Apache Kafka

Latest Releases of Kafka

As of now, 3.3.1 is the latest version of Apache Kafka. This is the most stable version among all the previous versions. The latest version was released on October 3, 2022. You can download the kafka-3.3.1-src.tgz (asc, sha512).

Some of the previous versions of Apache Kafka are listed below:

• 3.3.0: There were some bugs in this version, so the decision was made not to release this version but rather release the next version with the fix.
• 3.2.3: This version was released on September 19, 2022.
• 3.2.1: This version was released on July 29, 2022.
   

There are many more previous versions that came, but the current version is the safest as of now.

Working of Apache Kafka?

 A distributed streaming platform called Apache Kafka is made to handle real-time data feeds. Producers, Brokers, and Consumers make up its essential parts. Producers publish data to logical channels called Kafka topics. Brokers handle and store topics, ensuring the scalability and durability of data. 

Consumers process data in real time and subscribe to topics. Kafka allows consumers to replay and digest data at their own leisure by keeping data available for a defined retention time. Kafka is an effective tool for creating data pipelines, event sourcing, and stream processing systems because it allows for scalable, fault-tolerant, and real-time data streaming.

Apache Kafka combines two messaging tools to work properly.

1. Queuing
2. Publish-Subscribe
    

Let us understand both messaging tool one by one.

Queuing: Queuing is very scalable since it enables the distribution of data processing across various customer instances. Yet, traditional queues aren't multi-subscriber.

Publish-Subscribe: The publish-subscribe method supports multiple subscribers, as each message is sent to each subscriber individually. It can't be used to divide work among several worker processes.

Kafka combines these two ideas using a partitioned log model to stitch together these two solutions. This enables greater scalability by enabling multiple subscribers to the same subject, each of whom is given a partition.

Finally, Kafka's model gives replayability. This allows multiple independent apps reading from data streams to work independently at their own rate.

Need for Kafka

Apache Kafka meets several essential requirements for contemporary distributed systems and data processing:

• Real-time Data Streaming: Kafka's ability to handle real-time data dissemination and ingestion makes it the perfect choice for fast data processing programs.
   
• Scalability: Kafka is extremely scalable and capable of handling enormous volumes of data and traffic. By including more brokers and partitions, it scales horizontally.
   
• Data Persistence: Kafka's data persistence feature ensures that data is kept alive even if consumers crash or briefly disconnect. For data integrity and reliability, this durability is essential.
   
• Decoupling Data: Data producers and consumers can now work independently and at their own pace thanks to Kafka's decoupling of the two groups. Better fault tolerance and flexibility in system architecture are made possible as a result.
   
• Event Sourcing: Kafka is frequently used in event sourcing systems as a trustworthy event log to record all alterations to an application's state over time.
   
• Log Aggregation: It is useful for combining logs from different sources, including server logs, application logs, and more. Monitoring and analysis are made simpler by this centralised logging.
   
• Data Integration: Kafka is a central hub for data integration, enabling effective communication and data sharing between diverse systems and applications.

What is Kafka Used For?

Apache Kafka is used for many purposes. Let us discuss them one by one.

• Apache Kafka is primarily used as a message broker. Message brokers are used to buffering unprocessed messages.
• Apache Kafka is used for Website Activity Tracking.
• Apache Kafka is also used for monitoring data.
• Apache Kafka is a kind of external commit log for a distributed system.
• A user can also use Apache Kafka for Event Sourcing.

Advantages of Apache Kafka

The advantages of Apache Kafka are listed below:

• The processing speed of Apache Kafka is much faster than other competitors in this field.
• The performance of Apache Kafka is very stable.
• Apache Kafka offers permanent storage for streaming safely and securely.
• Apache Kafka offers low latency that can deliver a high volume of messages using a cluster.
• Apache Kafka offers high scalability to the user for a better experience.

Disadvantages of Apache Kafka

If there are pros to anything, there will also be some cons. So, let's now discuss some disadvantages of Apache Kafka.

• There is not a complete set of monitoring tools for Apache Kafka.
• For some use cases, there is a lack of message paradigms.
• Apache Kafka also shows clumsy behaviour when the number of queue increase.
• Apache Kafka does not support the wild card topic selection.
• There are also some message tweaking issues in Apache Kafka.

Frequently Asked Questions

What is Apache Kafka used for?

Kafka is widely used in the big data space as a dependable method. It helps to quickly ingest and move large amounts of data. The reason is its performance characteristics and scalability.

What is Apache Kafka and how it works?

Producers publish data to topics, Brokers store and maintain topics, and Consumers subscribe to and process data to make the system operate. To ensure scalability, fault tolerance, and real-time data streaming, Kafka separates data production from data consumption.

Is Apache Kafka an API?

Apache Kafka isn't an API. It is a distributed streaming platform with several APIs that allow users to communicate with the message broker of Kafka. It offers resources and libraries for creating applications that stream real-time data.

What is meant by Apache Kafka?

Apache Kafka refers to both the open-source distributed streaming platform and the ecosystem of tools and libraries built around it. It is essential to creating scalable and low-latency data streaming systems because it is made for real-time data ingestion, storage, and delivery.

Conclusion

This article discusses the topic of Apache Kafka in detail, we have also seen its definition, releases, pros, and cons. Along with this, we have seen the usage and working of Apache Kafka.

We hope this blog has helped you enhance your knowledge of Apache Kafka. If you want to learn more, then check out our articles.

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3. Apache Tomcat Interview Questions
    

Happy Learning!