Stream summary, count-min sketches, loglog counting, linear counters. Some nifty algorithms for probabilistic estimation of element frequencies and data-set cardinality (via proggit)

good post about applying ML techniques to ops data. 'At a recent meetup about finance, Abhi Mehta encouraged people to capture and save "everything." He was talking about financial data, but the same applies here. We'd need to build Hadoop clusters to monitor our server farms; we'd need Hadoop clusters to monitor our Hadoop clusters. It's a big investment of time and resources. If we could make that investment, what would we find out? I bet that we'd be surprised.' Let's just say that if you like the sound of that, our SDE team in Amazon's Dublin office is hiring ;)

'Our goal is to create the world's fastest extendable, non-transactional time series database for big data (you know, for kids)! Log file indexing is our initial focus. For example append only ASCII files produced by libraries like Log4J, or containing FIX messages or JSON objects. Occursions was built by a small team sick of creating hacks to remotely copy and/or grep through tons of large log files. We use it to index around a terabyte of new log data per day. Occursions asynchronously tails log files and indexes the individual lines in each log file as each line is written to disk so you don't even have to wait for a second after an event happens to search for it. Occursions uses custom disk backed data structures to create and search its indexes so it is very efficient at using CPU, memory and disk.'

Nathan "Storm" Marz on building a dual realtime/batch stack. This lines up with something I've been building in work, so I'm happy ;)

great slides, via HN. recommends a canonical Hadoop long-term store and a quick, realtime, separate datastore for "not yet processed by Hadoop" data