Apache Kafka Advance

Apache Kafka Advance

Apache Kafka Advance

We assume that you already have understood the basics of Kafka, we will move to the advance topics.

Prerequisite: If you are new to Apache Kafka, it would be advisable for you to first complete Self-paced Apache Kafka Basic course.

Do you want to join live class instead and learn Apache Kafka directly from an expert trainer? Register for upcoming Apache Kafka Advance live boot-camp and up skill your understanding & knowledge about Apache Kafka.

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    1. A multi-node cluster involves multiple interconnected computers or servers, referred to as nodes. These nodes work together to distribute the workload, improving performance, fault tolerance, and scalability. Tasks can be distributed among nodes, allowing parallel processing and more efficient resource utilization. Multi-node clusters are often used in high-performance computing (HPC), data processing, and distributed computing environments.
    2. A multi-node Apache Kafka cluster is a distributed and scalable messaging system that comprises multiple Kafka broker nodes working together to handle the storage, processing, and distribution of data. Apache Kafka is designed for high throughput, fault tolerance, and horizontal scalability. Here's an overview of the key components and concepts involved in a multi-node Kafka cluster:
    3. Applications that read data from Kafka topics are known as consumers. Applications integrate a Kafka client library to read from Apache Kafka. Excellent client libraries exist for almost all programming languages that are popular today including Python, Java, Go, and others.
    4. In Apache Kafka, producers and consumers exchange messages in the form of key-value pairs. When working with Kafka, it's essential to serialize the data into bytes before sending it to Kafka and deserialize it back into its original format when consuming messages. Kafka allows us to use custom serializers and deserializers for keys and values even though  Publisher API provides serializers like IntegerSerializer, StringSerializer etc, same sense of deserializer. The serializer is used by the message publisher while deserializer is used by the message consumer. In short-form it refers as Kafka SerDe
    5. Kafka Replication means having multiple copies of the data, spread across multiple servers/brokers. This helps in maintaining high availability in case one of the brokers goes down and is unavailable to serve the requests. Data Replication helps prevent data loss by writing the same data to more than one broker. In Kafka, replication means that data is written down not just to one broker, but many. The replication factor is a topic setting and is specified at topic creation time. This replication factor is configured at the topic level, and the unit of replication is the topic partition.
    6. Kafka Connect is a framework for scalably and reliably streaming data between Apache Kafka and other systems. It makes it simple to quickly define connectors that move large data collections into and out of Kafka. Kafka Connect can ingest entire databases or collect metrics from all your application servers into Kafka topics, making the data available for stream processing with low latency. An export job can deliver data from Kafka topics into secondary storage and query systems or into batch systems for offline analysis.
    7. Apache Kafka connectors are components that allow you to integrate Kafka with other systems, enabling the seamless transfer of data between Kafka and various data sources or sinks. There are two main types of Kafka connectors: source connectors and sink connectors.
    8. A schema registry is a centralized service that manages schemas for data exchanged between systems in a distributed architecture, and it is often used in conjunction with Apache Kafka. The primary purpose of a schema registry is to enforce a shared schema for the data that flows through a messaging system, ensuring consistency and compatibility between producers and consumers of data.

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