Big data is one of the hottest sectors in tech right now, but how do you stay on top of the changing technologies? David Pardoe of Hays Recruitment talks about the differences between SQL and NoSQL in data.
How often have you heard that, in this (new) big data world, the newer NoSQL data sources and structures are the key to effective data science? And further, that relational data that can be queried with SQL is old-fashioned and traditional and is no longer fit for purpose?
Why spend time and investment in building ETL processes that shift data from one database to another and enforce a rigid, non-scalable data model?
Why not just dump all of the data in an unstructured or schema-less model? Surely that gives you the most flexibility to really find what you are looking for in the petabytes of data that your organisation collects.
The reality is far more complex; as is usually the case in the field of data science. In fact, the discussion is moot, as it always has been when talking about which technology is best for solving business problems. I heard nearly identical debates when I started my career over 20 years ago and I have found it odd to see such similar themes re-emerging.
The most critical aspect of data science is not the technology or the data structures; it is doing things that can result in better (or quicker) decisions being made. If you focus on that for just a second, you will realise that most, if not all, business decisions are made about things (or to get technical “entities”).