Litmus Automation Case Studies How an Edge-to-Cloud Data Platform Works

	How an Edge-to-Cloud Data Platform Works Litmus Automation

How an Edge-to-Cloud Data Platform Works

Litmus Automation

Technology Category	Analytics & Modeling - Edge Analytics Platform as a Service (PaaS) - Edge Computing Platforms
Applicable Functions	Discrete Manufacturing Process Manufacturing
Use Cases	Edge Computing & Edge Intelligence
Services	System Integration
Challenge	At Litmus, the biggest challenge the customers face is access to the data they need to fuel machine learning and analytics models. Large scale manufacturers come to Litmus looking for the fastest way to connect to their assets and send data to the cloud. Companies not only need to send data to the cloud to create machine learning models, but they also need to deploy those models back at the edge with a unified edge-to-cloud platform. Read More
Solution	A unified edge-to-cloud data connectivity platform bridges the gap between industrial devices at the edge and big data and machine learning systems in the cloud. The platform is deployed on edge computing devices and is designed to collect and structure data into a common data format. A unified edge-to-cloud platform acts as a data broker, enabling applications and analytics at the edge, and big data and machine learning in the cloud. Litmus is deployed as an edge computing platform next to the industrial assets – collecting and normalizing data at the source. Ready-to-use data is managed at the edge for local analytics and sent to cloud and big data systems for more complex processing. Data models are deployed back at the edge to complete the cycle. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Edge Analytics

Platform as a Service (PaaS) - Edge Computing Platforms

Applicable Functions

Discrete Manufacturing

Process Manufacturing

Use Cases

Edge Computing & Edge Intelligence

Services

System Integration

Challenge

At Litmus, the biggest challenge the customers face is access to the data they need to fuel machine learning and analytics models. Large scale manufacturers come to Litmus looking for the fastest way to connect to their assets and send data to the cloud. Companies not only need to send data to the cloud to create machine learning models, but they also need to deploy those models back at the edge with a unified edge-to-cloud platform.

Solution

A unified edge-to-cloud data connectivity platform bridges the gap between industrial devices at the edge and big data and machine learning systems in the cloud. The platform is deployed on edge computing devices and is designed to collect and structure data into a common data format. A unified edge-to-cloud platform acts as a data broker, enabling applications and analytics at the edge, and big data and machine learning in the cloud.

Litmus is deployed as an edge computing platform next to the industrial assets – collecting and normalizing data at the source. Ready-to-use data is managed at the edge for local analytics and sent to cloud and big data systems for more complex processing. Data models are deployed back at the edge to complete the cycle.

Impact #1	[Data Management - Data Integration] Device Connectivity - Litmus Edge offers rapid data connectivity to all modern and legacy industrial systems with just a few clicks – enabling data collection and structuring data in a ready-to-use format by any edge or enterprise application. Edge Analytics - Get immediate value at the edge with pre-built data visualizations and analytics for common KPIs to realize rapid time-to-value. Run data models created in the cloud back at the edge for closed-loop edge-to-cloud operations. Application Deployment - Host and access public or private applications in a centralized repository with the ability to rapidly and securely deploy and run applications at the edge. Stream normalized and structured data to any pre-built or custom application. Data Integration - Immediately feed valuable and ready-to-use data to any cloud or enterprise application to achieve a complete data picture from OT to IT. Activate machine learning and advanced analytics use cases with bi-directional integration from edge to cloud.
Impact #2	[Data Management - Data Syncing] Edge-to-Cloud Deployments - A vast majority of companies are standardizing on one of these major cloud platforms – Microsoft Azure, Google Cloud, AWS, or Cloudera. Litmus has built great relationships and pre-integrated Litmus Edge with these and other vendors.

Impact #1

[Data Management - Data Integration]

Device Connectivity - Litmus Edge offers rapid data connectivity to all modern and legacy industrial systems with just a few clicks – enabling data collection and structuring data in a ready-to-use format by any edge or enterprise application.

Edge Analytics - Get immediate value at the edge with pre-built data visualizations and analytics for common KPIs to realize rapid time-to-value. Run data models created in the cloud back at the edge for closed-loop edge-to-cloud operations.

Application Deployment - Host and access public or private applications in a centralized repository with the ability to rapidly and securely deploy and run applications at the edge. Stream normalized and structured data to any pre-built or custom application.

Data Integration - Immediately feed valuable and ready-to-use data to any cloud or enterprise application to achieve a complete data picture from OT to IT. Activate machine learning and advanced analytics use cases with bi-directional integration from edge to cloud.

Impact #2

[Data Management - Data Syncing]

Edge-to-Cloud Deployments - A vast majority of companies are standardizing on one of these major cloud platforms – Microsoft Azure, Google Cloud, AWS, or Cloudera. Litmus has built great relationships and pre-integrated Litmus Edge with these and other vendors.

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Overview

How an Edge-to-Cloud Data Platform Works

Operational Impact