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    <h1>Streams</h1>

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            <a href="/{{version}}/documentation/streams/quickstart">Play with a Streams Application</a>
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            <a href="/{{version}}/documentation/streams/core-concepts">Core Concepts</a>
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    <h2>Overview</h2>

    <p>
        Kafka Streams is a client library for processing and analyzing data stored in Kafka.
        It builds upon important stream processing concepts such as properly distinguishing between event time and processing time, windowing support, and simple yet efficient management of application state.
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    <p>
        Kafka Streams has a <b>low barrier to entry</b>: You can quickly write and run a small-scale proof-of-concept on a single machine; and you only need to run additional instances of your application on multiple machines to scale up to high-volume production workloads.
        Kafka Streams transparently handles the load balancing of multiple instances of the same application by leveraging Kafka's parallelism model.
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    <p>
        Some highlights of Kafka Streams:
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        <li>Designed as a <b>simple and lightweight client library</b>, which can be easily embedded in any Java application and integrated with any existing packaging, deployment and operational tools that users have for their streaming applications.</li>
        <li>Has <b>no external dependencies on systems other than Apache Kafka itself</b> as the internal messaging layer; notably, it uses Kafka's partitioning model to horizontally scale processing while maintaining strong ordering guarantees.</li>
        <li>Supports <b>fault-tolerant local state</b>, which enables very fast and efficient stateful operations like windowed joins and aggregations.</li>
        <li>Supports <b>exactly-once</b> processing semantics to guarantee that each record will be processed once and only once even when there is a failure on either Streams clients or Kafka brokers in the middle of processing.</li>
        <li>Employs <b>one-record-at-a-time processing</b> to achieve millisecond processing latency, and supports <b>event-time based windowing operations</b> with late arrival of records.</li>
        <li>Offers necessary stream processing primitives, along with a <b>high-level Streams DSL</b> and a <b>low-level Processor API</b>.</li>

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