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தமிழ் | APACHE KAFKA WHAT IS THE MEANING OF SCHEMA ? WHAT IS SCHEMA REGISTRY EXPLAIN | 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 THE MEANING OF SCHEMA ? WHAT IS SCHEMA REGISTRY EXPLAIN | InterviewDOT Creating a detailed Apache Kafka schema within a 4000-character limit is challenging, but I'll provide a concise overview: 1. **Topic**: Kafka organizes data into topics. Each topic represents a category or feed name to which records are published. 2. **Partition**: Topics are divided into partitions, which allow parallel consumption of data. Each partition is an ordered, immutable sequence of records. 3. **Offset**: Each record within a partition has a unique identifier called an offset, which represents its position in the partition. 4. **Producer**: A producer is responsible for publishing records to Kafka topics. Producers send data to Kafka brokers and specify the topic to which the records should be sent. 5. **Consumer**: Consumers read records from Kafka topics. They subscribe to one or more topics and consume records from specified partitions. 6. **Consumer Group**: Consumers are organized into consumer groups. Each consumer group consists of one or more consumers that jointly consume a topic. Each partition in a topic is consumed by exactly one consumer from each consumer group. 7. **Broker**: Kafka brokers are servers that manage storage and replication of topics. They handle producer requests, consumer fetch requests, and replication of data across the cluster. 8. **Replication**: Kafka replicates topic partitions across multiple brokers to ensure fault tolerance and high availability. Each partition has one leader and one or more replicas. 9. **ZooKeeper**: Kafka relies on ZooKeeper for distributed coordination and configuration management. ZooKeeper maintains metadata about brokers, topics, and partitions, and handles leader election and membership changes. 10. **Schema Registry**: In environments where schema evolution is necessary, the Schema Registry stores and manages schemas for Kafka messages. It ensures compatibility between producers and consumers when message formats change over time. 11. **Message Format**: Kafka messages typically consist of a key, value, timestamp, and optional headers. The key and value can be of any data type and are serialized/deserialized by the producer and consumer. 12. **Retention Policy**: Kafka topics have configurable retention policies that determine how long messages are retained in the topic. Messages can be retained based on time or size constraints. 13. **Log Compaction**: Kafka supports log compaction, which retains only the latest message for each key within a partition. This is useful for maintaining a compact history of updates, particularly in scenarios involving stateful processing. 14. **Security**: Kafka provides security features such as SSL/TLS encryption, authentication using SASL or Kerberos, and authorization using ACLs (Access Control Lists) to ensure data confidentiality and integrity. This overview covers the fundamental components and concepts of Apache Kafka's schema. For more detailed information, further exploration of Kafka documentation and resources is recommended.

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:**