In this tutorial, we will discuss all Kafka Features like scalability, reliability, and durability, which shows why Kafka is so popular. We will discuss each feature of Kafka in detail.
Apache Kafka is hugely popular because of its features that guarantee uptime, make it easy to scale, enable Kafka to handle high volumes, and much more.
What is Kafka?
To handle a high volume of data and enables us to pass messages from one end-point to another, Apache Kafka provides a distributed publish-subscribe messaging system. It is suitable for both offline and online message consumption. Basically, it designs a platform for high-end new-generation distributed applications. It is an open-source tool and is a part of Apache projects.
One of the best features of Kafka is, that it is highly available and resilient to node failures and supports automatic recovery. This feature makes Apache Kafka ideal for communication and integration between components of large-scale data systems in real-world data systems.
Moreover, in order to prevent data loss, Kafka messages are persisted on the disk and replicated within the cluster. In addition, it is built on top of the ZooKeeper synchronization service. While it comes to real-time streaming data analysis, it can also integrate very well with Apache Storm and Spark. There are many more Apache Kafka features.
Imagine you have a program that publishes user data to a database. Normally, if user data is sent to the database and the database is offline, the user data is lost. With Kafka, the user data will wait in a queue for the database to come back online.
Why should we use Kafka?
In the real world, we get data from one system to another system so it is very difficult for us to manage data according to i.e maintaining the order of the data and not storing duplicate data. Apache Kafka will take care of all your pain. You just need to publish the message on the Kafka topic rest leave everything on Kafka. Kafka also comes in handy when you’re using microService architecture.
Apache Kafka Features
Apache Kafka can handle scalability in all four dimensions, i.e., event producers, event processors, event consumers, and event connectors. In other words, Kafka scales easily without downtime.
Apache Kafka can work with a huge volume of data streams, easily.
3. Data Transformations
Apache Kafka offers provisions for deriving new data streams using the data streams from producers.
4. Fault Tolerance
Apache Kafka clusters can handle failures with the masters and databases.
Since Apache Kafka is distributed, partitioned, replicated, and fault-tolerant, it is very reliable.
Apache Kafka is durable because it uses Distributed commit logs, which means messages persist on disk as fast as possible.
For both publishing and subscribing to messages, Kafka has high throughput. Even if many TBs of messages are stored, it maintains stable performance.
8. Zero Downtime
Apache Kafka is very fast and guarantees zero downtime and zero data loss.
Kafka MirrorMaker provides replication support for your clusters. With replication features, messages are replicated across multiple data centers or cloud regions. You can use these inactive/passive scenarios for backup and recovery, or inactive/active scenarios to place data closer to your users, or support data locality requirements.
10. Free To Use
These are the main features of Apache Kafka which make Kafka a powerful tool for managing the real-time stream of data.