Big data technologies have become really popular with enterprises dealing with extensive data. While using Big data technologies, a considerable number of messages have to be published by the users. Hence, there arises a need for a system that can feed these messages in real-time. Apache Kafka is one of the commonly adopted technologies for this use.LinkedIn created it in the year 2011 and made it open source. Interestingly, it quickly became popular as a complete platform for streaming. Many web giants like Twitter, LinkedIn, etc., are using Apache Kafka to power their applications. Not to mention, this is the reason why there is a sudden surge in people eager to know about Apache Kafka’s working process.

Not only messaging, but Kafka can also do real-time streaming of data, collect big data, and at the same time, can perform real-time analysis. Besides, you can use Kafka with in-memory microservices and feed events to complex event streaming systems (CEP) and IoT or IFTTT-style automation systems.

Besides, Apache Kafka is a fault-tolerant and scalable messaging system that helps enterprises in building distributed applications. It works with the web companies to provide a central hub for the data streams. Also, it acts like a universal data pipeline that can help in building huge database applications. Alongside, the demand for resources equipped with the expertise in Apache Kafka is also rising. The technology is here to stay, and the knowledge can help the IT engineers and administrators greatly.

What is Apache Kafka?

Apache Kafka is a distributed event streaming platform that can handle trillions of events a day. Initially conceived as a messaging queue, Kafka is based on an abstraction of a distributed commit log. Not to mention, Kafka has been evolved from a messaging queue to a full-fledged event streaming platform.

Few highlighted points on Apache Kafka –

-It is a messaging system and, more specifically, a distributed message streaming platform.

-It works on the mode of publish and subscribe.

The streaming platform can essentially publish the stream of records on a message queue and then process them.

-The system runs as a cluster working on one or more servers. Kafka stores the stream of records durably and in a fault-tolerant way.

– Kafka can work with Flume/Flafka, Storm, Spark Streaming, Flink, HBase, and Spark for real-time ingesting, analysis, and processing of streaming data.

– Kafka data stream is used to feed Hadoop BigData lakes.

– Kafka Streams can be used for real-time analytics.

-In the Kafka messaging system, the cluster stored the records in different categories called topics. Topics can have any number of consumers that can subscribe to the data written on a topic.

-A partitioned log is maintained for every topic in a Kafka cluster. The record consists of three parts: key, value, and timestamp.

These are four main parts in a Kafka system:

Broker: This part of Kafka handles all requests from clients, which comes through produce, consume, and also metadata. This keeps data replicated within the cluster. In a Kafka cluster, there can be one or more brokers.

Producer: It sends records to a broker.

Zookeeper: It keeps the state of the cluster including brokers, topics, and users.

Consumer: It consumes batches of records from the broker.

How do the parts work in Kafka?

A Kafka cluster consists of one or more brokers, which are basically servers running in Kafka. Records come from producers into Kafka topics within the broker. At the same time, a consumer pulls records off a Kafka topic. It is possible to run a single broker also. However, that does not give all the benefits of Kafka.

In a Kafka cluster, brokers are managed by Zookeeper. It is recommended to keep three to five Zookeepers in a cluster.

Messages or records are written by the producers on Kafka’s topic from where consumers read the records. Furthermore, the records stay in a cluster until a configurable retention period. Additionally, Kafka retains records in the log from where the consumers can track the log’s position. This position is known as “offset.”

In a Kafka cluster, consumers can read messages from Kafka’s topic. There are two types of consumers in Kafka – low-level consumer and high-level consumer.

In the case of a low-level consumer, offset specifies the topics and partitions from where consumers need to read. It could be a fixed position at the beginning of the log or the end. It is necessary to keep notice so that the same records are not read more than once.

For high-level consumers, which consist of more than one consumer group, each group is specified with “group-id.” A consumer can be added or removed from a group, and based on that; the consumption is rebalanced.

Interestingly, Kafka supports a large number of consumers that pull messages from topic partitions. Consumer grouping help to parallelize message consumption, resulting in high throughput.

Apache Kafka APIs

Learning Apache Kafka also requires the understanding of its core APIs :

Source: https://kafka.apache.org/intro

Producer API: It allows the application to publish the records to the Kafka topics.

Streams API: It allows the application to work as a stream processor that consumes an input stream from a topic and transforms it into an output stream.

Connector API: It allows building producers and consumers who connect the topics to data applications or systems.

Features

In Kafka, client-server communication takes place in a simple and high-performance TCP protocol. This maintains the backward compatibility to work with older versions. Also, there is a Java client for Apache Kafka, and there may be other clients too.

Source: https://www.confluent.io/product/confluent-platform/

Storage System

Apache Kafka works as the storage system as well. The message queues allow for publishing the messages to act as an effective way of storing messages in flight. It writes the data to disk and replicates it. This helps in fault tolerance.

The producers wait for the acknowledgment to write until replication is complete. Kafka’s disk structures allow the same performance irrespective of the size of persistent data on the server.

Stream Processing

Along with reading, write, or store the data streams, there is a need to allow real-time data stream processing. Stream processor in Kafka takes data streams from input topics and processes them. It then sends the continuous data streams to output topics. There are Producer and Consumer APIs for simple processing. Apache Kafka provides a unified Streams API for complex transformations.

Read more – 6 Factors which will change Big data Landscape 2020

Why Is Kafka so Fast?

Kafka relies on zero-copy principles and depends heavily on the OS kernel to move data around quickly. Kafka handles data in batches, which allows efficient data compression and also reduces I/O latency. Kafka avoids random disk access and writes commit logs sequentially. Besides, it allows horizontal scaling through sharding. As a result, it can handle a massive amount of data.

Benefits of Apache Kafka

-Apache Kafka works as an excellent streaming platform that allows the combination of messaging, stream processing, and storage.

-It presents a combination of traditional messaging systems and distributed file system services. This helps in solving the problems like out of order data, performing computations, reprocessing, etc.

-Another advantage of it is that the topic can scale the processing and can have multiple subscribers.

-Kafka offers stronger ordering guarantees.

-There is a higher fault tolerance while processing historical data along with future data arriving constantly.

-Apache Kafka provides quick responses to the downstream systems reliably working on real-time data. At the same time, it works reliably with the customer-facing applications too. There are thousands of companies, including Airbnb, Netflix, Goldman Sachs, Target, etc., that use Kafka largely for their work.

Final words

Apache Kafka is a fastly emerging and growing messaging platform that can resolve complex back-end systems’ problems. It provides a single platform to do everything. Also, it helps in protecting legacy databases as well.

This popular and powerful tool is being used by developers extensively today. Hence, it becomes essential for big data engineers, data scientists, and engineers to have Kafka’s expertise. This will help them in the long run because of the continuous growth in the field.

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