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April 20th, 2018 marked the end of our first iteration of DAWNBench, the first deep learning benchmark and competition that measures end-to-end performance: the time/cost required to achieve a state-of-the-art accuracy level for common deep learning tasks, as well as the latency/cost of inference at this state-of-the-art accuracy level.

EMC announced today that they are acquiring Greenplum. Below are the first thoughts that crossed my mind when I heard about this deal.

As 24/7 availability becomes increasingly important for modern applications, database systems are frequently replicated in order to stay up and running in the face of database server failure. It is no longer acceptable for an application to wait for a database to recover from a log on disk --- most mission-critical applications need immediate failover to a replica.

Curt Monash has recently been discussing the differences between machine-generated data and human-generated data, and trying to define these terms on his blog. I think this is a good subject to dive into, since I frequently use the existence of machine-generated data to justify to myself why 90% of my research cycles are spent on scalability problems in database systems. Rather than try to fit a response as a comment on his post, I thought I would devote a post to this subject here.