What is Kafka?
Kafka is a messaging model that provides real-time data streaming. It was developed by LinkedIn in 2011 as a platform analytics tool for user activity at scale in social networking. It ensures message order inside a partition and provides parallel message processing across many partitions. It features a highly scalable design and can run on a cluster of machines.
Kafka is a reliable and fault-tolerant data processing system that is highly available, resilient to node failures, and offers automated recovery. It provides a Kafka broker, a Kafka Producer, and a Kafka Consumer. A Kafka broker is a node used to persist and duplicate data, while a Kafka Producer inserts messages into the Kafka Topic message container.
What is a Messaging System?
A messaging system enables multiple applications or systems to communicate with one another by exchanging messages. Two types of patterns in messaging systems :
P2P (Point - to - Point) :
- P2P messaging sends messages from one sender to one receiver, which is useful for one-to-one communication. Messages are stored in a queue, which can only be eaten by one consumer at a time, and are removed as soon as they are read by consumers.
Pub/Sub messaging :
- Pub/Sub messaging sends messages from one sender to multiple receivers, who receive a copy of the message when sent to a specific topic. Kafka consumers have the option to subscribe to one or more topics and read every message inside those topics.
What is Streaming Process?
The streaming process is the continual and real-time processing of data streams, which involves handling data as it is generated or produced. It is beneficial for various scenarios, such as real-time data analytics, system log monitoring, sentiment analysis on social media, and handling data in IoT applications.
Why Apache Kafka?
Kafka is a scalable data processing system with low latency capacity for handling massive amounts of data. It can replicate data for fault tolerance and high availability, and distribute data over multiple nodes in a cluster. It can be used for data streaming, messaging, and event-driven architectures, and supports a number of APIs. It is faster than SQL and NoSQL database storage.
Kafka Use Cases
Banking : Financial institutions use Apache Kafka for real-time regulatory compliance, cybersecurity, and fraud detection, as well as stock market trading applications. Retail : Companies use Apache Kafka to create omni-channel experiences, manage deliveries, inventory, and recommend products, and monitor user traffic. Healthcare : Data streaming applications such as IoT devices and HIPAA-compliant record-keeping systems are essential for medical staff to respond to system warnings.
Companies using Kafka
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Uber/Lyft:
Kafka helps Uber and Lyft connect customers and drivers globally, allowing them to create software solutions that optimise ride-sharing and taxi reservations. ML can be used to further enhance driver matching and route optimisation.
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Twitter:
Social media businesses use Apache Kafka to customise content recommendations for hundreds of millions of users, using algorithms, machine learning, omni-channel, and event streaming architecture for "big data" processing.
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Splunk:
Prometheus, Grafana, Splunk, and Datadog are utilised with Kafka-based Application Performance Monitoring (APM) solutions for cloud SaaS and SDDC administration on multi-cloud hardware.
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Netflix :
Netflix has embraced Apache Kafka to use the publish/subscribe model to make media suggestions to their user base. It is referred to as "the de-facto standard for its eventing, messaging, and stream processing needs" in the cloud.
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Walmart:
Walmart is a Fortune 500 company using Apache Kafka and a streaming data architecture to optimise internal processes using data-driven techniques and timestamp tracking in real-time.
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Tesla:
Tesla uses Apache Kafka's event streaming architecture to develop, manufacture, and supply chain manage autonomous vehicles.
Design Goals of Apache Kafka
Overall, Kafka was created to provide a platform for processing massive data streams that is scalable, fault-tolerant, high-throughput, durable, and real-time.
- Scalability: Kafka is designed to handle large data streams and high volumes of messages per second by partitioning data across multiple servers, enabling horizontal scaling.
- Kafka is designed to be highly available and fault-tolerant, with partitions replicated over multiple brokers to ensure data availability in the event of a broker failure.
- High throughput: Kafka was created to be optimized for high throughput, meaning it can process a lot of messages quickly.
- Durability: Kafka was created with durability in mind, making ensuring that data is not lost in the event of a breakdown. Replication and the durability of data on disc are used to achieve this.
- Processing of data streams in actual time : It is made possible by Kafka, allowing for the consumption of data as it is being produced.
Kafka Framework – Core APIs
In Kafka Frameworks, main APIs are offered which include the following:
- Kafka Topic
- Producer API
- Consumer API
- Connectors
- Stream Process
1.Kafka Topic:
Kafka is a data management system that divides topics into partitions and assigns each one a name. Each partition contains a set of messages that are sequentially sorted, immutable, and updated as new messages are published.
2.Producer API
Kafka's Producer API enables applications to publish data to a Kafka topic. An application must build a KafkaProducer class instance, which can be shared by multiple threads due to its thread-safe characteristics.
3.Consumer API
Applications can use the Consumer API to consume data from Kafka topics. Developers can choose the offset from which to start consuming data and the maximum amount of records to read in a single poll, among other customization options.
4.Connectors
Applications can combine Kafka with other platforms and systems via the Connect API. It offers a structure for creating connectors that can transfer data between Kafka and other data sources, like databases or file systems.
5.Stream Process
Applications can process data streams in real-time using the Streams API. It makes it possible for programmers to create intricate data processing programmes that can take in data from one or more Kafka topics, process it, and publish the findings to another topic.
About this Kafka Tutorial
The Scaler Kafka Tutorial is a thorough online course that introduces Apache Kafka in depth. The course covers from fundamentals of what is kafka about, its core APIs and its architecture, companies using Apache Kafka, and also which sectors are rigorously using Apache Kafka as a technology stack for their products and systems. Whether you’re a newbie or an experienced developer or a data person, this lesson will help you learn more about Apache Kafka.
Audience
The course of Apache Kafka is intended for those who are new, developers, and anyone who is eager to learn.The purpose of this course is to help professionals who want to work in the field of Big Data Analytics with the Apache Kafka messaging system. It will provide you with sufficient knowledge to use Apache Kafka with real time analytics.This tutorial of Apache Kafka will help to grow any individual its being used in small scale to large scale applications.
Prerequisites
To get the most out of the Scaler Apache Kafka Tutorial, you should have a basic understanding of Java as it was also used to create the native API for Kafka. Other programming languages, like as Go, Python, or C#, can be used to create application code that communicates with Kafka. It's expected to have some basic knowledge related to event streaming and messaging systems. However, even if you’re unfamiliar with these concepts, the course is designed to be straightforward.
