Streaming data processing: does your company need it
What streaming data processing is, how it differs from batch processing, and in which real situations it is a justified choice.
Streaming data processing comes up regularly in conversations about modern data architecture. Apache Kafka, Apache Flink, event streams, real-time analytics. It sounds technically complex and - as often happens - it sometimes gets applied where there is no actual need for it.
I will explain what it is in plain terms, and - more importantly - how to decide whether your company needs it or not.
Batch versus streaming: what is the difference
Most traditional analytical systems work in batches. Data accumulates somewhere in a source, then once a day (or once an hour, or once a week) it is loaded into storage, transformed, and made available for analysis. The nightly ETL process is the classic example of batch processing.
Streaming processing works differently: data is processed as it arrives, in real time or close to it. An event happens, and within seconds or minutes it is already reflected in the system.
This is not better or worse than batch processing. It is a solution for a different class of problems.
When streaming processing is needed
A real need for streams arises in a few specific situations.
When data latency affects a business decision. If a decision is made immediately - for example, approve a transaction, show a personalised offer, react to an anomaly - a delay of a day or even an hour makes the data useless. In that case, batch processing does not solve the problem.
When the volume of events is too large for periodic loading. If your system generates millions of events per second, trying to load them in a batch once a day creates a bottleneck. Streams allow continuous processing without accumulation.
When you need to react to patterns in real time. Fraud detection, industrial equipment monitoring, traffic management - tasks where it matters to identify an anomaly now, not in tomorrow morning's report.
When streaming processing is not needed
If your analytical reports update once a day and that is enough for decision-making - you do not need streams. Batch processing is cheaper, simpler to maintain, and less sensitive to failures.
If your data volumes are moderate - a few million records per day - batch processing handles it without problems.
If your team has no experience with streaming systems - starting with Kafka or Flink will create an operational burden disproportionate to the value.
Streaming processing is operationally more complex than batch processing. Debugging, monitoring, ensuring correctness through failures - all of this requires expertise that most small teams simply do not need.
A practical test for making the decision
Before considering streaming processing, answer three questions:
- What specific business decision is made immediately and based on fresh data? (If no such decision exists - streams are not needed.)
- What is the acceptable latency for this data - a minute, an hour, a day? (If a day is sufficient - this is a batch processing task.)
- Does the team have experience operating streaming systems, or is this something to learn from scratch? (If from scratch - estimate the time and resources realistically.)
Streaming processing is a valuable technology for the right class of problems. But the right class of problems is rarer than technical articles make it seem.