It’s been surprisingly challenging to maintain up-to-date and historical universes of investible ...
It’s been surprisingly challenging to maintain up-to-date and historical universes of investible stock instruments. The relevant data come from many sources, in many forms, changes do not arrive uniformly, and often contradict information about the past.
We’ve recently used the Event Sourcing architectural pattern to re-architect our Investible Stock Universe service, using projections of the event stream, which are task-specific read resources for querying from our various applications. The services we replaced were traditional REST/CRUD microservices, which had both relational and NoSQL data stores.
In this talk, we examine differences in timeliness, availability, and correctness on both the read and write paths. Specifically, we will look at how updates to this sort of data look in both the relational and Event Sourced models, and how the choice of architecture influenced the resulting integrations with our various alpha capture applications, which in turn are used by investment companies to communicate and evaluate proposed trades.
1. How does Event Sourcing Work?
2. What is different from CRUD?
3. CRUD models of Stock Market Data
4. Problems: availability, determinism, auditability, performance, correctness
5. Events modeling Stock Market Data
6. Resulting Read Projections
7. Differences in access patterns (R&W): Solved any Problems?