Molecula Named in Gartner Market Guide for Analytics Query Accelerators

By: Molecula  |  December 21, 2020

Molecula provides a centralized feature store that accelerates and unifies data access to make 100 percent of data available for use in real-time.

Austin, TX – December 21, 2020 – Molecula, an enterprise feature store built for machine-scale analytics and AI, today announced it was mentioned as a Representative Vendor in Gartner’s Market Guide for Analytics Query Accelerators1. Gartner identified Molecula as an Accelerator offering.

According to Gartner, “Data and analytics leaders continue to struggle with getting value from data lake initiatives that have grown to be unwieldy or that cannot deliver adequate performance as they have evolved. Analytics query accelerators provide a means of making data in semantically flexible data stores more accessible for production and exploratory use. For those data lakes that store some of their data in semi-structured or structured and understood form, the accelerators provide a means of accessing the data in situ.

“Analytics query accelerators provide optimization on top of semantically flexible data stores, typically associated with data lake architectures. Data and analytics leaders should use these offerings to accelerate the time to value of their data lake initiatives as they move toward operational production delivery.”

Molecula offers a new approach for continuous, real-time data analysis and AI through its centralized feature store. Molecula enables access to 100 percent of an organization’s big data, regardless of format or source location, for immediate, millisecond analytics performance.

By automating the process of converting data into features – the data format required for use with AI – Molecula keeps data at its source and extracts only features into a centralized feature store. This process eliminates the need to copy, move, or pre-aggregate data, maintains up-to-the-second updates, and provides a secure data format for sharing. All of an organization’s data can be converted to features and analyzed with full fidelity, made accessible in one centralized store for all analytics and AI projects.

Features create a 60-90 percent reduction in footprint compared to data and enable query performance orders of magnitude faster. This offers organizations considerable savings in data preparation and hardware costs, and a faster path to business outcomes.

“We believe our inclusion as an Analytics Accelerator in this Gartner Market Guide validates our groundbreaking approach to making data more accessible and more computable in real-time,” said Mimi Spier, chief strategy and marketing officer of Molecula. “Computing on features empowers companies to focus on extracting value from data instead of architecting, deploying, securing, and managing data infrastructure for every project.”

Molecula’s solution transformationally leverages big data, machine learning, and AI within the financial services, healthcare, life sciences, public sector, and technology industries so that organizations and companies can securely access and perform computations on any and all data at unprecedented speeds – with low latency and a fraction of the hardware.

1 “Gartner Market Guide for Analytics Accelerators,” by Analysts Adam Ronthal, Merv Adrian, Henry Cook, December 9, 2020

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About Molecula

Molecula is an enterprise feature store that simplifies, accelerates, and controls big data access to power machine-scale analytics and AI. The platform continuously extracts features, reduces the dimensionality of data at the source, and routes real-time feature changes into a central store, enabling 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 on all data. As an overlay to conventional systems, feature stores are easily adaptable, outperform traditional data-oriented approaches, and significantly reduce complexity, costs and risk.

Molecula is the enterprise version of Pilosa, an open-source feature-first storage format with 2,100+ global users. Molecula was founded in 2019 and has offices in Austin, TX and Palo Alto, CA, with a mission to establish its feature store as the new standard for big data access.

Media Contact:

Molecula, Jocelyn Johnson, 917-406-5886, [email protected]