Kafka Streams is an open-source stream processing library developed by the Apache Kafka community. It allows developers to build real-time, fault-tolerant, and scalable applications and microservices that process streaming data.
Kafka Streams is built on top of the Apache Kafka distributed streaming platform. It provides a high-level API that makes it easier to create applications that consume, process, and produce data streams. Unlike traditional batch-processing frameworks, operates on continuous data, processing records one at a time as they arrive.
Some key features of Kafka Streams include:
Distributed and Fault-Tolerant
Processing: Kafka Streams automatically distributes the processing load across multiple instances of an application, providing scalability and fault tolerance. If an instance fails, the work is redistributed to the remaining instances.
Stateful Processing: allows applications to maintain internal state, which enables advanced stream processing use cases such as windowing, aggregations, and joins.
Exactly-Once Semantics: provides uk phone number exactly-once processing guarantees, ensuring that each record is processed only once, even in the face of failures.
Flexibility: Kafka Streams can be embedded within any Java (or Scala) application, or run as a standalone application. It also supports a wide range of data types and serialization formats.
Integration with Other Kafka Components: integrates seamlessly with other Kafka components, such as Kafka Connect and Kafka Schema Registry, allowing for end-to-end stream processing pipelines.
Kafka Streams applications are typically composed of the following core concepts:
KStream: Represents an unbound
KTable: Represents a changelog stream, where each record represents an update to the current value of a key.
GlobalKTable: Represents a replicated, read-only table that can be joined with KStreams or KTables.
Processor Topology: Defines the processing Algeria Phone Number List logic, including source topics, transformations, and sink topics.
Developers use the Kafka Streams API to define their application’s processing logic, which is then compiled into a executable application. This application can then be deployed and run on one or more machines, automatically scaling and redistributing the workload as needed.
Kafka Streams has been widely adopted in the industry for building real-time data processing applications, microservices, and event-driven architectures. It is a powerful tool for teams looking to leverage the benefits of stream processing and the Apache Kafka ecosystem.
