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Confluent Developer Skills for Apache Kafka®

Learn how to build an application that can publish data to, and subscribe to data from, a Kafka cluster
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Duration

3 Days

Level

Beginner to Intermediate Level

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During this hands-on course, you will:

  • 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
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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.

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  • Explain the value of a *Distributed Event Streaming Platform*
  • Explain how the “log” abstraction enables a distributed event streaming platform
  • Explain the basic concepts of:
    • Brokers, Topics, Partitions, and Segments
    • Records (a.k.a. Messages, Events)
    • Retention Policies
    • Producers, Consumers, and Serialization
    • Replication
    • Kafka Connect
  • 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 `delivery.timeout.ms` to control retry behavior
  • Create a custom `producer.properties` file
  • Tune throughput and latency using batching
  • Create a producer with Confluent REST Proxy
  • Describe Kafka schemas and how they work
  • Use the Confluent Schema Registry to guide schema evolution
  • Write and read messages using schema-enabled Kafka

Des

  • Compare KStreams to KTables
  • Create a Custom `streams.properties` file
  • Explain what co-partitioning is and why it is important
  • Write an application using the Streams DSL (Domain-Specific Language)
  • Explain the motivation for Kafka Connect
  • List commonly used Connectors
  • Explain the differences between standalone and distributed mode
  • Configure and use Kafka Connect
  • 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
  • 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
  • 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

The hands-on lab exercises in the course follow the coherent 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
  • Creating a Kafka Streams application to do real-time distance aggregation
  • Extracting a table from an external database into Kafka using Kafka Connect
  • Creating a full event streaming application using ksqlDB that enriches driver location data with driver profile data
  • Experimenting with semantic partitioning
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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, either through:

  • Prior experience, or
  • By taking Confluent Fundamentals for Apache Kafka®
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