Some of you may not be that familiar with the concept of an ‘enterprise feature store’ in the world of data science, but I am pretty sure you have either interacted with or may have designed a feature store to solve some of the requirements for your project.

Let’s take an example. One of the most common use cases in machine learning world is building recommender systems in which users’ engagement data coming from various user facing frontend applications are sent to an ‘online’ database via streaming engines such as Kafka or Kinesis and processed in real time by stream…

We have watched various spy and sci-fi movies in which the surveillance systems of some secret service matches an unidentified individual by searching or identifying against their database. We have also bought many toy games that uses face identification and gamifies the experience.

In a real world scenario, identity verification systems at office security, visas & immigration, employee attendance use face biometrics. Face Identification is also used to identify shop lifters from their images/video captured through CCTVs. It has also become a very common household thing with mobile devices bringing face Identification as the ubiquitous way to unlock mobile phones.

There are 3 popular types of properties or theorems are associated with various database systems that are characterised by different properties based on these attributes. More often than not, data scientists and ML Engineers tend to overlook the importance of considering these properties while choosing an appropriate database for the required business use case.

Today, some of the most popular database storage systems are as below. They can be further classified into SQL vs No SQL and Row oriented vs Column oriented.


It is important to understand that what makes up for the existence of so many databases that are…

Shrey Shivam

Data Science & Big Data Engineering Lead | AI ML Architect ★ Technology Leader ★ MBA ★ Author

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