I am trying to implement a real-time AI pipeline where data from multiple IoT sensors needs to be transformed into features for a machine learning model. How can I use Kafka and Kafka Streams to maintain a rolling window of features? Is it better to push these features into a database or serve them directly from a Kafka state store for low-latency inference?
3 answers
To build a real-time feature store, Kafka Streams is the ideal tool because of its stateful processing capabilities. You can use KTable or Windowed aggregates to compute features like "average temperature over the last 5 minutes" directly as events flow through the topic. For ultra-low latency, I recommend using Kafka's Interactive Queries to serve these features directly from the local RocksDB state stores. This avoids the "database hop" latency. However, if multiple microservices need the same features, sinking them into a high-performance NoSQL database like Cassandra or Redis via Kafka Connect is a more scalable long-term architecture for enterprise AI environments.
How do you handle schema evolution in this pipeline? If the sensor data format changes, does the Kafka Streams application require a full state reset or can it handle the transition?
We use Kafka Connect to stream features into a Redis cache. It allows our ML model running in a separate Python service to fetch pre-computed features in under 5ms.
That’s a solid approach, Justin. Using Redis as a sink provides a nice decoupling between the data engineering team managing Kafka and the data scientists consuming the features in Python.
Brandon, the best way to manage this is by using the Confluent Schema Registry with Avro or Protobuf. By enabling forward and backward compatibility, your Kafka Streams app can process different versions of the schema simultaneously. If you make a breaking change, you might need a new consumer group and a re-process of the data, but for most AI feature updates, the Schema Registry allows you to evolve without a full system wipe, which is crucial for 24/7 real-time pipelines.