I'm the dev for an open source event-based database called Sky (http://skydb.io http://skydb.io). Similar data model but it's aimed at a different use case than

I'm the dev for an open source event-based database called Sky (http://skydb.io http://skydb.io). Similar data model but it's aimed at a different use case than Datomic -- it's primarily used for analytics. The event data model is nice for avoiding conflicts but it's an up road battle to try and replace traditional relational databases. I find that storing historical events is primarily useful for either analytics or auditing. It makes more sense to move that off your primary data store if possible. I do find that making databases smarter about the data they hold can really provide some great optimizations. Sky can crunch through about 60MM events per second on a single core because it is built for event data models. I'm sure Datomic can probably get some good optimizations too. I'll be curious to see some benchmarks. Here's a demo app of Sky crunching all the events in the GitHub Archive (starts around 3m:20s): http://skydb.io/blog/github-archive-visualizer.html http://skydb.io/blog/github-archive-visualizer.html I'm working on multi-core distribution right now and I'll be doing multi-node after that. Check out the web site or GitHub (https://github.com/skydb/sky https://github.com/skydb/sky) for more information. It's MIT licensed so feel free to hack away.