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தமிழ் | APACHE KAFKA - DIFFERENT TYPES OF KAFKA API PRODUCER CONSUMER STREAMS CONNECT | 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 - DIFFERENT TYPES OF KAFKA API PRODUCER CONSUMER STREAMS CONNECT | InterviewDOT Apache Kafka is a distributed streaming platform that is commonly used for building real-time data pipelines and streaming applications. It provides a set of APIs and tools that allow developers to work with streaming data efficiently. Here's an overview of the different types of Kafka APIs and tools: 1. **Producer API**: - The Producer API is used to publish (or produce) a stream of records to one or more Kafka topics. - Producers are typically used in applications where data is generated and needs to be sent to Kafka for further processing or consumption by downstream systems. - Producers can publish records synchronously or asynchronously and can configure various parameters such as partitioning, compression, and acknowledgment settings. 2. **Consumer API**: - The Consumer API is used to subscribe to one or more topics in Kafka and process the stream of records produced to those topics. - Consumers can be part of consumer groups, where each consumer in the group receives a subset of the partitions for parallel processing. - Consumers can be implemented to process records in batches or individually, and they can also manage offsets to keep track of their position in the Kafka topic. 3. **Streams API**: - The Streams API allows developers to build stream processing applications directly within the Kafka ecosystem. - With the Streams API, you can transform, aggregate, join, and analyze data streams in real-time. - Streams API applications are deployed as regular Java applications and can leverage Kafka's fault-tolerance and scalability features. 4. **Connect API**: - The Connect API is used to build and run scalable, fault-tolerant connectors that integrate Kafka with external systems. - Connectors can be either source connectors, which ingest data from external systems into Kafka, or sink connectors, which export data from Kafka to external systems. - Connectors are run as standalone processes within the Kafka Connect framework, which manages tasks such as parallelization, fault tolerance, and offset management. Each of these APIs and tools serves different use cases within the Kafka ecosystem, allowing developers to build robust and scalable streaming applications and data pipelines. By leveraging these components, organizations can process and analyze data in real-time, enabling them to make faster and more informed decisions based on their data streams.

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:**