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.
Exponentially faster, more portable and less expensive than other data virtualization approaches.
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.
|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|