Introduction to Apache Kafka :Building Real-Time Data Pipelines
We live in a Data driven world where Data is key for decision making in business organizations . Apache Kafka among others is one of the tools which aids this process;in this real-time Data.
Apache Kafka is an open-source distributed platform that enables the development of real- time Data, event driven application etc. It is designed in a way such that it can handle vast volume of Data , it is scalable and also user friendly.
It is a distributed streaming platform that is equipped with architecture that enables the development of real- time Data pipelines . Due to its low latency and high processing rate, it is an ideal tool for real time streaming.
CORE COMPONENTS OF APACHE KAFKA .
Kafka has several components that facilitate its processes but here only the major components will be discussed.
TOPIC:
A topic is a particular stream of Data . It is similar to a table in a data set. A topic is identified by name and is split into partitions for easy reference. A topic also is use to organize massages . Each partition contains as much topics as possible.
PARTITION:
Topics are organized into partitions .A partition is the smallest storage unit that holds a subset of records in a topic. Each partition is a single log file whose records are written in Read - only method. Once Data are written into a partition, it cannot be changed.
Each message within a particular has an ID called an offset which helps to identify the start and end of a message( smallest unit of Kafka; an array of byte).
With this , consumers can consume the message of their choice form a position by reading from a specific offset address .
CONSUMER
The Kafka consumer API(Application programming Interface) enables an application interact with the producer by subscribing to one or more Kafka topics in a partition.
Also, it makes it possible for the processing of streams of records produced by those topics.
PRODUCER
Apache Kafka producer are client Apps publishing events to topic partition.
It’s API enables an application to publish a stream of records to one or more topics.
BROKERS:
Kafka brokers manages the strongest of messages in the topic(s). A broker can be one or more. Each broker has a specific ID and contain certain partitions.
Real world Application of Apache Kafka
One of the pronounced reasons people use kafka is due to its versatility and robustness .
From across all industries and business organizations, Abacha kafka is used ranging form E-commerce to sales to Telecommunications including other financial institutions.
They all leverage the unique nature of Abacha kafka . It’s ability to handle vast volume of Data is a cherry on the cake for its users.
1.. E- commerce and sales:
The word of commerce is govern by Data . Only those who are able to manage Data survives.
When sales and commerce becomes difficult due to inability to access Real time Data , Apache kafka comes in .
It act as a powerful architecture to enable the streaming of real- time day for various Applications such as:
:: Recommendation of product
:: Managing customer complaint/request
:: Ensuring prompt response to customer actions etc
2:: Telecommunication:
The industry of telecommunications is another organization that employs the services of Apache Kafka to manage large volume of Data.
In the industry , Kafka facilitates :
. Real time Data processing
. Proactive monitoring
. Event streaming etc.
3:: Financial Services:
Financial institutions have always been at the forefront of leveraging advanced technologies.
Apache Kafka holds a key position in this field making real time Data and event streaming possible which is essential for expediting and Decision making process.
Kafka also aids fraud detection .
By enabling real time event and Data processing, it allows banks to analyze transactions as they occur and identify potential fraud in real time.
Moreover, Kafka strengthen decision making in financial services by enabling real time Data streaming..
Quick results and timely responses are critical in the fast-paced world of finance and Kafka ability in these areas makes it ideal for financial institutions.
Conclusion:
Kafkas efficiency and scalability is the main reason why it is chosen among others. It ability to tract financial records stream real time Data makes it one of the best tools around.
Data engineer leverage on these architecture to manage real time Data.
At the end of this article, I’ll provide links on how to install and set up Kafka environment and how to create a basic Pipeline with Kafka by setting up customer an producer.
Catch your soon
Recommendations:
1.. https://youtu.be/BwYFuhVhshI?si=0oHVwrADX175YfrX
Top comments (1)
New to working in a Kafka-based product, definitely needs a glossary!