Apache Kafka use cases are important to understand. Apache Kafka is an open-source messaging system. There are two different types of messaging patterns available. Point-to-point and publish-subscribe system. From these two systems, publish-subscribe or pub-sub is the popular messaging system.
Kafka works on low latency and delivers messages. It also has a fault-tolerance feature in case of machine failure. To understand Apache Kafka we need to understand the messaging system first.
What is Apache Kafka? Apache Kafka Use Cases.
Big Data requires a large amount of data. To collect such a large amount of data and analyze it is a challenge. Therefore, It requires a data messaging system to collect, manage and transfer the data. Messaging system transfers the data from one system to another.
There are two messaging patterns available:
The Point-to-Point messaging system follows the queue pattern where a single message is transferred to only one consumer only, multiple consumers can get the news from the queue.
In the pub-sub system, the messages with similar data are kept together in a topic. When a consumer subscribes to a particular topic he receives all the data that are mentioned in the topic. Publishers create the messages and consumers are the subscribers who subscribe to a particular topic.
Kafka is a distributed messaging system that Apache develops. Apache Kafka follows the publish-Subscribe pattern. To avoid data loss in case of machine failure, Apache Kafka replicates the data within the cluster.
Apache Kafka can move a large amount of data anywhere from one point to another all at the same time.
Benefits of Apache Kafka
- Apache Kafka is reliable. It has a distributed environment and replicates the data into the cluster to avoid data loss in case of machine failure.
- It is a Scalable system.
- Apache Kafka is durable because the data is persisted on disk faster.
- Apache Kafka is known for its faster performance. It can store Terabytes of data and still has stable performance.
We use Apache Kafka in many scenarios. Some of the important uses cases are mentioned below.
Apache Kafka is a better option for a messaging system. However, the traditional messaging system does not guarantee data safety. Apache Kafka has an inbuilt partitioning system that divides the data into parts for faster transfer through the system. It works with cluster computing techniques where the data is replicated to avoid any data loss. Apache Kafka can also stream a large amount of data on various locations at a single time which most of the traditional messaging systems cannot perform.
As we have seen that Apache Kafka can store a large amount of data. Organizations use Kafka in operational monitoring data. For instance, it can collect all the statistical data from different applications and create centralized feeds of data.
There are many Log aggregation software like Scribe and Flume. Apache Kafka also has a good performance and works under lower latency compared to other software. The Primary process of Log aggregation is to collect log files and event data from all the servers and store them in a central place.
Some popular frameworks like Strom and Spark can read the article, process the data and create a new topic from the processed data, and makes it available for the users. Kafka also has the Stream processing feature and due to its strong durability, it benefits the stream processing feature.
Apache Kafka follows the cluster architecture. And the main components are as follows.
Kafka Broker acts as a node in a Cluster. A cluster consists of a number of nodes similarly Apache Kafka consists a number of brokers. These brokers work simultaneously and achieve load balancing and machine failure.
To manage the coordination between the brokers Apache uses Zookeeper Component. However, each Broker has the capability to read and write a large number of data without impacting the cluster’s performance. Similarly, ZooKeeper assigns a unique id to each broker for identification.
Apache Kafka architecture consists of many brokers. To maintain the coordination between them, Apache Broker uses ZooKeeper. ZooKeeper keeps a track of every broker in the cluster. It also manages the addition of topic and broker into the cluster as well as and removes it.
ZooKeeper alerts the cluster about every newly added broker. It also informs the cluster if any broker fails to perform the task. Kafka partitions the broker into pairs as per the topics. ZooKeeper helps the cluster by enabling the elections to decide which broker will lead the partitioned brokers.
Apache Kafka Producers
Kafka Producers are the source of the data. Therefore, Producers are responsible for writing the messages. It also publishes the messages according to the topics. With the help of Broker Partitioning, Producers can serialize the data and balance the load among the brokers.
Apache Kafka Consumers
Consumers are also known as subscribers. Kafka allows consumers to read the data which the producers publish. A consumer belongs to a consumer group. Each Group is responsible for reading the published messages but only if they are subscribed to read the message.
The Kafka architecture works on four key API’s which are as follows.
This API allows the Produces component to publish the data to one or more topics.
Consumer API allows the consumers to subscribe to one or more topics. After that, the consumers will be able to read the messages to the subscribed topics.
Stream API is responsible for processing the data in Kafka. With the help of this API, Kafka will take the input data from one or more topics, It will then process the data with the use of stream processing paradigm and process the output stream to one or more topics. In other words, the Stream API can process the input stream into the output stream.
The Kafka topics collect and store similar streams or messages. The Connect API is used to manage the process of running the producers and consumers. The Connect API will ensure the updates made on the database and then it keeps track that those changes are also made in the Kafka topics.
- Apache has designed Kafka to perform operations on a large number of many data. It is built to perform heavy tasks on a large scale of data. Therefore, it is not suitable to perform small data. To perform a small amount of messages you can RabbitMQ messages queue.
- Apache Kafka is one of the popular message delivering systems. But cannot perform data operations in real-time. Kafka requires complex communication between producers and consumers, to maintain real-time operations. Therefore, it is better to avoid Apache Kafka in case of real-time processing.
- Kafka is not suitable for simple task queues. Therefore, We should use other applications for task queues.
- Kafka is a cluster server that can process large amounts of data. It stores this data to perform operations. But we do not prefer Kafka for long-term storage. One must use the database must to store data for the long term. Kafka saves data for a short period of time. Instead of Kafka use MongoDB, Cassandra, etc.
Apache Kafka is a popular messaging tool. Similarly, It supports the publish-subscribe pattern. Kafka is Scalable, Reliable, and can perform operations on a large amount of data. Its architecture is based on cluster architecture. Therefore, it has a fault tolerance feature. It also replicates the data for safety in case of machine failure.
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