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Apache kafka-a distributed streaming platform
The document discusses Apache Kafka as a distributed streaming platform, highlighting its capabilities in real-time data ingestion, processing, and storage. It emphasizes the simplicity and efficiency of building applications with Kafka, noting its scalability and native integrations with various technologies. Additionally, it mentions industry adoption and provides references for further information.
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Apache kafka-a distributed streaming platform
- 1. Apache Kafka A DistributedStreaming Platform StreamProcessing.be - Belgium Wednesday, 18th January 2017 < paolo @ confluent.io >
- 2. https://www.confluent.io/blog/stream-data-platform-1/ Industry shift fromBig Data to Fast Data and Stream Processing
- 3. $ cat <in.txt | grep “apache” | tr a-z A-Z > out.txt Apache Kafka APIs and UNIX analogy
- 4. $ cat <in.txt | grep “apache” | tr a-z A-Z > out.txt Connect APIs Apache Kafka APIs and UNIX analogy
- 5. $ cat <in.txt | grep “apache” | tr a-z A-Z > out.txt Producer/Consumer APIs Apache Kafka APIs and UNIX analogy
- 6. $ cat <in.txt | grep “apache” | tr a-z A-Z > out.txt Streams APIs Apache Kafka APIs and UNIX analogy
- 7. Streams APIs part ofApache Kafka http://kafka.apache.org/documentation/streams
- 8. Build applications, notclusters <dependency> <groupId>org.apache.kafka</groupId> <artifactId>kafka-streams</artifactId> <version>0.10.1.1</version> </dependency>
- 9.
- 10. How do Irun in production?
- 11. How do Irun in production? As any other Java applications...
- 12. How do Irun in production? Uncool Cool
- 13. Typical High LevelArchitecture
- 14. Typical High LevelArchitecture Real-time Data Ingestion
- 15. Typical High LevelArchitecture Stream Processing Storage Real-time Data Ingestion
- 16. Typical High LevelArchitecture Data Publishing / Visualization Stream Processing Storage Real-time Data Ingestion
- 17. How many clustersdo you count? NoSQL (Cassandra, HBase, Couchbase, MongoDB, …) or Elasticsearch, Solr, … Storm, Flink, Spark Streaming, Ignite, Akka Streams, Apex, … HDFS, NFS, Ceph, GlusterFS, Lustre, ... Apache Kafka
- 18. Simplicity is theultimate sophistication Apache Kafka Distributed Streaming Platform Publish & Subscribe to streams of data like a messaging system Store streams of data safely in a distributed replicated cluster Process streams of data efficiently and in real-time Node.js
- 19. Apache Kafka andStreams APIs benefits • Build applications, not clusters • Native integration with Apacke Kafka • Elastic, fast, distributed, fault-tolerant, secure • Scalable: S, M, L, XL, XXL • Run everywhere: from containers to cloud • Streams (with KStream) and tables (with KTable) • Local state replicated to Kafka for fault-tolerance • Windowing and event time semantics out of the box • Supports late-arriving and out-of-order events
- 20. Apache Kafka adoptionacross the industry… … everybody loves simplicity!
- 21. References • http://kafka.apache.org/ • http://kafka.apache.org/documentation/streams •http://docs.confluent.io/ • http://docs.confluent.io/current/streams/ • http://blog.confluent.io/ • http://github.com/confluentinc/examples • http://github.com/apache/kafka/tree/trunk/streams
- 22.
- 23. The easiest wayto get you started https://www.confluent.io/download/
- 24.
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- 26. Discount code: kafcom17 Usethe Apache Kafka community discount code to get $50 off www.kafka-summit.org Kafka Summit New York: May 8 Kafka Summit San Francisco: August 28 Presented by