Real-Time Streaming at Scale: Integrating Apache Flink, Kafka, Postgres, Elasticsearch, Kibana, and Docker

In the ever-evolving landscape of data processing and analytics, real-time streaming has emerged as a critical component for businesses looking to gain instant insights and react promptly to changing market conditions. Apache Flink, Kafka, Postgres, Elasticsearch, and Kibana form a powerful stack that enables organizations to process, store, search, and visualize streaming data in real time. This article delves into how these technologies can be integrated to create a robust real-time streaming architecture.

Understanding the Components

Before diving into the integration, let’s briefly understand what each component offers:

• Apache Kafka: A distributed streaming platform that excels in publishing and subscribing to streams of records, storing these records, and processing them as they occur.
• Apache Flink: A framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink is known for its high throughput and low-latency streaming capabilities.
• PostgreSQL (Postgres): A powerful, open-source object-relational database system known for its reliability, feature robustness, and performance.