Store and Process pattern

Description:
The Store and Process design pattern focuses on decoupling data storage from data processing to allow for flexible and scalable data stream handling. This pattern is particularly useful in scenarios where data needs to be ingested, stored, and then processed asynchronously. The main elements of this pattern include:

1. Data Store: A storage component that receives and holds incoming data. This component should be capable of handling high-throughput data ingestion and provide reliable storage.
2. Processor: A processing component that retrieves data from the store, processes it, and outputs the results. This processor can work independently of the data ingestion process, allowing for flexible and scalable data handling.
3. Decoupling: By separating the storage and processing responsibilities, the system can scale each component independently, optimizing for performance and resource usage.

References:

• Store and Process Design Pattern - Jenkov Tutorials
• Project Contribution Guidelines

Acceptance Criteria:

1. Implement a data storage component that can ingest and store high-throughput data reliably.
2. Implement a data processing component that retrieves data from the storage component and processes it asynchronously.
3. Ensure the data storage and processing components are decoupled to allow independent scaling and optimization.

The issue names no project files, tests, or entry points, so first read the Project Contribution Guidelines and the linked Store and Process pattern reference. Before implementation, establish the project’s expected boundaries for storage, asynchronous processing, and independent scaling; completion must satisfy all three acceptance criteria.