In this module, we will set the foundation for the course by providing an overview of its objectives, structure, and prerequisites. You’ll gain a clear understanding of what to expect and how to prepare for success in this learning journey.

In this module, we will dive into the intricacies of data contracts and serialization in Kafka. You’ll explore how serialization enhances Kafka's architecture and examine different serialization formats, such as AVRO, Protobuf, and Thrift, to understand their schema compatibility and applications.

In this module, we will introduce you to AVRO, one of the most popular serialization systems. You’ll explore the reasons behind its popularity and learn how to build a simple AVRO schema to get hands-on experience with its functionality.

In this module, we will guide you through setting up a local Kafka environment using Docker Compose. You’ll practice producing and consuming messages with CLI tools and delve into AVRO serialization by leveraging the AVRO console producer and consumer for hands-on experience.

In this module, we will set up the foundational components for a Greeting App using Gradle. You’ll learn how to configure the project for AVRO support and generate Java records from schema files, preparing the groundwork for seamless AVRO integration.

In this module, we will set up the Greeting App project using Maven as the build tool. You’ll learn how to configure Maven for AVRO support and generate Java records from schema files, ensuring the project is ready for AVRO-based serialization.

In this module, we will guide you through building an AVRO-based Kafka producer and consumer using Java. You’ll learn how to implement serialization and deserialization for seamless data exchange within Kafka topics.

In this module, we will build a real-time Coffee Shop Order Service using AVRO and Kafka. You’ll start with an application overview and progress through project setup, schema creation, and AVRO class generation. Finally, you’ll create producers and consumers to simulate and process coffee shop orders in a real-time streaming scenario.

In this module, we will explore logical schema types in AVRO and their practical applications. You’ll learn to enhance the CoffeeOrder schema by adding logical types like timestamps, decimals, UUIDs, and dates to improve data accuracy and functionality in real-world scenarios.

In this module, we will delve into the inner workings of an AVRO record. You’ll uncover how data is stored, organized, and encoded, gaining insights into its efficiency and compatibility with evolving schemas.

In this module, we will explore the effects of schema changes in AVRO, focusing on the consumer application's failure to process data with an updated schema. You’ll gain practical insights into schema evolution challenges and why a schema registry is crucial for maintaining compatibility.

In this module, we will introduce you to the schema registry and its critical role in handling AVRO schemas effectively. You’ll learn how to publish and consume records using the schema registry, interact with its REST API, and work with "key" fields as AVRO records to enhance data management and compatibility.

In this module, we will focus on data evolution through the lens of schema changes managed by a schema registry. You’ll learn to update project configurations, explore compatibility types like backward, forward, and full, and understand the consequences of modifying AVRO schemas, enabling you to manage data evolution effectively.

In this module, we will cover different schema naming strategies in AVRO and their practical applications. You’ll create a schema for Coffee Update events and learn how to utilize RecordNameStrategy to manage schema versions while ensuring seamless data processing.

In this module, we will create a coffee order service application using Spring Boot, Kafka, and Schema Registry. We'll cover every step, from setting up the project using Gradle or Maven to building RESTful endpoints for creating and updating coffee orders. Additionally, we'll explore how to configure Kafka producers and consumers to publish and process coffee order events, leveraging AVRO for structured data serialization.