CCDAK: Confluent Developer for Apache Kafka Skills

Contents

What You’ll Learn Requirements Description Who this course is for:

What
You’ll Learn

Apache Kafka Fundamentals: Kafka architecture (brokers
producers
consumers
and topics).
Kafka Development: Writing producer and consumer applications.
Data Modeling in Kafka: Choosing appropriate serialization formats (Avro
JSON
Protobuf).
Kafka Streams and KSQL: Developing streaming applications using Kafka Streams API.

Requirements

Basic Knowledge of Distributed Systems
Basic Programming Skills
Experience with Java or Another JVM-based Language

Description

The CCDAK: Confluent Developer for Apache Kafka course is a comprehensive training program designed to provide developers with the essential skills required to build, deploy, and manage real-time data-driven applications using Apache Kafka and the Confluent platform. Apache Kafka has become the standard for building event-driven architectures and data streaming applications due to its scalability, fault tolerance, and high throughput. This course will enable developers to understand the core principles of Kafka, and how to leverage the power of the Confluent platform for stream processing and data pipeline integration.

In this course, you will explore how to implement real-time data streaming applications, manage Kafka clusters, use Kafka Streams and KSQL for stream processing, and integrate Kafka with other systems. As you progress, you will gain hands-on experience with Kafka’s ecosystem, learning how to set up Kafka brokers, create Kafka topics, produce and consume messages, and use the powerful tools and connectors available within the Confluent platform.

Whether you are a developer aiming to enhance your skills in data streaming, a data engineer looking to work with real-time data pipelines, or a system architect designing event-driven systems, this course will provide you with the skills and confidence to work with Apache Kafka in production environments.

Learning Outcomes

By the end of this course, you will be able to:

Understand Kafka’s Architecture: Learn about Kafka’s components including brokers, topics, partitions, and consumer groups, and how they work together to ensure high throughput, scalability, and fault tolerance.
Produce and Consume Messages: Understand how to write Kafka producers and consumers in Java or other supported languages to interact with Kafka brokers. You will learn the concepts of message serialization, partitioning, and how to optimize message delivery.
Kafka Streams API: Learn how to use Kafka Streams, a client library for stream processing, to transform, filter, and aggregate real-time data. You’ll explore concepts such as stateful processing, windowing, and join operations to manipulate and analyze streams of data in real-time.
Use KSQL for Stream Processing: Discover KSQL, a streaming SQL engine for Apache Kafka, to perform stream processing using SQL-like queries. This tool allows you to create, manage, and analyze Kafka topics with ease.
Leverage Confluent Connectors: Learn how to use Confluent Connectors to integrate Kafka with various external systems like relational databases, cloud storage, NoSQL stores, and other messaging systems. This will allow you to seamlessly stream data between Kafka and other technologies.
Schema Management with Confluent Schema Registry: Learn how to use the Schema Registry to manage Avro schemas and ensure data consistency between producers and consumers. You’ll understand how to evolve schemas over time without breaking backward compatibility.
Monitor and Secure Kafka: Gain insights into monitoring Kafka clusters using tools like Confluent Control Center, Prometheus, and Grafana. Understand how to secure Kafka by configuring SSL, SASL authentication, and implementing authorization strategies with Kafka ACLs.
Deploy and Scale Kafka Clusters: Learn the best practices for deploying Kafka clusters, ensuring high availability, and scaling Kafka deployments to meet the needs of real-time data processing applications.
Kafka Use Cases and Real-World Applications: Explore real-world use cases for Kafka, such as event-driven architectures, data integration, real-time analytics, and log aggregation. Learn how companies are leveraging Kafka to build modern data architectures and solve critical business challenges.