[EN] Data Driven/Big Data Series #1: Apache Kafka and Elastic Stack Roles

This is the first in a series exploring the roles of Search and Streaming technologies within the Data Driven (Big Data) environment. In this post, I will do an overview of Kafka, the Elastic Stack (ELK/ECK), and examine practical scenarios where these techs can be applied and integrated. The combination of Apache Kafka and Elasticsearch can forming a backbone for many modern Big Data architectures. They excel at handling high-throughput data streams (Kafka) and providing fast, searchable persistence and analysis (Elasticsearch). Apache Kafka: Realtime Data Backbone Kafka's primary role is acting as a distributed streaming platform. It decouples data producers from consumers, allowing massive volumes of messages to be buffered, ordered, and delivered reliably in realtime. Primary Kafka Use Cases - Data Transport and Processing Activity Tracking and Logging Aggregation: What it does: Applications (websites, IoT devices, mobile apps) produce activity logs (c