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                  <li>Write standard Java and Scala applications</li>
                  <li>Exactly-once processing semantics</li>
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                  <li>Develop on Mac, Linux, Windows</li>

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        <h3 class="stream__text">Kafka Streams use cases</h3>
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                 <a href="https://open.nytimes.com/publishing-with-apache-kafka-at-the-new-york-times-7f0e3b7d2077" target="_blank">The New York Times uses Apache Kafka </a>and the Kafka Streams to store and distribute, in real-time, published content to the various applications and systems that make it available to the readers.
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               <a href="https://www.confluent.io/blog/ranking-websites-real-time-apache-kafkas-streams-api/" target="_blank" class="grid__logo__link">
                 <span class="grid__item__logo" style="background-image: url('/images/powered-by/zalando.jpg');"></span>
               </a>
               <p class="grid__item__customer__description extra__space">As the leading online fashion retailer in Europe, Zalando uses Kafka as an ESB (Enterprise Service Bus), which helps us in transitioning from a monolithic to a micro services architecture. Using Kafka for processing
                 <a href="https://www.confluent.io/blog/ranking-websites-real-time-apache-kafkas-streams-api/" target='blank'> event streams</a> enables our technical team to do near-real time business intelligence.
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                 <p class="grid__item__customer__description extra__space"><a href="https://engineering.linecorp.com/en/blog/detail/80" target="_blank">LINE uses Apache Kafka</a> as a central datahub for our services to communicate to one another. Hundreds of billions of messages are produced daily and are used to execute various business logic, threat detection, search indexing and data analysis. LINE leverages Kafka Streams to reliably transform and filter topics enabling sub topics consumers can efficiently consume, meanwhile retaining easy maintainability thanks to its sophisticated yet minimal code base.</p>
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                 <p class="grid__item__customer__description">Rabobank is one of the 3 largest banks in the Netherlands. Its digital nervous system, the Business Event Bus, is powered by Apache Kafka. It is used by an increasing amount of financial processes and services, one of which is Rabo Alerts. This service alerts customers in real-time upon financial events and is <a href="https://www.confluent.io/blog/real-time-financial-alerts-rabobank-apache-kafkas-streams-api/" target="_blank">built using Kafka Streams.</a></p>
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       <h3 style="margin-top: 5.3rem;">Hello Kafka Streams</h3>
       <p>The code example below implements a WordCount application that is elastic, highly scalable, fault-tolerant, stateful, and ready to run in production at large scale</p>

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               <pre class="line-numbers"><code class="language-java">import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.common.utils.Bytes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.kstream.KStream;
import org.apache.kafka.streams.kstream.KTable;
import org.apache.kafka.streams.kstream.Materialized;
import org.apache.kafka.streams.kstream.Produced;
import org.apache.kafka.streams.state.KeyValueStore;

import java.util.Arrays;
import java.util.Properties;

public class WordCountApplication {

   public static void main(final String[] args) throws Exception {
       Properties props = new Properties();
       props.put(StreamsConfig.APPLICATION_ID_CONFIG, "wordcount-application");
       props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "kafka-broker1:9092");
       props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
       props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());

       StreamsBuilder builder = new StreamsBuilder();
       KStream&lt;String, String&gt; textLines = builder.stream("TextLinesTopic");
       KTable&lt;String, Long&gt; wordCounts = textLines
           .flatMapValues(textLine -> Arrays.asList(textLine.toLowerCase().split("\\W+")))
           .groupBy((key, word) -> word)
           .count(Materialized.&lt;String, Long, KeyValueStore&lt;Bytes, byte[]&gt;&gt;as("counts-store"));
       wordCounts.toStream().to("WordsWithCountsTopic", Produced.with(Serdes.String(), Serdes.Long()));

       KafkaStreams streams = new KafkaStreams(builder.build(), props);
       streams.start();
   }

}</code></pre>
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           <div class="code-example__snippet b-java-7">
               <pre class="line-numbers"><code class="language-java">import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.common.utils.Bytes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.kstream.KStream;
import org.apache.kafka.streams.kstream.KTable;
import org.apache.kafka.streams.kstream.ValueMapper;
import org.apache.kafka.streams.kstream.KeyValueMapper;
import org.apache.kafka.streams.kstream.Materialized;
import org.apache.kafka.streams.kstream.Produced;
import org.apache.kafka.streams.state.KeyValueStore;

import java.util.Arrays;
import java.util.Properties;

public class WordCountApplication {

   public static void main(final String[] args) throws Exception {
       Properties props = new Properties();
       props.put(StreamsConfig.APPLICATION_ID_CONFIG, "wordcount-application");
       props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "kafka-broker1:9092");
       props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
       props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());

       StreamsBuilder builder = new StreamsBuilder();
       KStream&lt;String, String&gt; textLines = builder.stream("TextLinesTopic");
       KTable&lt;String, Long&gt; wordCounts = textLines
           .flatMapValues(new ValueMapper&lt;String, Iterable&lt;String&gt;&gt;() {
               @Override
               public Iterable&lt;String&gt; apply(String textLine) {
                   return Arrays.asList(textLine.toLowerCase().split("\\W+"));
               }
           })
           .groupBy(new KeyValueMapper&lt;String, String, String&gt;() {
               @Override
               public String apply(String key, String word) {
                   return word;
               }
           })
           .count(Materialized.&lt;String, Long, KeyValueStore&lt;Bytes, byte[]&gt;&gt;as("counts-store"));


       wordCounts.toStream().to("WordsWithCountsTopic", Produced.with(Serdes.String(), Serdes.Long()));

       KafkaStreams streams = new KafkaStreams(builder.build(), props);
       streams.start();
   }

}</code></pre>
           </div>

           <div class="code-example__snippet b-scala">
               <pre class="line-numbers"><code class="language-scala">import java.util.Properties
import java.util.concurrent.TimeUnit

import org.apache.kafka.streams.kstream.Materialized
import org.apache.kafka.streams.scala.ImplicitConversions._
import org.apache.kafka.streams.scala._
import org.apache.kafka.streams.scala.kstream._
import org.apache.kafka.streams.{KafkaStreams, StreamsConfig}

object WordCountApplication extends App {
  import Serdes._

  val props: Properties = {
    val p = new Properties()
    p.put(StreamsConfig.APPLICATION_ID_CONFIG, "wordcount-application")
    p.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "kafka-broker1:9092")
    p
  }

  val builder: StreamsBuilder = new StreamsBuilder
  val textLines: KStream[String, String] = builder.stream[String, String]("TextLinesTopic")
  val wordCounts: KTable[String, Long] = textLines
    .flatMapValues(textLine => textLine.toLowerCase.split("\\W+"))
    .groupBy((_, word) => word)
    .count()(Materialized.as("counts-store"))
  wordCounts.toStream.to("WordsWithCountsTopic")

  val streams: KafkaStreams = new KafkaStreams(builder.build(), props)
  streams.start()

  sys.ShutdownHookThread {
     streams.close(10, TimeUnit.SECONDS)
  }
}</code></pre>
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