GizmoEdge
The World's First IoT Distributed SQL Engine
GizmoEdge is a revolutionary new technology that allows you to distribute big data SQL workloads to virtually any device.
With GizmoEdge, you can run queries that are distributed and then execute on iPhones, MacBooks, Kubernetes (Linux) pods, Windows computers, or virtually any device with compute, storage, and a network connection.
Divide-and-Conquer
GizmoEdge allows you to process workloads across a heterogeneous network of hundreds, to thousands of IoT computing devices.
Harnessing the power of Apache Arrow, DuckDB, Websockets, and military-grade security - you can query, join, aggregate, and filter terabytes of data in seconds, or less.
You can run GizmoEdge data compute nodes in your cloud's VPC, in Kubernetes, on-premises, and across your employees' or customers' computing devices.
Performance
Using GizmoEdge's patented SQL distribution algorithms, you can run workloads you only dreamed of before.
Tap into the virtually unlimited computing resources of IoT to run OLAP workloads at amazing speed.
Eliminate costly ETL for IoT-generated data
Skip the step of loading unstructured data to the cloud - where it must be processed with expensive distributed technologies.
Use the powerful computing capabilities of devices at the edge to summarize, filter, join, aggregate, and project just the rows and columns you need - in Apache Arrow format.
GizmoEdge SQL Engine - Distribute SQL to IoT computing devices
Experience the power of the GizmoEdge SQL engine
Install the GizmoEdge Worker - from the Apple App Store to have your Apple device join the distributed computing cluster. Then from a browser - go to the GizmoEdge SQL IDE page to run queries that execute on your device!
A Revolutionary Distributed Data Computing Platform
Join the IoT Computing Revolution
GizmoEdge allows you to leverage devices you already have on-premises, in the cloud, and in the palm of your very hand - to process your OLAP workloads.
With GizmoEdge, you can save 80% or more on your cloud computing costs by processing your data workloads at the edge.
