Apache Kafka is a distributed streaming platform developed by LinkedIn and later open-sourced as an Apache Software Foundation project. It is designed to handle real-time data feeds and processing pipelines. Kafka is used for building real-time data pipelines and streaming applications. It is capable of handling high volumes of data and provides features like fault tolerance, scalability, and durability. At its core, Kafka is a distributed messaging system that allows producers to publish streams of records, and consumers to subscribe to these streams, process them in real-time, and store them in various data systems or perform analytics.
Key aspects of Apache Kafka: -
Topics: Kafka topics are the feeds of messages in categories. Producers write data to topics and consumers read data from topics.
Partitions: Topics can be divided into partitions, which allows for parallel processing and scalability. Each partition is ordered, and messages within a partition are assigned an offset.
Brokers: Kafka brokers are servers responsible for storing and managing the topic partitions. They handle the storage, replication, and distribution of data across the cluster.
Producers: Producers are applications that publish data to Kafka topics. They write data to Kafka brokers, which then distribute it across partitions.
Consumers: Consumers are applications that subscribe to Kafka topics and process the data. They read data from partitions and can be part of a consumer group for load balancing and fault tolerance.
Consumer Groups: Consumers can be organized into consumer groups, where each group reads data from a topic partition. This allows for parallel processing and scaling of consumer applications.
Kafka as a giant library where books (messages) from different authors (producers) are stored. Each book belongs to a specific section (topic) in the library and is placed on a shelf (partition) alongside other books in that section. The books on each shelf are arranged in order of when they were placed there (offset), and each book has a timestamp to show when it was added. Visitors (consumers) can come to the library and read books from any section they're interested in. Additionally, Kafka has a special service where people can write down their thoughts about the books they've read and leave them in the library for others to see (Streams API). This service helps people share their insights and ideas with each other. The library is managed by a team of librarians (brokers) who make sure the books are organized correctly and that there are copies of popular books on multiple shelves (partition replication) to ensure they're always available, even if one shelf gets too crowded or breaks. With Kafka's transactional feature, it's like the librarians can ensure that when someone borrows a book and returns it, it's done exactly once, without any mistakes. This makes the whole borrowing and returning process smoother and more reliable.
Apache Kafka as a sophisticated post office. In this post office, you can set up different mailboxes (topics) for various types of mail (messages). You can send letters (messages) to these mailboxes about anything, from a notification on your blog to a simple text that triggers another action. Kafka Broker In a Kafka setup, you have several servers (called Kafka brokers) acting as the post office workers. These brokers manage everything. There are senders (producers) who drop their letters (messages) into the appropriate mailboxes (topics) managed by these brokers. On the other side, you have receivers (consumers) who pick up the letters (messages) from the mailboxes (topics) they are interested in.
Topic Partition: Kafka topics are split into smaller pieces called partitions. This lets you spread data across different servers.
Consumer Group: This is a group of consumers (applications) that read messages from the same topic together.
Node: A single computer in a Kafka cluster.
Replicas: Backups of partitions that don't read or write data but are there to prevent data loss.
Producer: An application that sends messages.
Consumer: An application that receives messages.
Real-Time Applications
Apache Kafka is a distributed streaming platform designed to handle high-throughput, low-latency data streams. It is widely used for building real-time data pipelines and streaming applications. Kafka is highly scalable, fault-tolerant, and designed for distributed data processing.
Key Components
Kafka Architecture
Kafka's architecture is designed for scalability and high availability:
Each broker in a Kafka cluster can handle data partitions and balance the load across the cluster. Partitions can be replicated across multiple brokers to ensure data durability and availability.
Key Features
How Kafka works: -
7.Exactly-Once Semantics (EOS):
Kafka Ecosystem
Kafka's architecture consists of several key components:
Use Cases
Conclusion
Apache Kafka is a robust and versatile platform for handling real-time data streams. Its ability to scale, combined with high throughput and low latency, makes it a preferred choice for many large-scale, data-intensive applications. By providing strong guarantees around data durability and fault tolerance, Kafka ensures that data is processed reliably and efficiently.
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