The Slow and Steady Evolution of New Age Data Stores

It started far before clouds filled the sky

The disruption in databases started in draught-prone California far before cloud technologies from cloud-filled Seattle became mainstream. we discussed in one of the previous blogs how “necessity is the mother of invention” led to the creation of Google File System and Map-Reduce. When Google published papers to outline both technologies, it inspired Doug Cutting to create Hadoop framework

Clouds in the sky

When public clouds felt the need to create cloud-native databases, they had a lot of inspiration to draw from big data-oriented databases. It started with AWS releasing DynamoDB in 2012 which was a key-value store. It was the first service billed based on throughput, not storage. Since it was a cloud data store, it had auto-scaling as a core feature.


The disruption of the legacy database industry is happening in multiple waves. Big Data enabled data to reside in a distributed architecture and slowly took on lower hanging fruits like OLAP. Cloud-native databases took it to the next stage by offering these datastores As-a-Service. The last frontier left is query engines which have constantly been evolving in the new age datastores (at least for a decade) but still have to go a long way to be a threat to the reign of legacy players (who are not sitting idle either).



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Rishi Yadav

Rishi Yadav


This blog is mostly around my cloud-native & Environments-as-a-Service (EaaS) technology insights. I would throw some crypto wisdom here and there.