Summer-Ready Savings: Find the Training Course You Need at a Price You'll Love

closeClose

Confluent Developer Skills for Building Apache Kafka

Course Details
Code: AK-DEV
Tuition (USD): $2,475.00 • Classroom (3 days)
$2,475.00 • Virtual (3 days)

In this three-day, hands-on Developer training course, you will learn how 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.

Skills Gained

In 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

Who Can Benefit

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. Python, C# and NodeJS clients will also be used.

Prerequisites

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:

Course Details

Hands-on Training:

The hands-on lab exercises in the course follow the coherent story of building and upgrading a driver location app. This gives a throughline throughout the course where 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 kslqDB that enriches driver location data with driver profile data
  • Experimenting with semantic partitioning

Course Outline

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 `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

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 `consumer.properties` 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 `streams.properties` 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

Stream Processing with Kafka Streams

  • 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)

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
Contact Us 1-800-803-3948
Contact Us Live Chat
FAQ Get immediate answers to our most frequently asked qestions. View FAQs arrow_forward