In this module, we will provide an introduction to Apache Kafka, exploring its origins and how it fits within data pipelines. You’ll also get familiar with essential Kafka terminology and its client APIs that are fundamental for messaging and stream processing.

In this module, we will dive deep into Kafka's internal components, such as topics, partitions, and replication. You’ll learn how to set up a Kafka broker environment locally and explore Kafka’s distributed architecture for high-throughput stream processing.

In this module, we will introduce the Library Inventory application that serves as the basis for Kafka integration throughout the course. You’ll learn how Kafka is leveraged to streamline and manage events within the system.

In this module, we will guide you through building a Kafka producer using Spring Boot. You’ll start by setting up the base project and then proceed to develop the logic for producing messages, including using KafkaTemplate for message publishing.

In this module, we will explore the importance of integration testing for Kafka applications. You’ll learn how to write and configure tests for the Kafka producer and consumer, leveraging Embedded Kafka for a reliable and isolated testing environment.

In this module, we will cover the essentials of unit testing for Spring Boot applications. You’ll learn how to write unit tests for your REST APIs and implement error handling for invalid requests, ensuring robust error management.

In this module, we will focus on creating a PUT endpoint to update Library Events and send messages with specific keys. You’ll understand how key-based partitioning works in Kafka and its importance for data distribution.

In this module, we will explore Kafka producer configurations that impact performance and reliability. You’ll learn how to adjust important settings like retry behavior to handle network issues or broker failures.

In this module, we will focus on setting up a Spring Boot Kafka consumer. You will configure Kafka consumers with Spring profiles, and use the @KafkaListener annotation to handle incoming Kafka messages.

In this module, we will explore how Kafka consumer groups operate, and demonstrate the effects of rebalancing on message consumption. You’ll learn both default and manual offset management strategies in Spring Kafka, and configure concurrent consumers for parallel message processing to optimize your Kafka consumer setup.

In this module, we will set up an H2 in-memory database to persist Library Events. You will also learn to create JPA entities for LibraryEvent and Book, and develop the service layer to handle adding and modifying events, ensuring smooth database interactions for Library Events.

In this module, we will configure Embedded Kafka to test consumer behavior in isolation. You will create integration tests to validate both new and updated Library Events, as well as handling error scenarios such as invalid updates. Additionally, we'll explore using TestContainers for database-driven integration tests.

In this module, we will dive into Kafka consumer error handling, building custom error handlers, and configuring retry policies with exponential backoff. You will also implement recovery strategies such as using retry topics, dead-letter topics, and saving failed messages to databases for manual intervention and further processing.

In this module, we will focus on error handling and retry strategies for Kafka producers. You will learn how to configure retries when Kafka brokers are unavailable or when replication requirements are violated, and how to manage failed messages for recovery.

In this module, we will cover Kafka security, specifically focusing on SSL encryption for secure data transmission. You will learn how SSL works in Kafka, how to set up SSL in a local cluster, and best practices for managing SSL certificates at an enterprise level.

In this module, we will guide you through securing your Spring Boot applications to interact with an SSL-enabled Kafka cluster. You’ll learn how to configure both the Kafka producer and consumer to securely connect and exchange data with the Kafka cluster using SSL encryption.