First, welcome to this course on Kafka.

Although Kafka is quite simple to install, we decided to make base it on Docker and Docker-Compose. This gives us a couple of advantages:

• Easier installation:
  • As long as you can get Docker to run, we know that the Kafka installation will work
  • Install docker, then simply run the docker-compose file
• Consistency between Windows, Mac and Linux
• The ability to scale up and down the Kafka cluster

This link will lead you to all the labs and examples

DAY 1

• Introduction (Lecture ~ 20 min)
  • Who are we?
  • What is Kafka?
  • Explain first lab
• Verify that everything is installed and working (Lab ~ 20 min)
  • Install Kafka through Docker
  • Run a simple example of Kafka
• Introduction to Kafka (Lecture ~ 30 min)
  • Kafka under the hood
  • What is a topic?
  • What is a partition?
  • What is a producer?
  • What is a consumer?
• Creating a topic and pass a message (Lab ~30 min)
  • Create a topic
  • Run a simple consumer
  • Run a simple producer
• Dissecting the first example (Lecture/Discussion ~ 30 min)
  • Walk-through of the first lab
  • Question and answers
• Design of Kafka topics and partitions (Lecture ~ 30 min)
  • Case study
  • How to select topics?
  • How to select partitions?
• Exercise: Designing topics and partitions (Group Project ~ 20 min)
  • Design topics and partitions

DAY 2

• Evaluation of the designs and suggested solutions (Discussion ~20 min)
  • Discussion of the suggested solution(s)
  • Recommended design of case study
• Implement Topics and Partitions for case study (Lab ~30 min)
  • Define a topic and partition in Kafka
  • Create a consumer and producer
  • Run a test script
• Scaling Kafka (Lecture ~30 min)
  • Kafka Brokers
  • Kafka Clusters
  • Cluster mirroring
  • Consumer groups
• Streaming APIs for Kafka (Lecture ~20 min)
  • What is streaming?
  • Why use streams?
  • Programming to streams
  • Example streams using Spark
• Streaming and IoT Case Study (Lab ~30 min)
  • Consume a stream from Kafka
  • Build a Spark application over the Kafka stream
• Kafka Administration and Integration (Lecture ~30 Min)
  • Integration with Big Data tools (Storm, Spark, Hadoop)
  • Kafka Connect
  • Certified Kafka connectors
  • Kafka administration
  • Kafka monitoring
  • Security
• Exactly once delivery