Data Strategy Series: Observations and Facts about Traditional Data Architectures
In this, one of its kind Data Strategy Series, I will be covering many aspects of Enterprise Data that include data pipelines, curation, storage, services, data management and governance of Data Architectures.
In this first article of the series, I will try to describe how data architectures traditionally been designed and optimised for years. In parallel, I will also share a perspective on the changing demands of the advanced analytics platforms and what is shaping the modern data architecture strategies.
Observation 1: Data stores were always for a purpose
Traditionally data architectures had a clear segregation of purpose, the OLTP (Online Transaction Processing), typically known to be used for transactional needs and OLAP (Online Analytic Processing) data stores that typically used for reporting and analytical needs. The following table elaborates the typical differences.
