Data Virtualization platform that makes your data ready for AI and Advanced Analytics

Molecula is a Data Virtualization Platform that enables instantaneous, secure access to large, fragmented, and geographically dispersed datasets to support the most demanding Machine Learning and analytical workloads at a fraction of the operational cost of typical “Hadoop Zoo” approaches. At great expense, Enterprises typically make numerous copies of their data before they get to make decisions with it. Molecula’s zero-copy data virtualization approach gets you from data to decision without the typical aggregations, federations or other techniques employed by information era vendors. Molecula provides real-time virtualized access to all data in-memory, avoiding data movement and provides a layer of abstraction above the physical implementation of data, irrespective of the source, how it is formatted and where it is physically located.

The basic building block of Molecula is the Virtual Data Source (VDS). Virtualizing underlying data from the datastore enables predictable, high performance queries that are free of compromises, and can unlock your enterprise data while eliminating the need for an expensive patchwork of solutions. Whether your data is sitting in a database, data lake or streaming through a data pipeline, enterprise-grade data virtualization creates a unified high performance access layer for all data consumers.

Data engineers can easily control their ecosystem of VDSs programmatically or in our user interface. While we have reduced the complexity of managing data infrastructure to generating a few lines of code, the visual interface is a quick stop for  The VDSMS allows you to clone, move, manage access to, and apply plugins to VDSs.

Key Capabilities

Capability	Advantage
Zero-Copy Virtualization	Abstract 100% of data across RDBMS, data lakes, data warehouse and event streams
Split-Second Analysis	Virtual Data Sources allows split-second access to all of your data
Native Universal Joiner	Native query across multiple data sets from temporal, static and/or streaming data
AI-Ready	Data insights and preparation for model development and execution
Model Execution Engine	ML runtime engine to allow models to be executed in the same compute layer as the data
Hybrid, Multi-Cloud	Replicate Virtual Data Sources across data centers and clouds with zero data movement
IoT/Edge Analytics	Allow secure mobility through virtualized data at the edge for remote decisioning
Multi-Source Joins	Unified query access with relational like JOINS across multiple data sources and/or VDSs
Simplicity of Scale	Horizontally scalable, with few dependencies, that can easily drop into existing architectures
Enterprise Grade	High performance distributed system focused reliability, availability and serviceability
Plugin Hub	Extensive pre-built and 3rd party integrations for security, governance and transformation
Open Source Core	Built on Pilosa, an open source project with a flourishing community driving innovation