What is Kafka primarily used for?

Kafka is primarily a distributed event streaming platform designed to handle real-time data feeds. Its architecture allows it to efficiently manage the stream of records in real-time, which is crucial for organizations looking to process data as it arrives. This capability is particularly valuable for use cases that require rapid data ingestion and immediate processing, such as monitoring applications, log aggregation, and real-time analytics.

Kafka's design supports high throughput, scalability, and fault tolerance, making it an ideal choice for building real-time streaming data pipelines. This includes scenarios where data from various sources needs to be processed concurrently and delivered to multiple consumers seamlessly.

In contrast, building static data warehouses, ingesting data in batch mode, and storing legacy data securely focus on different approaches and requirements for data management that do not align with Kafka's streaming capabilities. These alternatives cater to more traditional data handling methods, which do not effectively leverage Kafka's strengths in real-time data processing.