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Apache Kafka - Developer

( Duration: 3 Days )

This Apache Kafka - Developer training course provides skills to build an application that can publish data to, and subscribe to data from, an Apache Kafka cluster. You will learn the role of Kafka in the modern data distribution pipeline, Kafka architectural concepts and components, and review Kafka developer APIs.

By attending Apache Kafka - Developer workshop, delegates will learn to:

  • Write Producers and Consumers to send data to and read data from Kafka
  • Integrate Kafka with external systems using Kafka Connect
  • Write streaming applications with Kafka Streams & ksqlDB
  • Integrate a Kafka client application with Confluent Cloud

  • Familiar with developing professional apps in Java (preferred), C#, or Python. Additionally, a working knowledge of the Apache Kafka architecture is recommended.

The Apache Kafka - Developer class is ideal for:

  • Application developers and architects who want to write applications that interact with Apache Kafka.



Fundamentals of Apache Kafka

  • Explain the value of a "Distributed Event Streaming Platform"
  • Explain how the “log” abstraction enables a distributed event streaming platform
  • Brokers, Topics, Partitions, and Segments
  • Records (a.k.a. Messages, Events)
  • Retention Policies
  • Producers, Consumers, and Serialization
  • Replication
  • Kafka Connect

Producing Messages to Kafka

  • Sketch the high level architecture of a Kafka producer
  • Illustrate key-based partitioning
  • Explain the difference between `acks=0`, `acks=1`, and `acks=all`
  • Configure `` to control retry behavior
  • Create a custom `` file
  • Tune throughput and latency using batching
  • Create a producer with Confluent REST Proxy

Consuming Messages from Kafka

  • Illustrate how consumer groups and partitions provide scalability and fault tolerance
  • Tune consumers to avoid excessive rebalances
  • Explain the difference between “range” and “round robin” partition assignment strategies
  • Create a custom `` file
  • Use the Consumer API to manage offsets
  • Tune fetch requests
  • Create a consumer with Confluent REST Proxy

Schema Management in Apache Kafka

  • Explain what Avro is
  • Write Avro messages to Kafka
  • Use the Confluent Schema Registry to guide schema evolution

Stream Processing with Kafka Streams

  • Compare KStreams to KTables
  • Create a Custom `` file
  • Explain what co-partitioning is and why it is important
  • Write an application using the Streams DSL (Domain-Specific Language)

Data Pipelines with Kafka Connect

  • Explain the motivation for Kafka Connect
  • List commonly used Connectors
  • Explain the differences between standalone and distributed mode
  • Configure and use Kafka Connect

Event Streaming Apps with ksqlDB

  • Use ksqlDB to filter and transform a stream
  • Write a ksqlDB query that joins two streams or a stream and a table
  • Write a ksqlDB query that aggregates values per key and time window
  • Write Push and Pull queries and explain the differences between them
  • Create a Connector with ksqlDB

Design Decisions

  • List ways to avoid large message sizes
  • Decide when to use ksqlDB vs. Kafka Streams vs. Kafka Connect SMTs
  • Explain differences and tradeoffs between processing guarantees
  • Address decisions that arise from key-based partitioning
  • Authenticate a client app with a secure Kafka cluster

Confluent Cloud

  • Explain what “fully-managed” means in the context of Confluent Cloud
  • Authenticate a Kafka client to Confluent Cloud
  • Do basic operations with the “ccloud“ CLI

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