The requirements for a streaming data layer are different from a batch-oriented data lake. Firstly, the time dimension plays a crucial role. Any event data that enters the message bus as a stream must be timestamped. Secondly, performance and latency are more important since it must be certain that data can be processed in due time. Thirdly, the way that analytics and machine learning are applied differs; while the data is being streamed in, the system must analyze it in near-real-time. In general, streaming data software relies more on computing power than storage space; processing speed, low latency, and high throughput are key. Nevertheless, the storage requirements that are in place for a streaming data system are worth considering and are a bit different from "static" batch-driven applications.