KSQLãŒConfluent Cloudã§åˆ©ç”¨å¯èƒ½ã«
Spring Bootã«ã‚ˆã‚‹APIãƒãƒƒã‚¯ã‚¨ãƒ³ãƒ‰æ§‹ç¯‰å®Ÿè·µã‚¬ã‚¤ãƒ‰ 第2版 何åƒäººã‚‚ã®é–‹ç™ºè€…ãŒã€InfoQã®ãƒŸãƒ‹ãƒ–ック「Practical Guide to Building an API Back End with Spring Bootã€ã‹ã‚‰ã€Spring Bootを使ã£ãŸREST API構築ã®åŸºç¤Žã‚’å¦ã‚“ã 。ã“ã®æœ¬ã§ã¯ã€å‡ºç‰ˆæ™‚ã«æ–°ã—ãリリースã•ã‚ŒãŸãƒãƒ¼ã‚¸ãƒ§ãƒ³ã§ã‚ã‚‹ Spring Boot 2 を使用ã—ã¦ã„る。ã—ã‹ã—ã€Spring Boot3ãŒæœ€è¿‘リリースã•ã‚Œã€é‡è¦ãªå¤‰...
Kafka with AVRO vs., Kafka with Protobuf vs., Kafka with JSON Schema
Kafka serialisation schemes — playing with AVRO, Protobuf, JSON Schema in Confluent Streaming Platform. The code for these examples available at https://github.com/saubury/kafka-serialization Apache Avro was has been the default Kafka serialisation mechanism for a long time. Confluent just updated their Kafka streaming platform with additional support for serialising data with Protocol buffers (or
LinkedIn Data Infrastructure - Software Engineering Daily
LinkedIn has become a staple for the modern professional, whether it’s used for searching for a new job, reading industry news, or keeping up with professional connections. As a rapidly growing platform that serves more than 675 million users today, LinkedIn is a company that can boast of having one of the largest user bases in the world. How these users interact with the site and react to recomme
Building robust CDC pipeline with Apache Hudi and Debezium
We have covered the need for CDC and the benefits of building a CDC pipeline. We will compare various CDC streaming and reconciliation frameworks. We will also cover the architecture and the challenges we faced while running this system in the production. Finally, we will conclude the talk by covering Apache Hudi, Schema Registry and Debezium in detail and our contributions to the open-source comm
SE Radio 393: Jay Kreps on Enterprise Integration Architecture with a Kafka Event Log – Software Engineering Radio
SE Radio 393: Jay Kreps on Enterprise Integration Architecture with a Kafka Event Log Jay Kreps, CEO of Confluent discusses an enterprise integration architecture organized around an event log. Robert Blumen spoke with Jay about the N-squared problem of data integration; how LinkedIn tried and failed to solve the integration problem;Â the nature of events; the enterprise event schema; schema defin
マイクãƒã‚µãƒ¼ãƒ“スã¨ãƒ¡ãƒƒã‚»ãƒ¼ã‚¸ãƒ³ã‚°ã®ãªãœ [概è¦ç·¨] - 赤帽エンジニアブãƒã‚°
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Kafka開発元ã®Confluentã«èžã„ãŸã€‚ã‚¨ãƒ³ã‚¿ãƒ¼ãƒ—ãƒ©ã‚¤ã‚ºå¸‚å ´ã¸ã®é“ç‹ã€å¤§æ‰‹ã‚¯ãƒ©ã‚¦ãƒ‰ã¨ã®ç¾åœ¨ã®é–¢ä¿‚ã«ã¤ã„㦠分散メッセージングシステムã®Kafkaã¯ã€ãƒ“ッグデータã®å‡¦ç†åŸºç›¤ã¨ã—ã¦æ¬ ã‹ã›ãªã„ソフトウェアã®1ã¤ã§ã™ã€‚ ãã®Kafkaã®é–‹ç™ºè€…ã§ã‚り開発元ã®Confluentå…±åŒå‰µæ¥è€…ã§ã‚ã‚‹Jun Raoæ°ã¨ã€åŒç¤¾ã‚·ãƒ‹ã‚¢ãƒ‡ã‚£ãƒ¬ã‚¯ã‚¿ã®Tim Attwoodæ°ãŒNTTデータ テクノãƒã‚¸ãƒ¼ã‚«ãƒ³ãƒ•ã‚¡ãƒ¬ãƒ³ã‚¹2019ã«åˆã‚ã›ã¦æ¥æ—¥ã—ã¾ã—ãŸã€‚ ãŠäºŒäººã«ã€KafkaãŒã“ã“ã¾ã§æˆåŠŸã—ã¦ããŸè¦å› ã€ãã—ã¦å¤§æ‰‹ã‚¯ãƒ©ã‚¦ãƒ‰ãƒ™ãƒ³ãƒ€ã«å発ã—ã¦ã‚ªãƒ¼ãƒ—ンソースã®ãƒ©ã‚¤ã‚»ãƒ³ã‚¹å¤‰æ›´ã«åŠã‚“ã 後ã®ã‚¯ãƒ©ã‚¦ãƒ‰ãƒ™ãƒ³ãƒ€ã¨ã®é–¢ä¿‚ã«ã¤ã„ã¦ã€ç¾åœ¨ã®èªè˜ã‚’èžãã¾ã—ãŸã€‚ Kafkaã®æˆåŠŸè¦å› ã¯ãƒ“ッグデータ時代ã¨ã®åˆè‡´ã€ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ã®å®Œæˆåº¦ ―― Kafkaã¯ãƒ“ッグデータ処ç†ã®ã‚·ã‚¹ãƒ†ãƒ ã§éžå¸¸ã«è‰¯ã利用ã•ã‚Œã¦ã„ã¦ã€åˆ†æ•£ãƒ¡ãƒƒã‚»ãƒ¼ã‚¸ãƒ³ã‚°ã‚·ã‚¹ãƒ†ãƒ ã®åˆ†é‡Žã®ãƒ‡ãƒ•ã‚¡ã‚¯ãƒˆã‚¹
Kafka Streamを使ã£ãŸã‚¹ãƒˆãƒªãƒ¼ãƒ 処ç†ã®æ¦‚è¦ã¨é‹ç”¨æ™‚ã®è€ƒæ…®ç‚¹ - joker1007’s diary
最近ã€ä»•äº‹ã§åˆ†æ•£ã‚¹ãƒˆãƒªãƒ¼ãƒ 処ç†ã«æ‰‹ã‚’出ã—ã¦ã„ã¦ã€ãã®åŸºç›¤ã¨ã—ã¦Apache Kafkaã¨Kafka Streamsを使ã†ã“ã¨ã«ã—ãŸã®ã§ã€å‹•ä½œæ¦‚è¦ã¨ã‚¹ãƒˆãƒªãƒ¼ãƒ 処ç†ã®ã‚¤ãƒ¡ãƒ¼ã‚¸ã«ã¤ã„ã¦ã¾ã¨ã‚ã¦ãŠã。 kafkaãã®ã‚‚ã®ã«ã¤ã„ã¦ã¯ä»Šæ›´èª¬æ˜Žã®å¿…è¦ã¯ç„¡ã„ã ã‚ã†ã¨æ€ã†ã€‚ Kafka Streamsã¯kafkaを基盤ã«ã—ã¦åˆ†æ•£ã‚¹ãƒˆãƒªãƒ¼ãƒ 処ç†ã‚’ç°¡å˜ã«æ›¸ããŸã‚ã®DSLライブラリ。 https://kafka.apache.org/documentation/streams/ 延々æµã‚Œã¦ãるデータを変æ›ã—ã¦åˆ¥ã®topicã«æµã—ãŸã‚Šã€æ™‚é–“ã®ã‚¦ã‚¤ãƒ³ãƒ‰ã‚¦ã‚’区切ã£ã¦ã‚«ã‚¦ãƒ³ãƒˆã—ãŸçµæžœã‚’æµã—ãŸã‚Šã€ã¿ãŸã„ãªã®ãŒã‚µã‚¯ã£ã¨æ›¸ã‘る。 Apache Flinkãªã‚“ã‹ã¨ä¼¼ãŸæ§˜ãªã“ã¨ãŒã§ãる。 Kafka StreamsãŒè‰¯ã„ã®ã¯ä»¥ä¸‹ã®ç‚¹ã€‚ ãŸã ã®Consumer/Producerã®ãƒ©ãƒƒãƒ‘ーãªã®ã§fat-jarファイル一ã¤ã§ç°¡å˜ã«å‹•ã‹ã›
Kafka Connect Deep Dive – JDBC Source Connector | Confluent
What we’ll cover JDBC drivers No suitable driver found Specifying which tables to ingest Incremental ingest Query-based ingest One connector or many? Why is there no data? Resetting the point from which JDBC source connector reads data Starting table capture from a specified timestamp or ID Setting the Kafka message key Changing the topic name Bytes, Decimals, Numerics and oh my Working with multi
イベントストリームKafkaã¨ãƒ¯ãƒ¼ã‚¯ãƒ•ãƒãƒ¼ã‚¨ãƒ³ã‚¸ãƒ³Zeebe
Spring Bootã«ã‚ˆã‚‹APIãƒãƒƒã‚¯ã‚¨ãƒ³ãƒ‰æ§‹ç¯‰å®Ÿè·µã‚¬ã‚¤ãƒ‰ 第2版 何åƒäººã‚‚ã®é–‹ç™ºè€…ãŒã€InfoQã®ãƒŸãƒ‹ãƒ–ック「Practical Guide to Building an API Back End with Spring Bootã€ã‹ã‚‰ã€Spring Bootを使ã£ãŸREST API構築ã®åŸºç¤Žã‚’å¦ã‚“ã 。ã“ã®æœ¬ã§ã¯ã€å‡ºç‰ˆæ™‚ã«æ–°ã—ãリリースã•ã‚ŒãŸãƒãƒ¼ã‚¸ãƒ§ãƒ³ã§ã‚ã‚‹ Spring Boot 2 を使用ã—ã¦ã„る。ã—ã‹ã—ã€Spring Boot3ãŒæœ€è¿‘リリースã•ã‚Œã€é‡è¦ãªå¤‰...
Microservices, Apache Kafka, and Domain-Driven Design | Confluent
Everybody is talking about creating an agile and flexible architecture with microservices, a term that is used today in many different contexts. Although microservices are not a free lunch, they do provide many benefits, including decoupling. Decoupling is the process of organizing a system around business capabilities to form an architecture that is decentralized. Smart endpoints and dumb pipes e
uReplicator: Uber Engineering’s Robust Kafka Replicator
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Uber’s Analytics Pipeline At Uber, we use Apache Kafka as a message bus for connecting different parts of the ecosystem. We collect system and application logs as well as event data from the rider and driver apps. Then we make this data available to a variety of downstr
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Building Near Real-time Big Data Lake: Part 2
Apache Kafka - Faster with Oracle GraalVM Enterprise | Oracle
https://jp.techcrunch.com/2020/05/07/2020-05-06-confluent-introduces-scale-on-demand-for-apache-kafka-cloud-customers/
https://docs.confluent.io/current/connect/kafka-connect-s3/index.html
Building zero-latency data lake using Change Data Capture
https://jp.techcrunch.com/2019/10/01/2019-09-30-confluent-adds-free-tier-to-kafka-real-time-streaming-data-cloud-service/
Kafka Applications with Tim Berglund - Software Engineering Daily
Live Aggregators: Highly Reliable and Massively Scalable Real Time Aggregation System - Mist
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