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All Classes Will Be Held Virtually – Live Online Intertech's Training Division has been successfully instructing professionals through virtual live online training since the advent of the smartboard. It is a proven form and offers the convenience of live questions, group interaction, and labs with an instructor looking over your shoulder. Because of this, we will continue all classes live but virtually, including Agile and Scrum instruction, so businesses and individual’s seeking professional development can keep moving forward during these unexpected times.

Kafka for Application Developers Training

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In this course, you will learn how to use Kafka to modernize your applications. In modern applications, real-time information is continuously generated by applications (publishers/producers) and routed to other applications (subscribers/consumers). Apache Kafka is an open source, distributed publish-subscribe messaging system. Kafka has high-throughput and is built to scale-out in a distributed model on multiple servers. Kafka persists messages on disk and can be used for batched consumption as well as real-time applications.

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For groups of 5 or more, let Intertech bring this course to your location. Customized versions tailored towards your objectives are also available.

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Learning Objectives

  • Understand the use of Kafka for high performance messaging
  • Identify the usages for Kafka in Microservices
  • Explain the benefits of Kafka patterns
  • Differentiate between messaging and message brokers
  • Describe Kafka messaging environments
  • Develop producers and consumers for Kafka
  • Recognize how Kafka enables Cloud-native applications
  • Summarize characteristics and architecture for Kafka
  • Demonstrate how to process messages with Kafka
  • Design distributed high throughput systems based on Kafka
  • Describe the built-in partitioning, replication and inherent fault-tolerance of Kafka


This is a general introduction course for developers, architects, system integrators, security administrators, network administrators, software engineers, technical support individuals, technology leaders & managers, and consultants who are responsible for elements of messaging for data collection, transformation, and integration for your organization supporting Application Modernization, Cloud-Native Development, and Digital Data Supply Chain (Big Data/IoT/AI/Machine Learning/Advanced Analytics/Business Intelligence).


Basic understanding of messaging, cloud, development, architecture and virtualization would be beneficial.

Course Outline

Chapter 1. Introduction to Kafka

  • Messaging Architectures – What is Messaging?
  • Messaging Architectures – Steps to Messaging
  • Messaging Architectures – Messaging Models
  • What is Kafka?
  • What is Kafka? (Contd.)
  • Kafka Overview
  • Kafka Overview (Contd.)
  • Need for Kafka
  • When to Use Kafka?
  • Kafka Architecture
  • Core concepts in Kafka
  • Kafka Topic
  • Kafka Partitions
  • Kafka Producer
  • Kafka Consumer
  • Kafka Broker
  • Kafka Cluster
  • Why Kafka Cluster?
  • Sample Multi-Broker Cluster
  • Overview of ZooKeeper
  • Kafka Cluster & ZooKeeper
  • Who Uses Kafka?
  • Summary

Chapter 2. Using Apache Kafka

  • Installing Apache Kafka
  • Configuration Files
  • Starting Kafka
  • Using Kafka Command Line Client Tools
  • Setting up a Multi-Broker Cluster
  • Using Multi-Broker Cluster
  • Kafka Cluster Planning
  • Kafka Cluster Planning – Producer/Consumer Throughput
  • Kafka Cluster Planning – Number of Brokers (and ZooKeepers)
  • Kafka Cluster Planning – Sizing for Topics and Partitions
  • Kafka Cluster Planning – Sizing for Storage
  • Kafka Connect
  • Kafka Connect – Configuration Files
  • Using Kafka Connect to Import/Export Data
  • Creating a Spring Boot Producer
  • Adding Kafka dependency to pom.xml
  • Defining a Spring Boot Service to Send Message(s)
  • Defining a Spring Boot Controller
  • Testing the Spring Boot Producer
  • Creating a Nodejs Consumer
  • Summary

Chapter 3. Building Data Pipelines

  • Building Data Pipelines
  • Considerations When Building Data Pipelines
  • Timeliness
  • Reliability
  • High and Varying Throughput
  • High and Varying Throughput (Contd.)
  • Data Formats
  • Data Formats (Contd.)
  • Transformations
  • Transformations (Contd.)
  • Security
  • Failure Handling
  • Coupling and Agility
  • Ad-hoc Pipelines
  • Loss of Metadata
  • Extreme Processing
  • Kafka Connect Versus Producer and Consumer
  • Kafka Connect Versus Producer and Consumer (Contd.)
  • Summary

Chapter 4. Integrating Kafka with Other Systems

  • Introduction to Kafka Integration
  • Kafka Connect
  • Kafka Connect (Contd.)
  • Running Kafka Connect
  • Key Configurations for Connect workers:
  • Kafka Connect API
  • Kafka Connect Example – File Source
  • Kafka Connect Example – File Sink
  • Kafka Connector Example – MySQL to Elasticsearch
  • Kafka Connector Example – MySQL to Elasticsearch (Contd.)
  • Write the data to Elasticsearch
  • Building Custom Connectors
  • Kafka Connect – Connectors
  • Kafka Connect - Tasks
  • Kafka Connect - Workers
  • Kafka Connect – Workers (Contd.)
  • Kafka Connect - Converters and Connect’s data model
  • Kafka Connect - Offset management
  • Alternatives to Kafka Connect
  • Alternatives to Kafka Connect (Contd.)
  • Introduction to Storm
  • Other Components of Spark
  • Integrating Storm with Kafka
  • Integrating Storm with Kafka – Sample Code
  • Integrating Storm with Kafka
  • Introduction to Hadoop
  • Hadoop Components
  • Integrating Hadoop with Kafka
  • Hadoop Consumers
  • Hadoop Consumers (Contd.)
  • Hadoop Consumers (Contd.)
  • Hadoop Consumers – Produce Topic
  • Hadoop Consumers – Fetch Generated Topic
  • Kafka at Uber
  • Kafka at Uber (Contd.)
  • Kafka at LinkedIn
  • Kafka at LinkedIn – Core Kafka Services
  • Kafka at LinkedIn – Core Kafka Services (Contd.)
  • Kafka at LinkedIn – Libraries
  • Kafka at LinkedIn – Monitoring and Stream Processing
  • Summary

Chapter 5. Kafka and Schema Management

  • Evolving Schema
  • Protobuf (Protocol Buffers) Overview
  • Avro Overview
  • Managing Data Evolution Using Schemas
  • Confluent Platform
  • Confluent Schema Registry
  • Schema Change and Backward Compatibility
  • Collaborating over Schema Change
  • Handling Unreadable Messages
  • Deleting Data
  • Segregating Public and Private Topics
  • Summary

Chapter 6. Kafka Streams and KSQL

  • What Kafka can be used for?
  • What Kafka can be used for? (Contd.)
  • What Exactly is Kafka?
  • The APIs for Stream Processing
  • Kafka: A Streaming Platform
  • What is KSQL?
  • What is KSQL? (Contd.)
  • Starting KSQL
  • Using the KSQL CLI
  • KSQL Data Types
  • Review the Structure of an Existing STREAM
  • Query the STREAM
  • KSQL Functions
  • Writing to a Topic
  • KSQL Table vs. Stream
  • Windows in KSQL Queries
  • Miscellaneous KSQL Commands
  • Summary

Chapter 7. KSQL UDF and Deployment

  • KSQL Custom Functions
  • Implement a Custom Function
  • Creating UDF and UDAF
  • Creating UDF and UDAF (Contd.)
  • UDFs and Null Handling
  • UDFs and Null Handling (Contd.)
  • Sample UDF Class
  • Build Engine
  • UDAF
  • UDAF Sample Class
  • Supported Types
  • Deploying Custom Functions
  • Using Custom Functions
  • Summary

Free Resources from Intertech

Complete Guide to Becoming a Full Stack Developer

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