Apache Kafka was developed by LinkedIn but later became an open-source software. It is a messaging system which is fast and scalable. It was developed to provide low latency and high throughput for real-time data feeds.
An enormous volume of data is used in Big Data, and to collect and analyze all this data, a messaging system is needed. This is where Kafka comes in. Kafka can be used to get data between systems and applications with real-time streaming pipelines. It can also be used to build streaming applications to react to this stream of data.
Apache Kafka structure
The Apache Kafka structure is primarily made of three components: topic, producer, and consumer. It needs ZooKeeper to manage the clusters. ZooKeeper is a file system used to configure the information and coordinate the cluster topology.
Basically, in Kafka, the producers write to Topics. A topic is a log which is a data structure on disk. The consumers read from the topic.
Kafka messages are structural, and so only a certain type of message is produced on a particular topic. In a Kafka cluster, the topic is unique and identified by its name. Though there is no limit to the number of topics that can be produced, once a topic is published, the data within it cannot be changed.
The producers cluster the data to brokers. The producer sends a message to a broker at a speed the broker can handle. This makes the transaction real-time and the producer doesn’t need to wait for data from the broker. When a producer starts, it can also search for new brokers and send messages to them.
Kafka consumers maintain messages that are consumed. When a partition is offset, the consumer acknowledges each offset, assuring that the messages before it are consumed.
A Kafka training course will help you understand the components of the application better, and also give you an understanding of how developers can use it.
Apache Kafka benefits
Here are some benefits of using Apache Kafka.
- With its distributed commit log system, messages persist on disk faster and enable intra-cluster replication. Thus, the application is durable.
- Kafka has a great performance capability, with high throughput for publishing and subscribing to messages. This stability is maintained even when dealing with high terabytes of messages.
- Kafka is a highly scalable system, so it scales messages quickly without requiring any downtime.
- Among the many features of Kafa are partition, replication, and fault tolerance. It allows for replication of data, so it can support multiple messages.
- There is no need for multiple integrations, which reduces wait times.
Why it is popular?
Apache Kafka has gained a lot of popularity recently. The reason for it is that it has shown high performance when it comes to real-time streaming of data. It can also work very well with Apache Storm and Spark. Below are some of the top features of Kafka that make it a preferred choice for major corporations.
Manage high volume
Kafka can manage a high volume of data streams easily.
No data loss
Apache Kafka is fast and requires zero downtime. It also has no data loss.
Kafka uses ingest pipelines to replicate events.
There are various ways and plugins where Kafka can be used. It also offers ways to write new connectors when needed.
The clusters can handle masters and database failures.
Apache Kafka use cases
Below are some among the many use cases of Kafka.
Kafka collects data from the various distributed applications and produces it in centralized feeds.
Organizations use Kafka to collect log from various aggregators and make them available to consumers in a standard format.
Effectively processing stream
Kafka is durable, which enables it to effectively process stream. In streaming, the data is read from one topic and processed to a new topic.
Kafka can be used as a commit log for distributed systems. It re-syncs failed nodes to restore their data and helps with data replication between nodes.
In even sources, changes are logged as a time-ordered sequence of records. Kafka supports a large volume of this stored data.
Apache Kafka is used by many large corporations like Uber, Airbnb, Twitter, etc. The primary reason for its popularity is that it allows for the creation of real-time data feeds. This enables real-time architecture without requiring wait time for data availability. With so many companies adopting the Kafka application, the demand for Kafka developers is also on the rise. Taking a Kafka developer training can help you advance your career in the field.