In today’s era, the analysis of real-time data is becoming critical for SMEs & Large Corporations alike. Industries such as Financial Services, Legal services, IT operation management services requires the analysis of massive amount of real-time data as well as historical data which has to process.
Big data is defined by velocity, volume, and variety of the data and these characteristics make Big data different from regular data. Unlike regular big data applications, real-time applications must initiate fast actions that are usually bounded by specific time frames dictated by the targeted domain, therefore to handle this massive volume and high-velocity data there is a need of highly reliable data processing system.
To handle these extremely high volume data we need some high throughput messaging system and Kafka is the best solution for this.
What does Kafka do?
The question is how Apache Kafka can be used to build a data pipeline (high throughput messaging system). Before answering this one should know what Apache Kafka is?
Apache Kafka is a scalable, durable, fast and the fault-tolerant publish-subscribe messaging system. It can be used to solve the following use cases:
- Stream Processing
- Messaging system.
- Activity Tracking
- Monitoring of Data.
- Event sourcing
- Log message tracking
Building real-time data pipelines
There are many ways of implementing real-time data pipelines, and for every business, it looks different. For building any data pipeline, there are few principles which have to follow:
- Data should process in such a way that whenever it reaches to the database, it should available for Query.
- The data store should run analytics with minimal delay.
- Converge the information database with the system of insight.
There are a couple of use cases which can be used to build the real-time data pipeline using Apache Kafka. The first one is when we want to get data from Kafka to some connector like Amazon AWS connectors or from some database such as MongoDB to Kafka, in this use case Apache Kafka used as one of the endpoints. The second use case is building the data pipeline where apache Kafka used as an intermediate. For example, suppose a person wants to send the data from some social networking service like Twitter to Kafka and then from Kafka to some kind of search engine.
To support fault tolerance, and to provide the scalable messaging Apache Kafka uses the following four basic concepts:
Topics: All the user-defined categories has a topic through which message has published. Kafka uses topics to maintain the message feeds in different categories. To manage the high volume data, each topic divided into the different partition in an ordered sequence. For keeping the uniqueness, every message in a topic assigned a sequential number within the partition.
Producer: It is the process through which message is published to Kafka topics. It is responsible for deciding which message has to go to which partition in the topic.
Consumers: It is the process through which one or more topic can subscribe so that the feeds of the message can be published from the topic.
Broker: There may be one or more servers inside the Kafka Cluster, and each server is known as the broker. All the producers will send the message to the Kafka cluster. The Kafka cluster will analyze the stream of data and send it to the consumers. There is no record of the consumer in the broker. Hence the Kafka broker can be scalable to any extents.
How can Apache Kafka extract the data?
Kafka is widely acceptable by the modern enterprise because it provides the distributed streaming platform for the high velocity of data. For extracting the data from the different source, Kafka comes with the various connectors known as Kafka connectors, and it runs within the framework. For any enterprise which deals with the real-time high-velocity data, it is imperative to examine the data inside the database, for this purpose Change Data Capture (CDC) is provided by the Kafka Connect. It continuously checks the source database and keeps track of all the changes which occurred in data. A streaming engine like spark can be used for the real-time analysis of such data.
Any applications like IoT, POS system, mobile, web, and also social media requires the analysis of real-time data. The platform which analyzes such high volume real-time data should be highly scalable, fault-tolerant, high throughput and should also have geographically distributed data stream processing. Apache Kafka is the best solution which satisfies all the demands for processing high volume data. Perfomatix Dataramp AI platform helps enterprises in building this real-time data pipeline using Apache Kafka with the niche of the technology stack.
Leverage our Dataramp AI platform to build a highly scalable and distributed real-time data pipeline for your business.