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Event stream processing in Kafka
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Introduction to Event Streaming Services & how is it used with Apache Kafka.

What is Event Streaming Services?

Data is at the heart of the new-age business built upon several hundred data-driven applications that account for a system that is continuously generating data. Organizations also use data to garner actionable insights and create an effective data-driven decision-making system.  It is one of the reasons that IDC projects a 26% CAGR growth of data captured and consumed over the period 2019-2024. With so much data being generated and used to run an organization, it is most likely to get buried in highly complex and often inaccessible systems. Event streaming is a process that unwinds this complexity. Event Streaming Services helps businesses to create a robust infrastructure that leverages the power of real-time data.


An event refers to each of the data points in a continuous data generating system. It defines an incident that occurs at a specified time and with a table of information. Stream refers to the continuous flow of data. Datastream often refers to a series of event streams in action.

For example, when you log in to your internet banking account with your ID and password, you create an event. This event sends out continuous data packs to several systems and processes the request to create and send a security code to your registered phone number. The continuous data flow is the stream which is analyzed and used to create a trigger or an alert (generating a security code).

Event Stream processing

How Does Event Streaming Services in Kafka Work?

Event stream processing includes generating, capturing, and analyzing data streams to create and deliver appropriate actions. Some of the most common actions are:

  • Aggregations that involve calculation such as sum, standard deviation, etc.
  • Analytics that help companies understand the current market situations and foresee event-based patterns.
  • Transformations that a user may need, like correcting an address after submitting a form or survey.
  • Enrichment where more value to the data is provided by combining data from different sources.

Apache Kafka is an event streaming platform and has a distributed system of servers and clients. To maintain a semantic connection between the two, event streaming Kafka uses TCP network protocol.

In event streaming using Kafka, the servers form Kafka clusters that span over one or more data centers or cloud regions. Some of the servers also work as storage layers called brokers. The clients allow users to write applications and microservices that read, write, and process streams of events.

The server usually stores data on a timestamp to create an event, while the client lets the user take an action about the same. Each event in Kafka is stored as topics, similar to files and folders in a computer system.

These Kafka topics are distributed over buckets on various brokers. It allows users to read and write applications to and from more than one broker, adding to the scalability of the event processing platform.

Traditional Analytics Versus Event Stream Processing

Event stream processing differs from the traditional ways of data analysis and processing. The conventional analytics approach includes batch processing, where data generated and captured is processed and stored in various databases. These data are then published only when it is required. It does not depend on real-time data.

On the other hand, real-time data is the key driver for event stream processing. The data is captured, generated, stored, and processed continuously. In event streaming using Kafka and other similar platforms, the relevant data is published once processed and triggers an action as soon as possible.


The role of data in the advancing digital world cannot be overlooked. About  80% of enterprise Business Operations leaders say data integration plays a crucial role in running their business operations effectively.

It becomes more prominent with the onset of integrated technologies like the internet of things, which rely heavily on real-time data. Event processing using Kafka is one of the best ways to leverage the benefits of event streams like scalability and high-fault tolerance to create coherent and seamless business processes

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