Seven Reasons to Use Apache Flink in IoT

Translated from the data Artisans blog: 7 Reasons to Use Stream Processing & Apache Flink in the IoT Industry.

This article by Freeport Metrics’ technology director discusses stream processing and Apache Flink in IoT. It originally appeared on the Freeport Metrics Blog.

Distinctive data-processing challenges make Flink a sound choice for IoT. These include:

• Devices produce far more data than users. Traditional databases can struggle with the volume of these streams.
• IoT users need timely data to decide what to do next immediately. ETL and batch processing do not satisfy these needs.
• Cellular connections carrying device data can be unreliable.

Freeport Metrics has used Flink in several IoT projects, including solar monitoring and billing, wind-farm data optimization, and a large-scale real-time RFID tracking platform. Here are seven reasons to use Apache Flink in IoT:

1. Real-Time Processing

IoT requires immediate insight into device behavior. A wind turbine may stop producing power despite wind, or valuable equipment may unexpectedly leave a factory. Both require prompt business action. Moving from batch to streaming fundamentally changes engineering thinking, enabling data-driven triggers, scheduled alerts, and event-pattern monitoring.

2. Event-Time Support

Mobile devices uploading over cellular networks introduce delays and transmission failures. Even a stable network cannot guarantee device availability or eliminate the effects of physical distance. For example, sensors on machines or automotive parts moving through a production line do not necessarily deliver events in order. Processing by event time is also more appropriate than using the time a server receives data. Event-time support is therefore essential when choosing a processing framework.

For a comparison of event, processing, and ingestion time, see event time in Apache Flink.

3. Tools for Messy Data

Special-case preprocessing is often the hardest part, particularly in IoT, where you cannot control source data. This leads to extensive cleanup code and exceptional handling. Streaming does not eliminate these problems, but it supplies useful tools. One is windowing, which groups an unbounded stream into datasets according to conditions such as time or count. Analog sensor or GPS readings can be noisy, but a window-processing function can simplify them by calculating averages. A power center may send frequent readings over an unreliable Modbus connection, producing accurate files only at midnight. Modbus readings can provide real-time estimates, while the nightly file produces an accurate bill. A trigger can close the active input/output window when the file arrives. When it is impossible to know whether all data has arrived, heuristic watermarks can trigger window output. They may reflect experience-based estimates or reception timeouts. Flink also supports allowed lateness and side outputs for late data.

4. Parallel Processing by Group

IoT often requires calculations for subsets of data. A cat-monitoring platform, for example, should give each owner only their own cat’s data. Flink’s grouping by key supports this, and groups can be processed in parallel for horizontal scalability. Keys need not be fixed to devices or locations: fleet management may group by GPS, hardware sensors, or registration details. We also recommend exploring data Artisans Streaming Ledger, which supports distributed transactions through shared state and tables across parallel streams, as implemented in River Edition.

5. Local-State Performance

Hardware and infrastructure advances change our expectations of latency, but some principles remain:

• The sooner data arrives, the sooner it can be processed.
• Disk I/O costs performance.

Apache Flink maintains correctness with local state. Lightweight checkpointing provides fault tolerance while optimizing I/O. Local state is more than a read-only cache: its benefits become clear when new data continuously updates it. Some even question whether a separate storage layer is needed when Flink serves as the sole ingestion point. For more, see Viktor Klang of Lightbend’s discussion of streaming and microservice convergence.

6. Message-Queue Support

Streaming often goes together with scalable, reliable messaging systems such as Apache Kafka, AWS Kinesis, and RabbitMQ. Flink supports producing and consuming through them and uses their distributed partitioning and sharding capabilities for performance. Where required, end-to-end exactly-once guarantees extend to these external systems. An earlier post explains how Apache Flink manages Kafka consumer offsets.

7. Simpler Processing After the Learning Curve

Finally, a stream-processing framework should feel natural once adopted. Learning Flink and operator parallelism may initially be challenging, but familiarity lets a team focus on core business logic while the framework handles repetitive infrastructure work. At Freeport Metrics, moving from batch to Flink streaming resembled many technology migrations. Eventually, you recognize that it is the right tool and wish you had adopted it sooner.

These experiences reinforce our belief that IoT has a real need for stream-processing frameworks such as Apache Flink. Its features, connectors, fault tolerance, and reliability make it a strong option for processing large data streams.