Big data is awash with acronyms at the moment, none more widely used than HDFS. Let's cut to the chase... it stands for Hadoop Distributed File System.

This is the system of distributing files that allows Hadoop to work on huge data sets at speed. It spreads blocks of data across different servers, as well as duplicating those blocks of data, and storing them distinctly.

Let's see why with an example.

Sarianne works in the financial markets, and runs a lot of predictive models to make sure her investments are minimum risk.

Utilising HDFS, her queries through Hadoop can run quickly because the data blocks are stored separately -- meaning all the computation can happen in one go, rather than queuing up behind each other.

As an added benefit, if one server fails (as one is bound to, given the amount of servers and disk drives needed to run big data projects) it won't stop Sarianne's models from pulling the data they need, because HDFS duplicated those blocks -- meaning Hadoop can return Sarianne's results in double quick time.

Today we're going to take a look at the V that allows big data to be immediate and reactive: Velocity.

As well as having to master the sheer volume and variety of information within big data, organizations also have to be able to contend with the speed at which all of this data is generated. Real benefit can be gained by pouncing on this data in real-time -- affecting outcomes while they are still forming.

What kind of benefit?

Well, as we've already established, data can take many different forms. How working on this stream of real-time big data will benefit you will depend on your industry. For this example I'll focus on the financial services sector.

Andy is in charge of online security for a big bank, trying to make sure his customers' money is safe. When he can detect fraud after the event, it's fairly useless, but if he can spot it as it happens, it can be priceless. If a malicious computerized attack is started on Andy's bank, it will be generating thousands of events every second -- but Andy has put the right system in place to detect these events by comparing them to the way actual, normal customers behave. And it happens in real time, so alarms are going off to let him know.

As Frank Bria told us in his Big Data Republic article, Big Data Tackles Fraud:

Many fraudsters will access online banking and go directly to the transfer section of a Website without first checking balances and transactions. That clickstream is foreign and unfamiliar to the complex event processing engine and thus gets flagged.