Our vision: Unlock human potential through the power of data.
What is Molecula?
Molecula is an enterprise feature store that simplifies, accelerates, and controls big data access to power machine-scale analytics and AI. Continuously extracting features, reducing the dimensionality of data at the source, and routing real-time feature changes into a central store enables millisecond queries, computation, and feature re-use across formats and locations without copying or moving raw data. The Molecula feature store provides data engineers, data scientists, and application developers a single access point to graduate from reporting and explaining with human-scale data to predicting and prescribing real-time business outcomes with all data.
What problems does Molecula solve?
At Molecula, we believe there is a tipping point in data access where the effort needed to enable real-time use cases and making big data ready for machine-scale analytics and AI, far exceeds the value created with the data. Enterprises spend a lot of money preparing, aggregating, and making numerous copies of their data for every project before they can make decisions with it. Molecula brings an entirely new paradigm for continuous, real-time data analysis to be used for all your mission-critical applications.
What is a feature store?
A feature store is an overlay to conventional big data systems that automatically extracts features, not data, from each of the underlying data sources or data lakes and stores them into one centralized feature store. The feature store maintains up-to-the-millisecond data updates with little to no upfront data preparation. This is achieved by reducing the dimensionality of the original data, effectively collapsing conventional data models (such as relational or star schemas) into a highly-optimized format that is natively predisposed for machine-scale analytics and AI.
What is Molecula’s mission?
To power the next generation of machine-scale analytics and AI by establishing Molecula’s feature store as the new standard for big data access.