Connecting an Industrial Universal Namespace to AWS IoT SiteWise using HighByte Intelligence Hub
AWS Architecture BlogConnecting an Industrial Universal Namespace to AWS IoT SiteWise using HighByte Intelligence Hubby Michael Brown, Aron Semle, John Harrington, Rajesh Gomatam, and Scott Robertson | on28 JAN 2022| inArchitecture,AWS IoT Core,AWS IoT SiteWise,Customer Solutions,Kinesis Data Firehose|Permalink|ShareThis post was co-authored with Michael Brown, Sr. Manufacturing Specialist Architect, AWS; Dr. Rajesh Gomatam, Sr. Partner Solutions Architect, Industrial Software Specialist, AWS; Scott Robertson, Sr. Partner Solutions Architect, Manufacturing, AWS; John Harrington, Chief Business Officer, HighByte; and Aron Semie, Chief Technology Officer, HighByteMerging industrial and enterprise data across multiple on-premises deployments and industrial verticals can be challenging. This data comes from a complex ecosystem of industrial-focused products, hardware, and networks from various companies and service providers. This drives the creation of data silos and isolated systems that propagate one-to-one integration strategy.To avoid these issues and scale industrial IoT implementations, you must have a universal namespace. This software solution acts as a centralized repository for data, information, and context, where any application or device can consume and publish data needed for a specific action.HighByte Intelligence Hubdoes just that. It is a middleware solution for universal namespace that helps you build scalable, modern industrial data pipelines in AWS. It also allows users to collect data from various sources, add context to the data being collected, and transform it to a format that other systems can understand.Overview of solutionHighByte Intelligence Hub, illustrated in Figure 1, lets you configure a single dedicated abstraction layer (HighByte refers to this as the DataOps layer). This allows you to connect with various vendor schema standards, protocols, and databases. From there, you can model data and apply context for data sustainability.Figure 1. HighByte Intelligence HubHighByte Intelligence Hub uses a unique modeling engine. This allows you to act on real-time data to transform, normalize, and combine it with other sources into an asset model. This model can be deployed and reused as necessary. It represents the real world, and it is available to multiple connections and configurable flow paths simultaneously.For example, Figure 2 shows a model of a hydronic heating system that was created with HighByte Intelligence Hub.Figure 2. Creating a model of a hydronic heating system in HighByte Intelligence HubWith this model, you can define a connection toAWS IoT SiteWiseand publish the model directly. This way, the general model and the instance of the model will immediately be available in AWS.This model can also:Send the temperature and current information from this system to a database for reporting. You can do this without changing anything from the original configuration.Add another connection in HighByte Intelligence Hub forAWS IoT Core(MQTT) and publish the existing model information to the fully managed AWS IoT Core service.Stream the hydronic data into an industrial data lake on AWS, as shown in Figure 3, by adding anAmazon Kinesis Data Firehoseconnection in HighByte Intelligence Hub and attaching the existing flows to it.Figure 3. AWS reference architecture for HighByte Intelligence HubThe next sections will take a closer look at how to configure HighByte Intelligence Hub to work with AWS.PrerequisitesFor this walkthrough, you must have the following prerequisites:AnAWS accountAccess to AWS IoT SitewiseA copy ofHighByte Intelligence HubAccess to industrial data source(s)Note that this post shows the major steps to connect HighByte Intelligence Hub to AWS IoT SiteWise; we will not dive too deeply into all areas of configuration. Please refer to the HighByte Intelligence Hub documentation for specific questions and theAWS service documentationfor a full explanation.Let’s get started!After logging into HighByte Intelligence Hub, create connections to AWS by selecting the “Connections” tab on the menu on the top right corner of the screen.Figure 4 shows the following four connections to AWS resources:AWS IoT Core – US East 1 RegionAWS IoT SiteWise – US East 1 RegionKinesis Data Firehose – US East 1 RegionAWS IoT Greengrass edge device – located on-premisesFigure 4. HighByte Intelligence Hub AWS connectionsFor each connection, HighByte Intelligence Hub uses native AWS security and connectivity patterns. Figure 5 shows the AWS IoT SiteWise connection settings as an example.Figure 5. AWS IoT SiteWise connection settingsFigure 5 shows where to provide an AWS access key and secret key that’s attached to an appropriate AWS Identity and Access Management (IAM) role. This role must have the required AWS IoT SiteWise permissions.Now that you have your connections created, let’s build a model. Select “Modeling” on the menu on the top right corner of the screen. Define all the attribute names and the data types that you want to include in the model. When you are finished, you should have something that looks like Figure 6, which shows the attribute names, attribute types, if it is an array or not, and if it a required attribute for the model.Figure 6. HighByte Intelligence Hub hydronic heating modelNext, create an instance of the asset model. To do this, use the “Actions” dropdown menu on the upper right corner and select “create instance,” because it will preserve your model name.Figure 7. Hydronic model instanceAs shown in Figure 7, you can produce a standardized model and attach normalized labels that map multiple protocols such as OPC, MQTT, and SQL data sources. In our example, our data sources are all MQTT.Now, take your new model instance and assign a flow (Figure 8) that details the source and destination.Figure 8. HighByte Intelligence Hub flowIn this step, as shown in Figure 8, drag and drop the instance of the hydronic model from the right side of the screen to the “Sources” box in the middle of the screen. Then, change the reference type to “Output” from the dropdown menu, select AWS IoT SiteWise as the connection, and drag and drop the AWS IoT SiteWise instance to the “Target” box.From here, you’ll select the following flow settings, as shown on Figure 9:Interval – How often you send dataMode – Always send, On-Change, On-True, or While TruePublish Mode – All Data, Only Changes, Only Changes CompressedEnabled – On or OffOnce you turn the Enabled switch to On and submit, your data will show up in AWS IoT SiteWise.Figure 9. HighByte Intelligence Hub flow settingsNow you’ve configured your MQTT data sources, created a HighByte Intelligence Hub model and instance, and defined a flow to send the data to AWS IoT SiteWise!Next, let’s see how your model and data are represented.When HighByte Intelligence Hub first connects to AWS IoT SiteWise, the hub creates an AWS IoT SiteWise model. The model is configured through the AWS IoT SiteWise API. As shown in Figure 10, the name and type from the HighByte Intelligence Hub model are copied to the measurement name and data type in the AWS IoT SiteWise model. Likewise, the AWS IoT SiteWise model name will inherit from the HighByte Intelligence Hub model name.Figure 10. AWS IoT SiteWise modelAfter the model has been created, HighByte Intelligence Hub will create an AWS IoT SiteWise asset using the model it just created. The asset name will be inherited from the hub instance name. As Figure 11 shows, data will flow from the HighByte Intelligence Hub input data source and through the flow definition, using the attributes defined in the model.Figure 11. AWS IoT SiteWise assetThe final step in this process is to set up a visualization of the data in the AWS IoT SiteWise portal by creating adashboardand addingvisualizationto it. After you do this, the display shown in Figure 12 will update as new data comes into AWS IoT SiteWise.Figure 12. AWS IoT SiteWise portal dashboardConclusionHighByte Intelligence Hub is the first industrial DataOps solution designed specifically for operational technology and information technology teams. It allows you to securely connect, merge, model, and flow industrial data to enterprise systems in AWS Cloud without writing or maintaining code.This post showed you how to integrate HighByte Intelligence Hub with AWS to quickly model and extract data so that multiple teams can simultaneously analyze, interpret, and use the data without constraint and generate rich data models in minutes.Ready to get started?Try outHighByte Intelligence Hubtoday.TAGS:Customer SolutionsMichael BrownMichael Brown is the North American Manufacturing Specialist Architect for AWS. With over 25 years of global experience, Michael has served as an OEM/Machine Builder, System Integrator, Industrial Software Architect, and Enterprise IT Architect. In his current role at AWS, he serves industrial customers in North America through education and shows how cloud adoption can accelerate Digital Transformation and Industry 4.0 initiatives with real-world examples.Aron SemleAron Semle is the Chief Technology Officer of HighByte, focused on guiding the company’s product strategy through research and development and technical evangelism. Aron has had the opportunity to serve in various roles in his 15-year career in industrial technology, including software engineer, product manager, R&D lead, and director of solutions management, building innovative software solutions for the manufacturing operations market.John HarringtonJohn Harrington is the Chief Business Officer of HighByte, focused on defining the company’s business go-to-market strategy. John has spent his 25-year career delivering software to manufacturers and working for manufacturers in operations roles. This experience has given him a unique perspective on how suppliers and end users each play an integral role in implementing new technology solutions.Rajesh GomatamDr. 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.Scott RobertsonScott Robertson is a Sr. Partner Solutions Architect working with Manufacturing Partners in the Industrial Software segment at AWS. He works as a technical leader and trusted advisor for IoT and Edge Solutions. He enjoys helping partners solve problems as they leverage cloud services.
This post was co-authored with Michael Brown, Sr. Manufacturing Specialist Architect, AWS; Dr. Rajesh Gomatam, Sr. Partner Solutions Architect, Industrial Software Specialist, AWS; Scott Robertson, Sr. Partner Solutions Architect, Manufacturing, AWS; John Harrington, Chief Business Officer, HighByte; and Aron Semie, Chief Technology Officer, HighByte Merging industrial and enterprise data across multiple on-premises deployments and industrial […]
Michael Brown
Architecture AWS IoT Core AWS IoT SiteWise Customer Solutions Kinesis Data Firehose