Relational databases are designed to run on a single server in order to maintain the integrity of the table mappings and avoid the problems of distributed computing.

We’re at a tipping point with data volume. In my last post, I showed the stat from EMC about how the digital universe is expected to grow from 4.4 zettabytes in 2013 to 44 zettabytes in 2020 (remember a zettabyte is 1 trillion gigabytes). That’s hockey stick growth, and we’re just at the start of the curve. Organizations

Relational databases have resulted in accidental complexity that keeps most organizations spinning in circles. Organizations simply cannot keep up with the many shapes, sizes, and types data that are quickly growing in volume and changing.

In the previous post, I discussed why today’s dynamic, constantly changing data is a problem for relational databases. In this post, I am going to discuss a somewhat related, but unique problem that is also not easy for relational databases to handle.

Today, change occurs frequently, and data modeling is a huge challenge because of the time and resources that relational databases require. Unfortunately, when using a relational database, even a simple change like adding or replacing a column in a table might be a million dollar task.

From CIOs to developers, everyone is realizing that relational databases were simply not designed for the challenges with today’s data. That’s why there’s been an explosion of data and new database products