IBM Confluent Developer Skills for Building Apache Kafka
- Kursuskode DEVKAFKAG
- Varighed 3 dage
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Beskrivelse
ToppenBuild, 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
Kursusdato
ToppenMålgruppe
ToppenApplication 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.
Kursets formål
ToppenThe 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
Kursusindhold
ToppenIntroductory 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
Forudsætninger
ToppenAttendees 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.
Certificeringstest
Toppen- None
Efterfølgende kurser
Toppen- None recommended
Mere information
Toppen- Official course book provided to participants