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In today's data-driven world, the ability to efficiently process and analyze real-time data streams is becoming increasingly crucial for building modern applications. Redpanda, a streaming platform built on the Apache Kafka protocol, offers developers a powerful and scalable solution for handling high-volume streaming data.
As a Developer Advocate at Redpanda, I often get questions from developers asking,
If you’re familiar with Apache Kafka®, then you might have encountered a Kafka-compatible schema registry—a separate component that you deploy outside of your Kafka cluster, since Kafka itself doesn’t have one built-in.
Essentially, a schema is a logical description of how your data is organized, and so a schema registry provides a central repository for those schemas, allowing producers and consumers to seamlessly send and receive data between them. For event-driven architectures, this can become complex and difficult to manage as you scale, since data schemas can change and evolve over time (potentially breaking things down the line).
A message might have a limited lifetime, and if it is not processed within this period, it is useless and should be discarded.
For example, a pizza order is useless when its maximum wait time is passed. The customer might have considered ordering from another shop. Also, in a highly dynamic environment like stock trading, a BUY order must be processed within seconds. If not, the order will be irrelevant as the prices can fluctuate.
Unbounded data refers to continuous, never-ending data streams with no beginning or end. They are made available over time. Anyone who wishes to act upon them can do without downloading them first.
As Martin Kleppmann stated in his famous book, unbounded data will never “complete” in any meaningful way.