Modernising data architecture for enterprises

This is a 1 min read


Before getting into the topic of focus i.e. how to modernise data architecture for large enterprises (which typically comes with lot of legacy baggage and organisational memory), I would like to set the context by clearing the air around one thing that is related to this subject. 

When people talk about Big data, what are they actually talking about ?

First step in Big data solutions consulting like any consulting space is knowing your audience. (Surprisingly there are no many factions.)

  1. Large Enterprises: Those Banks and Telecom companies who thought “if Google or Facebook is crunching tonnes of data in such low cost, for only the fraction of data we have big data must fit right for all our data driven decision making needs (data warehousing and analytics)”. TL;DR. Folks who are solving a big analytics problem, too large for their traditional EDW (read expensive and typically implemented in a DWA, btw while this is the popular request while there smart enterprises who are solving other genuine Big data problems like tackling the Data deluge.)
  2. Rest of the (sane) world:  Web 2.0 and social media startups and IoT companies who are trying to solve an OLTP scalability problem thinking “NoSQL = = Big data ??”  or “How to serve data requests at scale ?”
  3. Last, but not least: This is the interesting faction, who is basically infatuated by buzzwords like Big data and NoSQL. These guys don’t have a big data problem.

Modernising data architecture for enterprises