AWS for Industries

Securely Ingest Operational Data from Historian and SCADA platforms like CygNet to Cloud-based Analytics Systems in real-time with AWS Industrial Machine Connectivity (IMC) for Energy using ISV Edge Connectivity solutions like Embassy of Things (EOT) TwinTalk Express

This is part 2 of our blog series on transforming production operations. Part 1 of this series on our anchor customer bpx energy implementation can be found here.

The global energy industry is undergoing a fundamental transition to net-zero energy systems and sustainable practices. Simultaneously, upstream energy companies are high-grading their portfolios and optimizing performance to meet future demands. Industrial IoT (IIoT) has become critical at all levels of the industry value chain in that transition. Energy companies collect operational technology (OT) data from millions of sensors. Much of this valuable data along with the context remains unused in different data historians and Supervisory Control and Data Acquisition (SCADA) systems. Organizations are finding it difficult to make timely decisions because OT data are in silos. Companies need to liberate data from OT systems before they can utilize operational KPIs via cloud-based advanced analytics and machine learning (ML) insights.

The AWS Industrial Machine Connectivity (IMC) for Energy is designed to accelerate digital transformation for oil and gas (O&G) operators by providing a scalable, modularized architecture for data and asset metadata ingestion at scale across thousands of assets and sites. The primary customer need we are addressing is the development and deployment of industrial AI solutions with IIoT data. IMC for Energy makes it convenient for customers to modernize their upstream, midstream, and downstream operations by utilizing data along with OT context hidden inside SCADA, historian, and legacy systems. IMC for Energy can help with asset surveillance including anomaly detection resulting in increased efficiencies and reduced operational costs. IMC for Energy also facilitates the integration between operational technology and information technology, so that customers can build value-added analytics, near real-time predictive capabilities, and operational digital twins.

The AWS IMC for Energy initiative achieves this by bringing together AWS services like AWS IoT SiteWise, Amazon Simple Storage Service (Amazon S3), and Amazon Lookout for Equipment. To simplify these solutions, we bring together AWS Partner products and skills to move data from proprietary applications and data silos to the AWS cloud:

  1. ISV Edge Connectivity Partners whose edge connectivity software products can liberate OT data with asset information and asset analytics from on-premises data stores and bringing it securely to AWS
  2. ISV Energy Solution Partners whose cloud software applications can consume data to generate near real-time insights and analytics
  3. Regional and Global Systems Integrators (SI/GSIs) that can deliver complete solutions to customers and develop custom applications to meet their unique needs.

This blog describes the industrial machine connectivity (IMC) for Energy Quick Start, which is a solution framework that enables customers to easily get real-time operational data from their assets into the AWS Cloud. Let’s now discuss the overall framework of the AWS IMC for Energy solution and specifically the integration of CygNet SCADA platform with AWS IoT SiteWise.

AWS IMC for Energy Framework Overview

The IMC for Energy framework addresses challenges in unifying data and asset context across various data platforms in the cloud for engineers, operators, data scientists, and vendors. IMC for Energy framework enables customers and partners to extract real-time operational and asset data from industrial assets to AWS in a structured process. The framework can convert customers’ existing asset hierarchy definitions (such as plants, wells, individual assets) from historians like OSIsoft PI or SCADA like Weatherford CygNet to the equivalent asset hierarchy in AWS IoT SiteWise. Customers can achieve this through ISV Partner edge applications, such as Inductive Automation’s Ignition Server, Embassy of Thing’s TwinTalk Express, or PTC’s KEPServerEX.

A component of the IMC for Energy framework is the Asset Model Converter (AMC). AMC is an AWS cloud-native, serverless solution that syncs asset model definitions. With asset hierarchies defined in AWS IoT SiteWise, customers’ data from SCADA and historians’ systems can be ingested continuously to the AWS Cloud with all the pertinent metadata accessible for applications. Customers can further integrate with AWS to drive immediate business value, for example, innovate with AI and Industrial IoT to achieve near-real-time process monitoring, automate anomaly detection, and predict equipment failure.

Customers can transform O&G operations with an advanced set of AWS Partner solutions:

  • Connect valuable production data from disparate protocols from PLCs, IIoT sensors, and equipment.
  • Liberate OT data and asset information stored in historians and SCADA systems with embedded historians.
  • Deliver near real-time production monitoring, surveillance with anomaly detection.
  • Provide a low-cost data lake for single source of truth and visibility across different wells and plants.
  • Improve productivity and quality by optimizing operations.
  • Improve equipment availability with predictive maintenance capabilities with ML.
  • Increase decision speed with edge computing capabilities.

The reference architecture below shows the end-to-end implementation of the IMC for Energy solution.

Reference Architecture for Embassy of Things TwinTalk integration with AWS Industrial Machine ConnectivityFigure 1: Reference Architecture for Embassy of Things TwinTalk integration with AWS Industrial Machine Connectivity

Now let’s take a step-by-step view of the reference architecture diagram:

  1. EOT TwinTalk Express/SiteWise Connector runs on AWS certified industrial hardware. TwinTalk Express ingests asset model, hierarchy, and additional metadata from CygNet. TwinTalk Express also extracts real-time industrial tag and sensor data from CygNet SCADA system continuously. It enriches each data record with asset-related metadata and delivers it to AWS IoT SiteWise using the serverless AMC Converter.
  2. AMC ingests and normalizes the enriched data from Twin Talk Express/SiteWise Connector, which then updates AWS IoT SiteWise.
  3. AWS IoT SiteWise publishes MQTT messages to AWS IoT Core each time the asset property receives a new value. Engineers can leverage AWS IoT SiteWise Monitor to view and share operational data from processes, devices, and equipment connected to AWS IoT SiteWise.
  4. AWS IoT Core rule publishes near-real-time data to Amazon Kinesis.
  5. Amazon Kinesis streams data from AWS IoT Core and delivers the data to customer’s Data Lake built on Amazon S3.
  6. Once streaming real-time and historical data from CygNet SCADA is available in Amazon S3 Data Lake, native AWS services and other third-party services can consume the data for reporting, analytics, machine learning, and ad-hoc query workloads.

bpx energy Case Study

bpx energy is the anchor customer of our IMC for Energy solution. AWS partners Embassy of Things, Tensor IoT, and TEKsystems deployed the solution together. It addresses bpx energy’s use cases that include IT infrastructure monitoring and business operations for end users.

The IMC for Energy solution ingests tag values and asset model contextual data from bpx energy’s CygNet SCADA system. bpx uses TwinTalk Express, which is a data management and integration platform. TwinTalk creates secure and scalable access channels between industrial OT and IT systems, thus unlocking value from operational data. TwinTalk enriches sensor/tag values and integrates with AWS services. TwinTalk also moves data to Data Lake on Amazon S3 through Amazon Kinesis. In parallel, TwinTalk syncs asset model in AWS IoT SiteWise, which is a cloud-native data store with asset modeling capability. bpx energy is exploring the use of SiteWise to provide a near-real-time monitoring solution.

Successful digital transformation brings key benefits to bpx energy. Its technology and business teams collaborated closely to increase data granularity. Before implementing the solution, business gets reports from a traditional BI reporting system the next day. Furthermore, the data in the daily reports were aggregated at a high level. With increased data granularity, business teams have much better visibility into their operational state. Now, the business teams are enabled to solve different industry problems with machine learning technologies. The transformation also allowed bpx energy to adopt more cloud-native technologies and de-couple from proprietary vendor technologies. bpx energy estimates it’ll save a few million dollars a year from transforming its technology estate.


To remain competitive during the energy transition, O&G customers need to transform digitally to maximize productivity, asset availability, and lower costs. Energy customers can do this by unlocking data from their OT systems and leverage new tools in AWS like ML to glean insights from their data. This blog dives deep into the AWS IMC for Energy solution, which helps achieve bpx energy’s business objectives. To learn more about the bpx energy case study, check out our previous part 1 blog.

Krishna Doddapaneni

Krishna Doddapaneni

Krishna is an IoT Specialist Partner Solutions Architect with AWS, essentially helping partners and customers build crazy and innovative IoT products and solutions on AWS. Krishna has a Ph.D. in Wireless Sensor Networks and a Postdoc in Robotic Sensor Networks. He is passionate about ‘connected’ solutions, technologies, security and their services.

Mu Li

Mu Li

Li is a Senior Manager of Solutions Architect with AWS Energy. He’s passionate about working with customers to achieve business outcomes using technology. Li has worked with customers to launch the Production Monitoring & Surveillance solution, deploy OpenLink Endur on AWS, and implemented AWS-native IoT and Machine Learning workloads. Outside of work, Li enjoys spending time with his family, following Houston sports teams, and reading on business and technology.

Rajesh Gomatam

Rajesh Gomatam

Dr. Rajesh Gomatam is Principal Partner Solutions Architect working for Industrial Software segment at AWS and also leads the AWS Industrial Data Fabric and Computer Vision for Quality Insights solutions. He enjoys working with industrial partners adhering to AWS best practices and has expertise in industrial data platforms, industrial IoT, time series data, analytics, and edge computing. He works closely as a trusted advisor and industry specialist with the partners across manufacturing and energy verticals.

Thomas Cummins

Thomas Cummins

Thomas is an IoT (Internet of Things) Solutions Architect with a passion for developing new software application architectures for IoT gateway devices and sensor networks. Past experience includes IoT systems design and development as well as medical imaging system hardware and software development. As a PhD student in the Biomedical Engineering Department at the University of Southern California, he developed an ultrasound imaging system designed to improve breast cancer diagnosis.