The Slow and Steady Evolution of New Age Data Stores

Databases and Data Management Systems have existed even before computers learned how to count time (epoch time: 1st January 1970). It’s another matter that computers had to relearn how to count time at the dawn of the new millennium.

Databases became mainstream with the introduction of the relational database management system or RDBMS. In a relational database, the database schema was separated from the physical storage. It made schema the first-class citizen and the physical storage became the secret sauce of leading database vendors like Oracle and Microsoft.

In previous blogs, we have talked a lot about how cloud-native evolution has affected multiple aspects of software architecture. Cloud-native has also disrupted how people think about databases and this aspect is not talked about a lot. In this blog, we are going to focus on databases and data management systems disruption.

It started far before clouds filled the sky

Hadoop was a simple framework to store unstructured data in Hadoop Distributed File System (HDFS) and retrieve it using Map-Reduce. What Hadoop itself achieved was much less important than the innovation trend it started commonly knowns as big data. Big Data was famous for three traits: volume, velocity, and variety.

As much as Hadoop was good for unstructured data, data needs to be given some form to analyze it. In addition, high-velocity data needed a way to be stored and retrieved in an efficient manner. This created Hadoop-specific and Hadoop-adjacent databases HBase and Cassandra respectively. Both were columnar stores as opposed to row-based stores in traditional databases.

Clouds in the sky

Summary

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

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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.