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IBM Confluent Developer Skills for Building Apache Kafka

  • Code training DEVKAFKAG
  • Duur 3 dagen

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Trainingsbeschrijving

Naar boven

Build, integrate, and architect real-time streaming applications using Apache Kafka and Confluent.

 

Through interactive lessons and real-world exercises, you'll gain end-to-end expertise across the entire streaming pipeline covering client development, stream processing, and enterprise data governance.

Several of the hands-on lab exercises in this course follow the story of building and upgrading a driver location app. Throughout the course concepts are applied directly to a working application. Exercises are available in Java, C# and Python. Exercises include: 

  • Working with Kafka command line tools 
  • Producing driver location data to Kafka and consuming that data in real-time
  • Refactoring the application to use Avro and Schema Registry
  • Pulling the driver data into a Kafka Streams app to enrich it
  • Extracting a table from an external database into Kafka using Kafka Connect
  • Experimenting with semantic partitioning

 

Updated 16/09/2026

Doelgroep

Naar boven

Application Developers and Architects who want to write applications that interact with Apache Kafka.

The course treats Java as a first-class citizen, but students will derive value even if Java is not their primary programming language. C# and Python clients will also be used in some options for labs.

Trainingsdoelstellingen

Naar boven

The lessons and activities in this course enable participants to build the skills to:

  • Write Producers and Consumers to send data to and read data from Kafka 
  • Create schemas, describe schema evolution, and integrate with Confluent Schema Registry 
  • Integrate Kafka with external systems using Kafka Connect 
  • Write streaming applications with Kafka Streams
  • Make design decisions about acks, keys, partitions, batching, replication, and retention polices

Inhoud training

Naar boven

Introductory Concepts 

  • Configure your Kafka applications to connect to a Kafka cluster 
  • Distinguish between leaders and followers and work with replicas 
  • Explain what a segment is and explore retention 
  • Use the CLI to work with topics, producers, and consumers 

Working with Producers 

  • Describe the work a producer performs, and the core components needed to produce messages 
  • Create producers and specify configuration properties 
  • Explain how to configure producers to know that Kafka receives messages
  • Delve into how batching works and explore batching configurations
  • Explore reacting to failed delivery and tuning producers with timeouts 
  • Use the APIs for Java, C#/.NET, or Python to create a Producer

Consumers, Groups, and Partitions 

  • Create and manage consumers and their property files 
  • Illustrate how consumer groups and partitions provide scalability and fault tolerance
  • Explore managing consumer offsets 
  • Tune fetch requests 
  • Explain how consumer groups are managed and their benefits 
  • Compare and contrast group management strategies and when you might use each 
  • Use the API for Java, C#/.NET, or Python to create a Consumer 

Schemas and the Confluent Schema Registry 

  • Describe Kafka schemas and how they work 
  • Write an Avro compatible schema and explore using Protobuf and JSON schemas 
  • Write schemas that can evolve 
  • Write and read messages using schema-enabled Kafka client applications
  • Build an Avro Kafka producer or consumer in Python, Java or C# using Confluent Schema Registry

Streaming with Kafka Streams and Flink 

  • Develop an appreciation for what streaming applications can do for you back on the job
  • Describe Kafka Streams and explore streams properties and topologies 
  • Compare and contrast streams and tables, and relate events in streams to records/messages in topics 
  • Write a Kafka Streams application using the Streams DSL (Domain-Specific Language) 
  • Detail Apache Flink's key capabilities and APIs, identifying their target use cases 

Kafka Connect 

  • List the components of Kafka Connect and describe how they relate
  • Set configurations for components of Kafka Connect 
  • Describe connect integration and how data flows between applications and Kafka 
  • Explore some use-cases where Kafka Connect makes development efficient 
  • Use Kafka Connect in conjunction with other tools to process data in motion in the most efficient way 
  • Create a Connector and import data from a database to a Kafka cluster 

Design Decisions and Considerations 

  • Delve into how compaction affects consumer offsets 
  • Explore how consumers work with offsets in scenarios outside of normal processing behavior
  • Understand how to manipulate offsets to deal with anomalies
  • Evaluate decisions about consumer and partition counts and how they relate 
  • Address decisions that arise from default key-based partitioning and consider alternative partitioning strategies 
  • Configure producers to deliver messages without duplicates and with ordering guarantees
  • List ways to manage large message sizes 
  • Describe how to work with messages in transactions and how Kafka enables transactions

Voorkennis

Naar boven

Attendees should be familiar with developing professional apps in Java (preferred), C#, or Python.

Additionally, a working knowledge of the Apache Kafka architecture is required for this course.

Participants are required to provide a laptop computer with unobstructed internet access to fully participate in the class.

Vervolgtrainingen

Naar boven
  • None recommended

Aanvullende informatie

Naar boven
  • Official course book provided to participants
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