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APACHE KAFKA: The Samurai Way

I think you’ll agree with me when I say: It’s REALLY hard to grow your business without the proper infrastructure to maintain your real-time data feeds.

Well, while not as easy as it used to be, you can still have complete control over your entire data library with the help of Apache Kafka through DecisionRules.

Photo by: Tanner Boriack on Unsplash

A growing number of businesses are deploying software solutions that make use of Big Data, Machine Learning, and cloud-native apps. This complicated and tough aspect of software development necessitates the usage of knowledgeable, adaptable individuals as well as scalable and resilient technologies.

Apache Kafka is one such tool. It enables you to take on the Big Data issue when broker systems based on other standards have failed.

If you’ve ever heard of Apache Kafka, you know it’s the “public streaming platform for all things.” Whether you’re outfitting an organization with analytics to deliver insights in real-time or turning the data pipeline into a platform for enterprise app development, Apache Kafka can help.

But what exactly is this tool? And how does it allow enterprises to act quickly on their data? This article will explore these questions and the necessary steps to enable Kafka Solver API to customers of DecisionRules.

What is Apache Kafka?

Apache Kafka is a distributed streaming platform that has become the backbone of many real-time data pipelines in enterprises. Simply speaking, it provides a scalable, persistent, configurable messaging service. The platform was created by LinkedIn in response to their need for an enterprise messaging solution to facilitate large-scale data analysis. Since its launch, the Apache Software Foundation has maintained the project and several companies provide commercial support.

Why use Apache Kafka?

The following reasons are helpful in understanding why enterprises need (and love) Apache Kafka:

It’s easy to use: you simply install the open-source software on commodity hardware. It’s highly scalable: Kafka is built for large data volumes, which means it can handle billions of events per day. It’s resilient: Apache Kafka deals with partitions, health problems, and leadership changes. All these factors increase the robustness of this agile messaging engine. You can also run multiple Kafka brokers to increase its resilience.

The need for streaming data has increased significantly over the last decade. This rise in demand has led to innovations in IDC, Apache Kafka is one of them. With more than 200 customers across industries that use Apache Kafka for their real-time needs, the platform has become a leading-edge tool for enterprises.

What are the business benefits of Apache Kafka through DecisionRules?

At the moment, Kafka is the second most active and visited Apache project, with over 100 000 enterprises worldwide using it.

Through DecisionRules Kafka, like other messaging systems, enables asynchronous data interchange between processes, applications, and servers and can handle trillions of events every day. To reach the needed performance, and real-time big data solution might benefit from its specific system. Kafka is an extremely versatile solution for communication between loosely linked pieces of IT systems, allowing you to consume and transport massive volumes of data in a reliable manner.

React to customers in real-time

Apache Kafka uses Kafka Streams, a client library for building applications and microservices. It is a big data technology that enables you to process data in motion and quickly determine what is working, what is not. Now the typical source of data — transactional data such as orders, inventory, and shopping carts — is enriched with other data sources: recommendations, social media interactions, search queries. Adopting the process of analyzing streaming data in real-time enables a significant reduction of time between when an event is recorded and when the system and data application react to it, so more and more companies can move towards more real-time processing like this. All of these data can feed analytics engines and help you win customers.

Source: Rockset

How to communicate from a client application using Kafka Messages always uses two Kafka topics to communicate with the client application.

  1. Response Topic (read-only)
  • Topic on which your client application listens
  • You will receive the evaluated data on this topic

2. Request Topic (write)

  • Kafka Topic into which you write data for evaluating DecisionRules
  • DecisionRules is listening on this topic

Apache Kafka Implementation Checklist

  • Do your business processes require message ordering?
  • Do you want to be able to configure the retention of the data?
  • Do you want to be able to scale the number of consumers almost linearly? Or maybe you want to have multiple processes reading the same data independently?
  • Does it make sense to have multiple consumers consuming the same data?
  • Do you want to integrate message brokers with other services, e.g. databases? Or maybe you need a fully-fledged stream processing solution?

To find out more, check our documentation or visit If you have any further questions or demo inquiries, contact us at

Thank you for reading 🚀 team

We offer an agile rule engine to let you create, edit and deploy business rules, while all rules run in a secure and scalable cloud. Unlike other rule engines, you can create your first rule in 5 minutes and make 100k decisions in a minute via API.

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