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தமிழ் APACHE KAFKA - WHAT IS KAFKA CLUSTER | APACHE KAFKA ARCHITECTURE - INTERVIEW | InterviewDOT

LEARN APACHE KAFKA IN 2 MINUTES TAMIL TUTORIAL SERIES

Click here - https://www.youtube.com/channel/UCd0U_xlQxdZynq09knDszXA?sub_confirmation=1 to get notifications. தமிழ் APACHE KAFKA - WHAT IS KAFKA CLUSTER | APACHE KAFKA ARCHITECTURE - INTERVIEW | InterviewDOT Apache Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and streaming applications. It is designed to handle high-throughput, fault-tolerant, and scalable streaming data. A Kafka cluster typically consists of multiple brokers, which are servers responsible for storing and managing Kafka topics, partitions, and messages. Here's an overview of a Kafka cluster in around 4000 characters: Apache Kafka operates as a distributed system, meaning it runs on multiple servers (brokers) that communicate with each other to manage the streaming data. Each Kafka broker is identified by a unique ID and can handle a portion of the overall workload. Brokers are usually deployed across multiple machines or nodes for fault tolerance and scalability. The core abstraction in Kafka is the topic, which is a stream of records or messages. Topics are divided into partitions, which are the basic unit of parallelism and scalability in Kafka. Each partition is an ordered, immutable sequence of messages and can be replicated across multiple brokers for fault tolerance. Kafka uses ZooKeeper for cluster coordination and management. ZooKeeper helps maintain metadata about Kafka topics, brokers, and partitions, as well as handles leader election, membership changes, and configuration management within the Kafka cluster. Producers are applications that publish data records to Kafka topics. They write records to specific topics and partitions, or Kafka can automatically assign partitions based on a partitioning strategy. Producers can choose to receive acknowledgments for data writes to ensure reliability. Consumers are applications that read data records from Kafka topics. Consumers subscribe to one or more topics and consume messages at their own pace. Kafka supports both traditional consumer groups, where each message is consumed by only one member of the group, and consumer groups using the new consumer API, which allows multiple consumers to read from a partition simultaneously. Kafka offers fault tolerance through partition replication. Each partition can have multiple replicas, with one designated as the leader and the others as followers. The leader handles all reads and writes for the partition, while followers replicate the leader's data asynchronously. If a broker hosting a leader partition fails, one of the followers is elected as the new leader, ensuring continuous availability and durability of data. Scaling Kafka involves adding more brokers, partitions, or consumers to the cluster. Horizontal scaling can be achieved by adding more brokers to distribute the load across more machines or by adding more partitions to existing topics to increase parallelism. Kafka's distributed nature allows it to handle large volumes of data and high-throughput workloads. Kafka provides strong durability guarantees by persisting messages to disk and replicating them across multiple brokers. It can retain messages for a configurable retention period, allowing consumers to replay past data if needed. Kafka also supports log compaction, which retains only the latest value for each key within a partition, useful for maintaining a compact history of updates for stateful applications. Overall, Apache Kafka is a powerful platform for building real-time streaming applications, offering high performance, fault tolerance, scalability, and durability for handling large-scale data pipelines and event-driven architectures.

About the course

**Apache Kafka Messaging System in 4000 Characters:** **Introduction:** Apache Kafka is an open-source distributed streaming platform designed for building real-time data pipelines and streaming applications. Developed by the Apache Software Foundation, Kafka has become a cornerstone technology for organizations dealing with large-scale, real-time data processing. **Key Concepts:** 1. **Publish-Subscribe Model:** - Kafka follows a publish-subscribe model where producers publish messages to topics, and consumers subscribe to those topics to receive the messages. This decouples data producers and consumers, enabling scalable and flexible architectures. 2. **Topics and Partitions:** - Data is organized into topics, acting as logical channels for communication. Topics are divided into partitions, allowing parallel processing and scalability. Each partition is a linear, ordered sequence of messages. 3. **Brokers and Clusters:** - Kafka brokers form a cluster, ensuring fault tolerance and high availability. Brokers manage the storage and transmission of messages. Kafka clusters can scale horizontally by adding more brokers, enhancing both storage and processing capabilities. 4. **Producers and Consumers:** - Producers generate and send messages to Kafka topics, while consumers subscribe to topics and process the messages. This separation allows for the decoupling of data producers and consumers, supporting scalability and flexibility. 5. **Event Log:** - Kafka maintains an immutable, distributed log of records (messages). This log serves as a durable event store, allowing for the replay and reprocessing of events. Each message in the log has a unique offset. 6. **Scalability:** - Kafka's scalability is achieved through partitioning and distributed processing. Topics can be partitioned, and partitions can be distributed across multiple brokers, enabling horizontal scaling to handle large volumes of data. **Use Cases:** 1. **Real-time Data Streams:** - Kafka excels in handling and processing real-time data streams, making it suitable for use cases like monitoring, fraud detection, and analytics where timely insights are crucial. 2. **Log Aggregation:** - It serves as a powerful solution for aggregating and centralizing logs from various applications and services. Kafka's durability ensures that logs are reliably stored for analysis and troubleshooting. 3. **Messaging Backbone:** - Kafka acts as a robust and fault-tolerant messaging system, connecting different components of a distributed application. Its durability and reliability make it a reliable backbone for messaging. 4. **Event Sourcing:** - Kafka is often used in event sourcing architectures where changes to application state are captured as a sequence of events. This approach enables reconstruction of the application state at any point in time. 5. **Microservices Integration:** - Kafka facilitates communication between microservices in a distributed system. It provides a resilient and scalable mechanism for asynchronous communication, ensuring loose coupling between services. **Components:** 1. **ZooKeeper:** - Kafka relies on Apache ZooKeeper for distributed coordination, managing configuration, and electing leaders within the Kafka cluster. ZooKeeper ensures the stability and coordination of Kafka brokers. 2. **Producer API:** - Producers use Kafka's Producer API to publish messages to topics. The API supports asynchronous and synchronous message publishing, providing flexibility for different use cases. 3. **Consumer API:** - Consumers use Kafka's Consumer API to subscribe to topics and process messages. Consumer groups allow parallel processing and load balancing, ensuring efficient utilization of resources. 4. **Connect API:** - Kafka Connect enables the integration of Kafka with external systems. Connectors, available for various data sources and sinks, simplify the development of data pipelines between Kafka and other systems. 5. **Streams API:** - Kafka Streams API facilitates the development of stream processing applications directly within Kafka. It enables transformations and analytics on streaming data, supporting real-time processing scenarios. **Reliability and Durability:** 1. **Replication:** - Kafka ensures data durability through replication. Each partition has a leader and multiple followers, with data replicated across brokers. This replication mechanism ensures fault tolerance and data redundancy. 2. **Retention Policies:** - Kafka allows the configuration of retention policies for topics. This determines how long messages are retained in a topic. Retention policies support both real-time and historical data analysis. **Ecosystem and Integration:**