Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://www.geeksforgeeks.org/apache-kafka/apache-kafka/ below:

What is Apache Kafka? - GeeksforGeeks

Apache Kafka is a publish-subscribe messaging system. A messaging system lets you send messages between processes, applications, and servers. Broadly Speaking, Apache Kafka is software where topics (a topic might be a category) can be defined and further processed. Applications may connect to this system and transfer a message onto the topic. A message can include any kind of information from any event on your blog or can be a very simple text message that would trigger any other event.

Kafka is an open-source messaging system that was created by LinkedIn and later donated to the Apache Software Foundation. It's built to handle large amounts of data in real time, making it perfect for creating systems that respond to events as they happen.

Kafka organizes data into categories called "topics." Producers (apps that send data) put messages into these topics, and consumers (apps that read data) receive them. Kafka ensures that the system is reliable and can keep working even if some parts fail.

To understand how Kafka works, it's essential to know about its core components. Let’s take a closer look at each of these:

A Kafka broker is a server that runs Kafka and stores data. Typically, a Kafka cluster consists of multiple brokers that work together to provide scalability, fault tolerance, and high availability. Each broker is responsible for storing and serving data related to topics.

A producer is an application or service that sends messages to a Kafka topic. These processes push data into the Kafka system. Producers decide which topic the message should go to, and Kafka efficiently handles it based on the partitioning strategy.

A topic in Kafka is a category or feed name to which messages are published. Kafka messages are always associated with topics, and when you want to send a message, you send it to a specific topic. Topics are divided into partitions, which allow Kafka to scale horizontally and handle large volumes of data.

A Consumer is an application that reads messages from Kafka topics. Kafka allows consumer groups, where multiple consumers can read from the same topic, but Kafka ensures that each message is processed by only one consumer in the group. This helps with load balancing and allows consumers to read messages starting from any offset.

Partitions allow you to parallelize a topic by splitting the data in a particular topic across multiple brokers.

Kafka uses Apache ZooKeeper to manage metadata, control access to Kafka resources, and handle leader election and broker coordination. ZooKeeper ensures high availability by making sure the Kafka cluster remains functional even if a broker fails.

To know more about Apache Kafka Architecture click on this link - Kafka Architecture

• Topic partition: Kafka topics are divided into a number of partitions, which allows you to split data across multiple brokers.
• Consumer Group: A consumer group includes the set of consumer processes that are subscribing to a specific topic.
• Node: A node is a single computer in the Apache Kafka cluster.
• Replicas: A replica of a partition is a "backup" of a partition. Replicas never read or write data. They are used to prevent data loss.
• Producer: Application that sends the messages.
• Consumer: Application that receives the messages.

With businesses collecting massive volumes of data in real time, there is a need for tools that can handle this data efficiently. Kafka solves several key problems:

1. Real-Time Processing: Kafka is optimized for handling real-time data streams, allowing businesses to process and act on data as it happens.
2. Fault-Tolerant: Kafka ensures that even if parts of the system fail, data won’t be lost, making it a highly reliable messaging system.
3. Scalable: Kafka scales horizontally by adding more brokers, allowing it to handle growing data loads and increasing numbers of producers and consumers.
4. Event-Driven Architecture: Kafka powers event-driven architectures, enabling systems to respond to events in real-time without having to constantly poll for changes.

Apache Kafka moves data from one place to another in a smooth and reliable way. Here’s how it works in simple terms:

• Producers are applications that create data and send it to Kafka.
• This data can be anything—logs, transactions, user activities, or events.
• Kafka splits the data into smaller parts called partitions, making it easier to handle large amounts of information.

• Kafka organizes the data into topics, where it is saved for a certain period.
• Even if a consumer reads the data, Kafka doesn’t delete it immediately.
• To prevent data loss, Kafka makes copies of the data and stores them on different servers.

• Consumers are applications that subscribe to topics and read messages.
• To manage the load, consumers are divided into consumer groups, so no message is processed twice.
• Consumers can choose where to start reading, whether from the newest message or an earlier point.

• ZooKeeper helps Kafka manage which server is in charge of storing and distributing data.
• If a server goes down, Kafka automatically redirects the data to another server.

• Once consumers receive the data, they can store it in a database, analyze it, or trigger other events.
• Kafka can work with tools like Apache Spark, Flink, and Hadoop for deeper analysis.

To know more about how Apache Kafka works click here

Apache Kafka is highly versatile and can seamlessly integrate various data processing models, including event streaming, message queuing, and batch processing.

Kafka’s primary function is event streaming, where:

• Producers (applications sending data) publish messages to Kafka topics.
• Consumers (applications reading data) subscribe to topics and receive messages as soon as they arrive.
• Multiple consumers can read the same message, allowing for real-time data distribution.

Example: A stock trading platform can use Kafka to stream live market data to multiple dashboards.

Kafka can also act like a message queue by using consumer groups:

• When multiple consumers are in the same group, Kafka distributes messages among them, ensuring each message is processed only once.
• This setup helps in load balancing, making sure no single consumer is overwhelmed.

Example: A ride-hailing app like Uber can use Kafka to assign incoming ride requests to available drivers efficiently.

Even though Kafka is designed for real-time data, it can also handle batch processing:

• Messages can be stored in Kafka topics and processed later.
• Tools like Apache Spark or Hadoop can read data from Kafka in batches and perform analytics.

Example: An e-commerce company can collect website visitor data in Kafka and analyze it later to improve product recommendations.

Kafka is flexible enough to support a mix of real-time and batch processing:

• It can send data immediately for real-time analytics while also storing it for batch processing later.
• This is often done using Kafka Streams, Spark Streaming, or Flink.

Example: A fraud detection system can process transactions in real time to flag suspicious activity while also running deeper batch analysis at the end of the day.

Apache Kafka is widely used across various industries. Some popular use cases include:

• Real-time Analytics: Processing data streams for live analytics, like monitoring user activities or stock prices.
• Event-Driven Applications: Kafka powers event-driven architectures, ensuring that systems react in real time to events like user actions, transactions, or sensor data.
• Log Aggregation: Collecting logs from multiple systems into a centralized logging system for better analysis and monitoring.
• Stream Processing: Kafka, along with tools like Apache Flink or Apache Spark, is used to process streams of data in real-time.
• Data Integration: Kafka integrates data between different systems, such as moving data between different microservices or syncing databases.

The following shows the list of companies using Apache Kafka:

Company

Use Case

LinkedIn

Uses Kafka to manage real-time activity streams, news feeds, and operational metrics.

Netflix

Streams real-time data for monitoring, analytics, and recommendations

Twitter

Processes live tweets, trends, and analytics using Kafka.

Uber

Tracks real-time ride locations and processes event-driven data.

Airbnb

Manages real-time booking, pricing, and user analytics.

Spotify

Analyzes music streaming data and user behavior in real time.

Pinterest

Handles event logging and recommendation systems.

Walmart

Uses Kafka for inventory tracking and fraud detection.

Box

Implements Kafka for real-time monitoring and analytics.

Goldman Sachs

Uses Kafka for financial data streaming and trading analysis.

Apache Kafka and RabbitMQ are both popular messaging systems, but they differ significantly in their architecture and use cases:

Feature

Apache Kafka

RabbitMQ

Architecture

Distributed event streaming platform

Message broker with queues

Message Model

Publish-Subscribe (log-based)

Messages are deleted after consumption (unless stored)

Scalability

Producer-Consumer (queue-based)

Message Persistence

Stores messages for a configured retention period

Messages are deleted after consumption (unless stored)

Scalability

Horizontally scalable with partitions and brokers

Scaling is possible but complex

Throughput

High (millions of messages per second)

Lower than Kafka, optimized for low-latency messaging

Latency

Higher latency (optimized for batch processing)

Low latency, real-time messaging

Message Replay

Supports replaying messages from logs

No built-in message replay feature

Delivery Guarantee

At-least-once (default), exactly-once (with configurations)

At-most-once, at-least-once, exactly-once (configurable)

Use Case

Event-driven architectures, real-time data streaming, log processing

Microservices communication, task/job queues, transactional messaging

Routing

Simple topic-based routing

Advanced message routing with exchanges

Protocol Support

Works with TCP-based Kafka protocol

Supports AMQP, MQTT, STOMP, and other protocols

The following are some of the benefits of using Apache Kafka:

Kafka is designed to handle large volumes of data, making it ideal for businesses with massive data streams.

Even if some servers fail, Kafka keeps data safe by making copies.

Perfect for applications that need instant data updates.

Producers and consumers work independently, making it flexible.

Can handle structured, semi-structured, and unstructured data.

With many companies using Kafka, there is a large and active community supporting it, along with integrations with tools like Apache Spark and Flink.

The following are some of the limitations you have to face while using Apache Kafka:

Requires technical knowledge to install and manage.

Since it saves messages for some time, costs may rise.

Guarantees order only within a single partition, not across multiple ones.

Needs extra tools for transforming or analyzing data.

Uses a lot of CPU, memory and network bandwidth.

Better for large data streams; smaller tasks may have unnecessary overhead.

Many companies rely on Apache Kafka because it helps them process large amounts of data in real time. Here’s why it’s so popular:

Kafka can handle massive amounts of data by breaking it into smaller pieces (partitions) and distributing them across multiple servers. This means it can grow as a business’s data needs increase.

Even if some servers fail, Kafka keeps running smoothly because it makes copies of data (replication). This ensures that no important information is lost.

Kafka can work with any type of data since it stores information as byte arrays. Whether it’s logs, events, or structured records, Kafka can handle it all.

Consumers (applications that read data) don’t have to start from the beginning every time—they can pick up exactly where they left off. This makes it easier to process data without interruptions.

Apache Kafka works well with several Apache technologies that help improve data management, processing, and integration. Here’s how they work together:

Kafka relies on ZooKeeper to manage cluster information, such as keeping track of active brokers and handling leader elections. It ensures the system runs smoothly.

Kafka often uses Avro for data serialization. It makes storing and sharing structured data more efficient while allowing schema changes without breaking compatibility.

Kafka and Flink work together to process real-time data streams. Flink helps analyze data as it arrives, making it useful for live monitoring, fraud detection, and event-driven applications.

Spark can read data from Kafka for both real-time and batch processing. It is widely used for machine learning, ETL (Extract, Transform, Load) tasks, and big data analytics.

Kafka streams large amounts of data, and Hadoop provides long-term storage for deep analysis. This combination is useful for businesses handling massive datasets.

For real-time, low-latency processing, Storm works well with Kafka. It helps in applications like tracking live events, detecting unusual activities, or updating dashboards in real time.

Kafka often integrates with different systems using Camel, which acts as a bridge between Kafka and various APIs, databases, or cloud services. It simplifies message routing and data transformation.

NiFi automates data flow between Kafka and other sources or destinations. It helps build scalable data pipelines without needing extensive coding.

These tools make Kafka more powerful, helping companies handle real-time data efficiently.

Apache Kafka is a powerful tool for handling real-time data streams, offering unmatched scalability, reliability, and performance. Whether you're building event-driven architectures, implementing real-time analytics, or aggregating logs, Kafka provides a flexible, fault-tolerant, and efficient solution. With its wide range of use cases and seamless integration with other tools like Apache Flink, Spark, and Hadoop, Kafka continues to be the go-to choice for organizations looking to process large amounts of data in real time.

Apache Kafka | Master Data Science Concepts

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4