Challenges with on-premises data systems
As data grew exponentially, so did the on-premises systems. However, visible cracks started to appear in the legacy way of architecting data and analytics use cases.
The hardware that was used to process, store, and consume data had to be procured up-front, and then installed and configured before it was ready for use. So, there was operational overhead and risks associated with procuring the hardware, provisioning it, installing software, and maintaining the system all the time. Also, to accommodate for future data growth, people had to estimate additional capacity way in advance. The concept of hardware elasticity didn’t exist. The lack of elasticity in hardware meant that there were scalability risks associated with the systems in place, and these risks would surface whenever there was a sudden growth in the volume of data or when there was a market expansion for the business.
Buying all this extra hardware up-front also meant...
