Un-structured, structured and relational data – how big is big?

So now that we are definitely in love with big data…how big does it have to be before we really consider it big?

Well…it depends.

Something is really not that big if it’s just sitting there and you are not hauling it around.

See – when Mr. Neumann put down the seminal architecture for programmed computers - he definitely chose sides! The ‘program‘ was the quarterback – and data played a decidedly subservient role – always at the beck and call to be hauled and mauled as programs saw fit.

Programs ‘fetch’ data – at their leisure, at their chosen time.

Even the operating term sounds more fitting for your Corgi than someone or something more serious!

So we have been writing code that merrily ‘fetches’ data and processes it. Works for most programs. Except when data grows. And grows. And grows…

It grows until it starts to be a real problem to just ‘fetch’ it. And it becomes a real pain to move it around. How you have to think about -

  1. perhaps change roles and send the ‘program’ to data instead of the other way around
  2. how to be smart about moving ONLY the required amount of data

For a PC-XT with a whopping 10MB Hard drive big data was just 10MB. That was the entire drive! The little 8088 CPU running at 4.77MHz on a 8-bit bus could scream at 4.77 MB/sec and could finish scanning the disk (theoretically) in less than 2 seconds.

My desktop is running on an i7-2600 CPU with 4 hyperthreaded cores at 3.4 GHz. This beastie can scan my 2TB hard-drive at the rate of a little under 100GB/sec (again, theoretically) – taking 20 seconds to do the scan.

Let’s take a look at that workhorse of enterprise relational data cruncher – Oracle RAC. A state-of-the-art 4-node RAC system should be able to scan in data at the rate of 4 to 5 GB/sec from the storage – or in excess of 20 GB/second. At that rate – a database can load at the rate of 60+TB/hour. Throw in scheduling overhead, network latency, and error checks and you  are looking at 10 to 20 TB/hour. That’s a very impressive number – giving you head-spinning bragging rights in Oracle OpenWorld data warehouse tutorial sessions…

Now consider a 50TB data Warehouse; not too extreme by Oracle standards but now we are talking about 3 hours to JUST LOAD THE DATABASE.

We’re not just ‘fetching’ data anymore, are we?

50TB is “big data” for Oracle RAC, even more so for single-instance Oracle installations.

Even the ne-plus-ultra of NoSQL – Hadoop is not used in isolation. Typically a Hadoop processing stage is followed by HIVE or other structured databases – even MySQL.

So a big ‘unstructured’ data setup may as easily feed into a big ‘structured’ data analysis stage.  So how big do they typically get before the big data characteristics start to show (difficulty in fetching the entire data, sending program to stationary data, etc., etc.). Here is my take:

Hadoop – The top dogs may sneer at something below a petabyte – but in reality a 100TB Hadoop/NoSQL cluster is getting big. You can’t just deal with it casually and it demands attention in care and feeding.

MySQL cluster – A 100 node cluster in the size range of 100TB is certainly getting there.

Oracle including RAC - 50TB and up…especially DSS (Decision Support System) and Warehouses. Folks up in Amazon and Ebay run some very impressive big data warehouses in Oracle. Then there are installations at “those who shall not be named.”

Hadoop is loved because it’s (supposedly) an open ended framework when it comes to data size. Petabytes of data pouring in as concrete? No problem – just add more nodes as your data grows – no need to change your program – the same java code works. But remember the story of war elephants crossing Alps – just because Mr. H. Barca decided to do it does not mean that you should consider it easy. Tilting at a 1,000 node cluster with Hadoop is a day’s work for Google but not for a typical enterprise CIO.

We’ll explore challenges unique to big structured/relational data and big unstructured data in the coming posts…