While Apache Spark is better known, Flink is often the stronger choice when results must be accurate within milliseconds to seconds and the system must remember a lot of state along the way.
Apache Flink, explained at three levels
At home
Think of counting fish in a river. You could dam it, drain the pond once a day, and count everything, or stand on the bank and count each fish as it swims past, keeping a running tally. Apache Flink is the second approach for data: it processes each event, such as a payment or a click, the moment it arrives.
At work
Apache Flink is open-source software that processes continuous streams of data as events arrive, instead of in scheduled batches. It can count, join, and analyze events within milliseconds to seconds while remembering what came before. Businesses care because it enables decisions in the moment, such as blocking a suspicious card payment while it's happening or updating a live delivery estimate.
At home
A Flink job is a chain of steps: read events, transform them, and send results somewhere. What makes it powerful is state, memory the job keeps between events, such as how many purchases a card made in the last hour. Windows group events by time, like five-minute totals. Combining new events with state and windows lets Flink spot patterns, such as five purchases in five cities within ten minutes.
At work
Flink jobs are written in Java, Python, or SQL and run on a cluster where a JobManager coordinates TaskManagers that do the work. They usually read from and write to Kafka or similar streaming platforms. Event-time processing uses watermarks, markers saying how far along in time the stream has progressed, to handle late-arriving data correctly. With checkpoints and transactional outputs, Flink can deliver exactly-once results. It processes batch data too.
At home
Flink takes checkpoints, consistent snapshots of all its state, while the job keeps running. It does this by sending markers called barriers through the stream; when every step has seen a barrier, their combined state forms a consistent picture. After a failure, Flink restores the last checkpoint and replays events from that point. In my view, those checkpoints are what make streaming trustworthy enough for money-related decisions.
At work
Operating Flink means managing state size, held in RocksDB or, since Flink 2.0, optionally in remote storage for cloud deployments; checkpoint duration; backpressure when downstream systems slow; and watermark tuning for late events. Upgrading stateful jobs requires savepoints and compatible state schemas. Managed services from Confluent, AWS, and Ververica reduce the burden. Before choosing Flink, confirm the business needs sub-minute results, since micro-batch tools or warehouse streaming features are simpler for less demanding cases.
Real-world examples
At home
Nadia was curious how ride-sharing apps update surge pricing so quickly. In a streaming course, she built a small Flink job that counted ride requests per neighbourhood every minute, a simplified version of the same idea.
At work
Jerome works on fraud prevention at a Canadian credit union. Their old system flagged fraud the next morning. A Flink pipeline now scores each card transaction within a second, catching stolen cards before a second purchase goes through.
How it compares
| feature | Apache Flink | Apache Spark |
|---|---|---|
| Processing model | True streaming, event by event | Batch first, streaming in micro-batches |
| Typical latency | Milliseconds to seconds | Seconds or more for streaming |
| State handling | Large, managed state | Supported, less central |
| Strongest use | Real-time applications | Large batch analytics and ML |
Flink is built for streams. Spark is built for batches and handles streams too.
For analysts
Real-time processing is expensive to build and run. I'd identify the decision that must happen in seconds, like stopping fraud, and quantify its value before approving a streaming platform.
History & origin
Flink began as Stratosphere, a research project at the Technical University of Berlin and other European universities around 2010. It entered the Apache incubator in 2014 and became a top-level project that December. Its creators founded Data Artisans, which Alibaba acquired in 2019 and renamed Ververica; Alibaba became a major Flink user. Confluent, the Kafka company, acquired Flink startup Immerok in 2023 and launched a managed Flink service, and AWS offers one too. Flink 2.0 arrived in 2025, separating state storage from computing for cloud deployments. In my opinion, Flink's rise reflects a broader truth: businesses increasingly want to react to events, not read about them tomorrow.