Big data is big headache

IBM claims that we are responsible of for creating 2.5 quintillion bytes of data every day. How much is 1 quintilion?

According to the web,

1 quintillion = 1,000,000,000,000,000,000

After billion, it is trillion, then quadrillion, and then quintillion. That’s what 1 quintillion is, with 18 zeroes!

These data comes from everything from social networking updates, meteorology (weather reports), remote sensing maps (Google Maps, GPS, Geographical Information Systems), photos (Flickr), videos (YouTube), Internet search (Google) and so on. The big data terminology, according to Wikipedia, is data that are too large to be handled and processed by conventional data management tools. This presents a new set of difficulties when it comes to collected these data, storing them and sharing them. Indexing and searching big data would require special technologies to be able to mine and extract valuable information from big data datasets, within an acceptable period of time.

According to Wiki, “Technologies being applied to big data include massively parallel processing (MPP) databases, datamining grids, distributed file systems, distributed databases, cloud computing platforms, the Internet, and scalable storage systems.” That is why EMC has paid big money to acquire GreenPlum and IBM acquired Netezza. Traditional data warehousing players such Teradata, Oracle and Ingres are in the picture as well, setting a collision course between the storage and infrastructure companies and the data warehousing solutions companies.

The 2010 Gartner Magic Quadrant has seen non-traditional players such as IBM/Netezza and EMC/Greenplum, in its leaders quadrant.

And the key word that is already on everyone’s lips is “ANALYTICS“.

The ability to extract valuable information that helps determines what the next future trend is and personalized profiling will be something that may already arrived as companies are clamouring to get more and more out of our personalities so that they can sell you more of their wares.

Meteorological organizations are using big data analytics to find out about weather patterns and climate change. Space exploration becomes more acute and precise from the tons and tons of data collected from space explorations. Big data analytics are also helping pharmaceutical companies develop new biological and pharmaceutical breakthroughs. And the list goes on.

I am a new stranger into big data and I do not proclaim to know a lot. But terms such as scale-out NAS, distributed file systems, grid computing, massively parallel processing are certainly bringing the data storage world into a new frontier, and it is something we as storage professionals have to adapt to. I am eager to learn and know more about big data. It is a big headache but change is inevitable.