Apache Kafka In Spring Boot

Apache Kafka is a type of technology that is used to publish and subscribe the messages based on the fault-tolerant messaging system. Kafka was originally developed by LinkedIn and after some time it open-sourced as a project under the Apache Software Foundation.It is used by many companies to build fault-tolerant, scalable data pipelines for streaming data. Kafka is specially designed to handle big amounts of data in real-time and that's why it is used for data-intensive applications. It is simply a publisher-subscriber model where producers publish the data on a topic and consumers subscribe to the topic to consume the data.

Advantages of Apache Kafka

1. Easily accessible: one of the advantages of using Kafka is that all our data gets stored in Kafka, very easily accessible to anyone.

2. Real-Time Handling: It is able to handle real-time data pipelines. Apache Kafka builds a real-time data pipeline that includes storage, processors, analytics, etc.

3. Low Latency: It offers a low latency value of up to 10 milliseconds. The reason behind this is that it decouples the message which lets the consumer consume the message at any time.

4. High Throughput: Because of low latency, it handles many messages of high velocity and volume.
It can support thousands of messages within a second. That's why most companies use Kafka to load a high volume of data.

5. Fault tolerance: It has an essential feature to provide resistance to node or machine failure within the cluster.

6. Reduces the need for multiple integrations: One of the advantages of using Kafka is that the data written by the producer easily go through Kafka we only need to make one integration with Kafka and it automatically integrates us with every consuming and producing system.

Disadvantages of Apache Kafka

1. Do not have a complete set of monitoring tools: The new enterprises and startups are fear to work with the technology named Kafka. Because it doesn't contain a complete set of managing tools.

2. Message tweaking issues: For delivering the messages to the consumer Kafka broker uses system calls. So if the message needs tweaking the performance of this technology gets reduced. So we can say it does not work properly if the message needs to be changed.

3. wildcard topic selection not supported by Kafka: This technology does not support wildcard topic selection. Instead of this, it matches only the correct topic name. The reason behind this is that selecting wildcard topics makes it incapable to address certain use cases.

4. Clumsy Behaviour: If the number of queues increases in the Kafka cluster then it behaves clumsy.

5. Some message paradigms missing: Some message paradigms are missing in Kafka such as point-to-point queues, request or reply, etc.

6. Reduces Performance: By compressing-decompressing the data flow, brokers and consumers reduced the performance of Kafka.

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