Re-envisioning microservices as Flink streaming purposes
A standard approach to course of knowledge is to drag it out of Kafka utilizing a microservice, course of it utilizing the identical or doubtlessly a unique microservice, after which dump it again into Kafka or one other queue. Nevertheless, you need to use Flink paired with Kafka to do all the above, yielding a extra dependable resolution with decrease latency, built-in fault tolerance, and occasion ensures.
Confluent
Flink will be set to pay attention for knowledge coming in, utilizing a steady push course of slightly than a discrete pull. As well as, utilizing Flink as an alternative of a microservice allows you to leverage all of Flink’s built-in accuracies, reminiscent of exactly-once semantics. Flink has a two-phase commit protocol that allows builders to have exactly-once occasion processing ensures end-to-end, which implies that occasions entered into Kafka, for instance, can be processed precisely as soon as with Kafka and Flink. Word that the kind of microservice that Flink finest replaces is one associated to knowledge processing, updating the state of operational analytics.
Use Flink to rapidly apply AI fashions to your knowledge with SQL
Utilizing Kafka and Flink collectively permits you to transfer and course of knowledge in actual time and create high-quality, reusable knowledge streams. These capabilities are important for real-time, compound AI purposes, which want dependable and available knowledge for real-time decision-making. Suppose retrieval augmented technology (RAG) sample, supplementing no matter mannequin we use with right-in-time, high-quality context to enhance the responses and mitigate hallucinations.
