tachyon

This cartoon actually explains why the Higgs Boson and tachyon hunting folks use p=0.000001 or so, not p=0.05.

You could combine SparkSQL with Tachyon but it is still pretty far from what MapD seems to be accomplishing.

SSDs as RAM + projects like Tachyon are the future of big data processing -- current workflows are still way too slow.

Can you explain what Tachyon does that's different from what Spark already provides?

Last week a project called photon, and now a project called tachyon, I don't get it, what's the idea?

I guess using non-native API would introduce significant overhead and kill all the advantages of using this Tachyon filesystem.

Even with ubiquitous/cheap bandwidth latency is still an issue (until we get Tachyon Display Port) in any real world scenario.

Basically, Tachyon acts as a distributed, reliable, in memory file system.

It's a successor to the Tachyon project mentioned in that post.

Also Tachyon is a full JS engine built in JS.

I remembered it as being a tachyon, so I typed that too.

A friend works at a company here called Tachyon and they have this part as part of the form on their jobs site where you can apply for jobs.

As others have explained Tachyon does the "put dataset in memory" part.

I've never used Tachyon, but based on the wonderful "getting started" experience Spark gives I'd be confident it would be similarly well thought out.

Tachyon, for example, will enable users to share data with a very robust in-memory file system.

Tachyon lets you share data between (say) your Spark jobs and your Hadoop Map/Reduce jobs at RAM speed, even across machines (it understands data-locality, so will attempt to keep data close to where it is being used).