Spark, Hadoop, Python, Tensorflow – an increasing number of software, frameworks, and platforms are emerging to help businesses get control over their huge volumes of data, and this can be overwhelming. Kafka software rises, amid the others, as a specifically designed software for speedy, real-time data processing making AI and Big Data possible.
What is Kafka Software?
Kafka software is open-source software that delivers a concrete framework for saving, comprehending and reporting streaming data. Kafka is open-source, which implies that it is free to use, backed by a huge network consisting of developers and users who keep contributing towards Kafka software updates, new features, and providing help to new users. Kafka software has been developed to operate in a distributed environment; hence, instead of being based on one user’s system, it operates across many servers, harnessing more processing power and additional storage that it offers. Kafka was primarily developed by LinkedIn. It was built for analyzing the network of millions of LinkedIn users. In 2011, it gained an opens source status when acquired by Apache Foundation.
The Scope of Kafka Software
Real-time data analysis has become a core need for businesses to remain competitive, as this analysis leads to quick valuable insights and timely decisions and responses. Traditionally, batch-wise data processing was the norm, which had its limitations like the limited speed of CPIs to handle calculations, transfer information and the creepy speed of sensors to identify data.
Being streamlined to avoid these bottlenecks regarding the analysis of incoming data and being distributed in nature, Kafka software can operate faster. Huge clusters can monitor and respond to millions of amendments to a dataset each second. It brings the concept of streaming data in real-time.
Kafka software was primarily developed to monitor the visitors’ behavior at big and highly visited websites like LinkedIn. Through analysis of the clickstream data of each session, businesses could better understand user behavior. This makes it feasible to forecast which blog articles, or product/service listed for sale, a visitor may come back and be interested in.
Kafka has been, since then, widely used and has become a vital component of the stack at PayPal, Spotify, Goldman Sachs, Netflix, CloudFlare and Uber. These all use Kafka software to process their streaming data and analyze systems and customer’s behavior.
There are 4 core APIs in Kafka software:
Kafka Software KPIs
- The Producer API lets an application publish a record’s streams to Kafka topic(s).
- The Consumer API enables the application to subscribe to the topic(s) and do the processing of the stream of records.
- The Streams API lets an application serve as a stream processor, taking an input stream from topic(s) and generating an output stream to output topic(s), thereby, effectively converting the input streams into the output streams.
- The Connector API allows to develop and operate reusable producers or consumers connecting Kafka topics to current data systems.
This efficient blend of messaging, stream processing and storage seems quite unusual but it is vital to Kafka’s role being a streaming platform.
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