Apache Kafka is a resilient streaming platform that facilitates the development of real-time streaming applications. It allows the users to instantaneously collect, process, and analyze the data from various sources. Furthermore, Kafka delivers a scalable, fault-tolerant, and high-throughput solution to manage the data streams.
The central aspect of the Kafka platform is the Kafka events. These events are comprised with messages that are transmitted and received by Kafka producers and consumers. This composition explores the familiar Kafka event illustrations and their application in varying scenarios.
What Are Kafka Events?
A Kafka event refers to a message that is produced by a Kafka producer and is consumed by a Kafka consumer. It is a data record which contains a key, value, and metadata. We can accumulate the Kafka Events in a Kafka topic and distribute them across several Kafka brokers.
Kafka events are immutable, which means that you cannot alter them after production. This trait makes the Kafka events ideal to construct the real-time streaming applications where the data precision and consistency are crucial.
Kafka Event Categories
There are three kinds of Kafka events:
Producer Event
A producer event refers to an event that is generated by a Kafka producer when it produces a message. The producer event incorporates the message details including the topic name, key, value, and timestamp.
Consumer Event
A consumer event refers to an event that is generated by a Kafka consumer when it consumes a message. The consumer event includes information regarding the message such as the topic name, key, value, and timestamp.
Broker Event
A broker event refers to an event that is produced by a Kafka broker when it executes an action such as new topic creation or partition reassignment.
Kafka Event Example: ClickStream Data
A Clickstream data is a sequence of user interactions with a website or web application including user clicks, page views, form submissions, and other activities. Clickstream data can be collected and analyzed to improve the user experience, optimize the web page performance, and increase the website traffic.
This step illustration utilizes Kafka to accumulate and process the clickstream data.
Producer
Generating the clickstream data – We establish a Kafka producer that generates the clickstream data by extracting the user interactions from a web server log file and transmitting them to a Kafka topic. The producer employs the log file as the data origin and the Kafka topic as the destination.
Consumer
Processing the clickstream data – To process the clickstream data, we establish a Kafka consumer that retrieves the data from the Kafka topic and performs an action such as updating a database or generating a report. The consumer employs the Kafka topic as the source of data.
Kafka Event Example: IoT Sensor Data
The IoT sensor data is a data that is created by sensors in internet-connected devices such as smart homes, smart cars, and industrial machinery. The IoT sensor data can monitor the device performance, detect anomalies, and optimize operations.
In this illustration, Kafka accumulates and processes the IoT sensor data.
Producer
Generating the sensor data – We establish a Kafka producer that generates the IoT sensor data by retrieving the data from sensors and transmitting it to a Kafka topic. The producer employs the sensors as the data origin and the Kafka topic as the destination.
Consumer
Processing the sensor data to process the IoT sensor data – We establish a Kafka consumer that retrieves the data from the Kafka topic and executes an action such as triggering an alert or sending a command to a device. The consumer employs the Kafka topic as the data source.
Consumer: Processing the Sensor Data
To the process IoT sensor data, we create a Kafka consumer that reads the data from the Kafka topic and performs some action such as triggering an alert or sending a command to a device. The consumer uses the Kafka topic as the source of the data.
Conclusion
We explored the various basics of Kafka events. We also provided some basic scenarios to set up a Kafka events application. To expand your Kafka knowledge, check out our other Kafka series including Kafka consumers, producers, transformations, streams DSL API, and more.
