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Kafka Streams in Action, Second Edition, Video Edition

سرفصل های دوره
  • 001. Part 1
  • 002. Part 1
  • 003. Chapter 1. Welcome to the Kafka event streaming platform
  • 004. Chapter 1. What is an event
  • 005. Chapter 1. An event stream example
  • 006. Chapter 1. Introducing the Apache Kafka event streaming platform
  • 007. Chapter 1. A concrete example of applying the Kafka event streaming platform
  • 008. Chapter 1. Summary
  • 009. Chapter 2. Kafka brokers
  • 010. Chapter 2. Produce requests
  • 011. Chapter 2. Fetch requests
  • 012. Chapter 2. Topics and partitions
  • 013. Chapter 2. Sending your first messages
  • 014. Chapter 2. Segments
  • 015. Chapter 2. Tiered storage
  • 016. Chapter 2. Cluster metadata
  • 017. Chapter 2. Leaders and followers
  • 018. Chapter 2. Checking for a healthy broker
  • 019. Chapter 2. Summary
  • 020. Part 2
  • 021. Chapter 3. Schema Registry
  • 022. Chapter 3. What is a schema, and why do you need one
  • 023. Chapter 3. Subject name strategies
  • 024. Chapter 3. Schema compatibility
  • 025. Chapter 3. Schema references
  • 026. Chapter 3. Schema references and multiple events per topic
  • 027. Chapter 3. Schema Registry (de)serializers
  • 028. Chapter 3. Serialization without Schema Registry
  • 029. Chapter 3. Summary
  • 030. Chapter 4. Kafka clients
  • 031. Chapter 4. Producing records with the KafkaProducer
  • 032. Chapter 4. Consuming records with the KafkaConsumer
  • 033. Chapter 4. Exactly-once delivery in Kafka
  • 034. Chapter 4. Using the Admin API for programmatic topic management
  • 035. Chapter 4. Handling multiple event types in a single topic
  • 036. Chapter 4. Summary
  • 037. Chapter 5. Kafka Connect
  • 038. Chapter 5. Integrating external applications into Kafka
  • 039. Chapter 5. Getting started with Kafka Connect
  • 040. Chapter 5. Applying Single Message Transforms
  • 041. Chapter 5. Adding a sink connector
  • 042. Chapter 5. Building and deploying your own connector
  • 043. Chapter 5. Summary
  • 044. Part 3
  • 045. Chapter 6. Developing Kafka Streams
  • 046. Chapter 6. Kafka Streams DSL
  • 047. Chapter 6. Hello World for Kafka Streams
  • 048. Chapter 6. Masking credit card numbers and tracking purchase rewards in a retail sales setting
  • 049. Chapter 6. Interactive development
  • 050. Chapter 6. Choosing which events to process
  • 051. Chapter 6. Summary
  • 052. Chapter 7. Streams and state
  • 053. Chapter 7. Adding stateful operations to Kafka Streams
  • 054. Chapter 7. Stream-stream joins
  • 055. Chapter 7. State stores in Kafka Streams
  • 056. Chapter 7. Summary
  • 057. Chapter 8. The KTable API
  • 058. Chapter 8. KTables are stateful
  • 059. Chapter 8. The KTable API
  • 060. Chapter 8. KTable aggregations
  • 061. Chapter 8. GlobalKTable
  • 062. Chapter 8. Table joins
  • 063. Chapter 8. Summary
  • 064. Chapter 9. Windowing and timestamps
  • 065. Chapter 9. Handling out order data with grace literally
  • 066. Chapter 9. Final windowed results
  • 067. Chapter 9. Timestamps in Kafka Streams
  • 068. Chapter 9. The TimestampExtractor
  • 069. Chapter 9. Stream time
  • 070. Chapter 9. Summary
  • 071. Chapter 10. The Processor API
  • 072. Chapter 10. Digging deeper into the Processor API with a stock analysis processor
  • 073. Chapter 10. Data-driven aggregation
  • 074. Chapter 10. Integrating the Processor API and the Kafka Streams API
  • 075. Chapter 10. Summary
  • 076. Chapter 11. ksqlDB
  • 077. Chapter 11. More about streaming queries
  • 078. Chapter 11. Persistent vs. push vs. pull queries
  • 079. Chapter 11. Creating Streams and Tables
  • 080. Chapter 11. Schema Registry integration
  • 081. Chapter 11. ksqlDB advanced features
  • 082. Chapter 11. Summary
  • 083. Chapter 12. Spring Kafka
  • 084. Chapter 12. Using Spring to build Kafka-enabled applications
  • 085. Chapter 12. Spring Kafka Streams
  • 086. Chapter 12. Summary
  • 087. Chapter 13. Kafka Streams Interactive Queries
  • 088. Chapter 13. Learning about Interactive Queries
  • 089. Chapter 13. Summary
  • 090. Chapter 14. Testing
  • 091. Chapter 14. Summary
  • 092. appendix A. Schema compatibility workshop
  • 093. appendix A. Forward compatibility
  • 094. appendix A. Full compatibility
  • 095. appendix B. Confluent resources
  • 096. appendix B. Confluent command-line interface
  • 097. appendix B. Confluent local
  • 098. appendix C. Working with Avro, Protobuf, and JSON Schema
  • 099. appendix C. Protocol Buffers
  • 100. appendix C. JSON Schema
  • 101. appendix D. Understanding Kafka Streams architecture
  • 102. appendix D. Consumer and producer clients in Kafka Streams
  • 103. appendix D. Assigning, distributing, and processing events
  • 104. appendix D. Threads in Kafka Streams StreamThread
  • 105. appendix D. Processing records
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    تاریخ انتشار: 15 آبان 1403
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