As the world undergoes a digital transformation, businesses face an ever-increasing amount of real-time data. This data is dispersed across several sources and in various formats. It necessitates ultra-low latency real-time processing, storage, integration, and analytics.
Modern data streaming platforms automatically process information. The platforms also combine data from several sources. Additionally, they help organize, manage, and act on data as it generates in real-time.
Businesses realize that by leveraging a data streaming platform. They can open new use cases and increase their competitive advantage. Organizations can also make their operations more effective and create new business prospects while decreasing operational stress and complexity.
Read more to know how the Apache Kafka streaming platform changes how businesses run.
What Is Event-Streaming?
“Real-time data” and “streaming data” are the latest buzzwords thrown around by almost every data vendor and company. Most businesses want the world to know that they have access to the most up-to-date data and use it to make business decisions. Your business executives and users must understand what event-streaming is.
The process of taking action on data generated or published is real-time stream processing. In technical terms, event streaming is the process of recording data in the form of streams of events from event sources such as databases, sensors, mobile devices, cloud services, and software applications. It also includes:
- To store these event streams and to retrieve later on;
- To change, process, and react to the event streams in real-time;
- To route, the event streams to various destination technologies as needed.
As a result, event streaming ensures that data is continuously flowed and interpreted. Thereby making sure that the relevant information is available at the right time.
Where Can You Use Event-Streaming?
Here are some top use cases of event-streaming applied to many industries and organizations.
1. Finance
Event-streaming is very popular in stock exchanges, banks, and insurance companies, real-time processing of payments. In addition, it can be used at every place where financial transactions happen.
2. Logistics
Logistics companies may manage and monitor their fleets and shipments in real-time. Firms can monitor cars, trucks, fleets, and shipments via event streaming.
3. IoT Analytics
Organizations can continuously acquire and analyze sensor data from IoT devices or other equipment, such as wind farms and industries.
4. Tourism And Retail
In retail, the hotel and travel business, and mobile applications, data is required immediately to act upon customer feedback. It doesn’t help the retailer if they learn about the data a week later. Companies can collect and respond quickly to consumer interactions and requests via event-streaming.
5. Healthcare
Event-streaming is widely used to keep track of patients in hospital care and predict changes in their status to provide prompt treatment in the event of an emergency.
What Is Apache Kafka?
Most businesses deal with an avalanche of data resulting from new apps, new business prospects, IoT, and other sources. The ideal architecture, according to most people, is a simple, well-designed system that helps firms to make the most of their data.
Traditional techniques for handling these difficulties cannot scale to meet the needs of today’s data-driven companies.
Apache Kafka is an event streaming platform built in a modern, distributed architecture to address these issues. Originally conceived as a scalable and fast distributed messaging queue, it has quickly evolved into a full-scale event streaming platform capable of not just publish-and-subscribe but also data storage and processing.
Apache Kafka integrates three crucial capabilities to allow you to execute your event streaming use cases from start to finish:
- To publish (write) and subscribe to (read) event streams, as well as continuous data import/export from other systems.
- To store streams of events for as long as possible in a safe and durable manner.
- To process real-time or past streams of events.
Moreover, it’s all delivered in a distributed, highly scalable, elastic, fault-tolerant, and secure manner. Kafka may be installed on bare metal, virtual machines, and containers, and it can be used both on-premises and in the cloud. Organizations may either maintain their Kafka setups themselves or choose fully managed services from a choice of suppliers.
Working Of Apache Kafka
Apache Kafka is a streaming platform that is free and open-source. It was first built on LinkedIn as a communications queue. It’s an excellent tool for dealing with data streams.
Let us understand working for Kafka in detail.
1. Events
The event is a single byte of information. When a user registers with the system, an event is created. You can think of an event as a data-filled communication. Kafka is a platform for interacting with event streams.
2. Producers
Producers are named because they write events (data) to Kafka. Producers come in a variety of shapes and sizes. Web servers, applications, whole apps, IoT devices, monitoring agents are examples of users. A weather sensor (IoT device) can generate hourly weather events, including data on temperature, humidity, wind speed, and other variables. Hence, anything that produces data qualifies as a producer.
3. Consumers
Consumers are organizations that make use of data (events). In other words, they can accept and use data written by producers. However, it’s also true that the same entities (application components, entire programs, monitoring systems, and so on) can serve as both producers and consumers. It is entirely dependent on the system’s architecture.
4. Topics
Topics are used to organize and store events. A topic is akin to a folder on a filesystem, and the events are the files in that folder. “Payments” is an example of a topic name. Events can be read as many times as necessary, unlike typical messaging systems. Also, they do not erase after consumption.
5. Partitions
Topics are partitioned, splitting into several “buckets” on various Kafka brokers. This distributed data placement is critical for scalability because it allows client applications to receive and write data from and to several brokers simultaneously. Every topic may be duplicated, even across geo-regions or data centres, to make your data fault-tolerant and highly available. This ensures that there are always many brokers with a copy of the data in case something goes wrong, you need to perform broker maintenance, and so on.
Summing-Up
Now is the moment to start adopting data streaming as businesses across industries shift to a data-driven approach. Apache Kafka is critical to the technical infrastructure since it collects and moves data from numerous CDC data streams to one or more endpoints. It serves as a bridge between data-generating applications and data-consuming apps. Hence, it is the right time to understand and act upon the explosion of data.
