Cluster Setup

Apache Cluster

Many organizations are in need of a robust and scalable messaging system to handle the high-throughput streaming of data across their applications. Existing solutions often struggle to provide the necessary reliability and fault tolerance, leading to data loss or processing delays. This problem hinders real-time data analytics, log aggregation, and other critical processes that rely on efficient data streaming.

To address this challenge, there was a need to implement a Kafka cluster consisting of seven nodes. The cluster was designed to distribute the workload across multiple nodes, ensuring fault tolerance and improved throughput. By implementing this solution, our customer overcome the limitations of existing messaging systems and unlock the potential of real-time data processing and analytics.

Client

A Bank in SouthAfrica

SERVICES

DevOps, Data Solution

Cluster

7 Nodes on AWS ec2

Events Per Hour

Topics

Application Integrations

Spark Processes

0 +

How System Work

The Kafka system is designed to efficiently handle large volumes of streaming data. It consists of a cluster of servers called brokers that work together to store, process, and deliver messages. Messages are organized into topics, which act as categories for different types of data. Each topic is divided into partitions, allowing for parallel processing. Within each partition, there is a leader broker responsible for handling read and write operations, while other brokers serve as replicas for fault tolerance. Producers publish messages to topics, specifying the target topic and partition, while consumers subscribe to topics and read messages at their own pace. Kafka uses ZooKeeper for cluster coordination and metadata management. It provides configurable replication and retention mechanisms for data durability and retention period. Kafka's distributed architecture allows for scalability, fault tolerance, and high throughput, making it suitable for various real-time data processing and messaging scenarios.

Value Add

We created a robust and scalable messaging system to handle high-throughput streaming of data across applications. Existing solutions often struggle to meet the requirements of reliability and fault tolerance, resulting in data loss or processing delays. By implementing a Kafka cluster with seven nodes, the workload is distributed across multiple nodes, ensuring fault tolerance and improved throughput. This addresses the limitations of existing messaging systems, enabling the organization to overcome obstacles and fully leverage real-time data processing and analytics. The Kafka cluster empowers the organization to achieve efficient data streaming, enabling critical processes such as real-time data analytics and log aggregation, ultimately driving better decision-making and insights.