There was an interesting, but not uncommon, comment (on

href="http://www.tibbr.com/" target="_blank">tibbr)

recently about a Proof Of Concept in the CEP space that had been

completed in 3 days by the 3-person TIBCO team while the

competitor team were still struggling at the 3 weeks mark. This

despite, per certain analysts' reports, this competitor

being one of the "big guns" in CEP. In the

past some could argue that high

productivity is an opposing requirement to

high performance / scalability; I

would counter that in event processing they are closely related.

Consider:

• 
  
  

  href="http://www.tibco.com/products/business-optimization/complex-event-processing/businessevents/default.jsp"

  target="_blank">TIBCO BusinessEvents remains today

  one of the few CEP technologies to include integrated high

  performance datagrid technology - you develop the

  concept model with the necessary metadata and methods for

  interacting with that data, but have no need to step out into a

  different (database) environment

• Large (Tb

  level) datasets can be accommodated in the

  href="http://www.tibco.com/products/soa/in-memory-computing/activespaces-enterprise-edition/default.jsp"

  target="_blank">DataGrid simply by organising

  several DataGrid service instances (and a fast

  interconnect!)

• Without such data

  interaction, development teams are forced to involve new

  skillsets and problems in integrating (at best) other cache or

  datagrid technologies to (at worst) high-latency

  databases.

Now not all CEP applications require fast data

mechanisms - the commonly stated CEP example being

"trading applications" in Capital Markets,

using event stream processing, where missing a trade opportunity

due to a trade pattern not being in memory is simply missing a

quick profit. However, in most "business event

processing", the context

required for effective decisions involve various dimensions of

customers, past transactions, services etc. Such context will

often be too much data to be entirely in-situ. So it's

no surprise that while most of the CEP competition historically

remains focused on the simple-data requirements of stream

processing, TIBCO's growth and success in this market

area comes from capabilities around processing high

rates of events against large

volumes of contextual data (via

"fast data")

through states, decisions and rules.

So if

fast data is driving the success of CEP, why is there so much

attention being paid to Big Data? The 2 are of course simply

different ends of the same continuum

of event data being collected across

businesses today. Old data is collected in repositories and

analysed for long-term trends (using

href="http://spotfire.tibco.com/products/overview/analytics-products.aspx"

target="_blank">data mining and predictive

analytics) which can then be applied to high-speed

short-term contextual processing of important (business) events.

Exploiting Big Data needs Fast Data

too - that is, assuming you need fast

operational decisions too (or "

href="http://www.tibco.com/company/default.jsp"

target="_blank">2SA")

…