Apache Kafka

Description

A data engineer is increasingly becoming a critical role for organizations to achieve success. Data engineers are experts in designing, building, and maintaining data-based systems in support of an organization’s analytical and transactional operations. As a big data enabler, Apache Kafka is a fast, scalable, durable, and fault-tolerant messaging system. It supports strong streaming capabilities and it can seamlessly connect to plenty of sources and sinks, as well as other big data ecosystem frameworks, such as Hadoop. This makes Kafka a key enabler for realizing challenging big data systems and a strong skill for data engineers.

Through this Code.Learn the Apache Kafka program, designed in exclusive collaboration with Athens Tech College, participants will be taken through understanding what a messaging system is and then provided with a thorough introduction to Apache Kafka and its internal details. Once the participant grasps the basics, they will be introduced to more advanced concepts such as capacity planning, fault tolerance, and security.

Key Objectives

The key learning objectives of this program can be summarized as follows:

• Introduction to Messaging Systems
• Message Brokers
• Introduction to Kafka – the distributed/clustered messaging platform
• Topics, Partitions, Offsets
• Producers, Consumers
• Kafka Streams introduction
• KStreams, KTables, GlobalKTable
• Kafka Connectors (Connect Source & Sink)

Target Audience

Higher education graduates in one of the following fields:

• Computer Science
• Ιnformatics
• Software Engineering
• Web and Mobile Development
• Computer Engineering
• Database Engineering
• Database Integration Development
• DW Engineering
• BI Engineering
• or any other relevant area

Prerequisite Knowledge

No prerequisite knowledge is required.

Classroom

Sessions can be carried out:

• Live in a physical classroom
• Live online through video conferencing environments
• Using a Hybrid combination of both live physical and online approaches

The teaching method will depend on the conditions at the time the training will run and on the participants’ preferences.