Acunu Analytics helps solve the problem of computing simple analytics
over large datasets, incrementally, and in real-time. For many workloads, it answers queries in milliseconds rather than the minutes needed by Hadoop and mapreduce. It is also write-friendly, supports thousands of writes per second, many times more than a relational store can handle. Like Haodop, it scales up to hundreds of millions of events and metrics stored durably to disk across multiple nodes and datacenters. It provides a simple yet powerful set of aggregation functions, including count, sum, average, variance, count distinct, and top-k. Using these primitives, you can do things like:
Storage is handled by Cassandra, so you get all of the benefits of Cassandra’s reliability, scalability, and load balancing. You can submit events and run queries through a simple HTTP REST interface. A Flume plugin is also provided so you can run analytics directly over log files.
Founder, CTO & VP Engineering @acunu I tweet about my company (Acunu) and my RepRap. bio from Twitter
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