This article is contributed. See the original author and article here.

Our team at EDGE Next has been developing with Azure Digital Twins since the platform’s inception and have made the Azure service a core component of our PropTech platform. From energy optimization to employee wellbeing, we’ve continued to innovate on top of Azure Digital Twins to provide our customers with a seamless smart buildings platform that puts sustainability and employment wellbeing front-and-center. We’ve upgraded our platform to take advantage of the latest Azure Digital Twins capabilities – like more flexible modeling and data integration options – that have equipped us to advance our goals of a reduced environmental footprint and increased workforce satisfaction. We’ve distilled some key learnings from our enhancements and we’d like to share our ideas with any team developing with Azure Digital twins – regardless of industry vertical.



The EDGE Next platform


EDGE Next is a PropTech company that was spun-off from EDGE, a real estate developer that shares our goal of connecting smart buildings that are both good for the environment and for the people in them.


Each EDGE project aims to raise the bar even higher to be the leader in the real estate market from a sustainability and wellbeing perspective. The EDGE Next platform provides a seamless way of ingesting massive amounts of IoT data, analyzing the data and providing actionable insights to serve both EDGE branded and non-EDGE branded (brownfield) buildings. EDGE Next currently has 13 buildings deployed, including Scout24, a tenant in the recently completed EDGE Grand Central Berlin building. We also have several pilots running, including with the Dutch Ministry of Foreign Affairs, IKEA and Panasonic.


At the heart of the EDGE Next platform is Azure Digital Twins, the hyperscale cloud IoT service that provides the “modeling backbone” for our platform. We leverage the Digital Twins Definition Language to define all aspects of our environment, from sensors to digital displays. Azure Digital Twins’ live execution environment is where we turn these model definitions into real buildings’ digital twins, which is brought to life by device telemetry. Finally, the latest data from these buildings is pushed to onsite digital signage and accessible via our platform. Azure Digital Twins played a vital role in enabling key capabilities of the EDGE Next platform, like allowing our implementation teams to onboard customer buildings to the platform without support from the EDGE Next development team (Self-Service Onboarding) and to integrate and manage customer devices in a (Bring Your Own Device). These capabilities are crucial to our platform’s onboarding experience and have brought the time it takes to onboard a customer’s building onto the platform down from weeks to just a couple of minutes.


One of the first buildings to use the platform was EDGE Next’s headquarters, EDGE Olympic in Amsterdam, the very first in a new generation of healthy and intelligent buildings. This hyper-modern structure is used as a living lab to help facilitate real scenarios for the team to materialize incubational ideas into concrete offerings. We leverage a host of sensors throughout the building that measure air quality, light intensity, noise levels and occupancy to create transparency around people counting, footfall traffic and social distancing metrics for COVID-19 scenarios.



EDGE Olympic building (Amsterdam, NL)


Data pathways in the platform


To give you an idea of how our platform works, we walk through the path of the data before and after it reaches Azure Digital Twins. In the diagram below, you can see how Azure Digital twins fits into our platform architecture, with emphasis on the data sources and destinations.




Data sources


The platform enables telemetry ingestion from a collection of IoT Hubs, but also allows messages to flow in from other clouds and APIs (like Azure Maps for outdoor conditions) in inter-cloud and intra-cloud integration scenarios. Given the wide range of different vendor specific APIs that the EDGE Next platform must cater to, our engineering team opted to implement a generic API connector – agnostic to the vendor implementation – and fully rely on a low-code, configuration-driven code base built on top of Azure Functions.




Once the data has been collected using the ingestion mechanisms, it passes through a mapping profile which transforms the raw telemetry messages to known typed messages based on the associated device twins inside the Azure Digital Twins instance. The process of mapping the incoming data is completely driven by low-code JSON Patch configurations, which enables Bring Your Own Device (BYOD) support without additional mapping code logic.




Each message that comes into the ingestion pipeline needs to contain specific fields or it will be rejected. The mapper consults a registry containing all data points in the system and their respective mapping profile configuration to be used for the transformation. The mapper not only transforms the values to the desired internal contract format, but also performs inline unit conversion functions (such as parts per billion to micrograms per cubic meter).


The messages are passed through our Filters stage (detailed below) and finally ingested into Azure Digital Twins.


Data destinations


Once Azure Digital Twins is updated with vendor data and sensor telemetry, the resulting events and twin graph state is accessible via a rich set of APIs that supports and enables multi-channel data delivery. The data is offered in three ways: A web-based portal for visualizations and actionable insights, a digital signage solution for narrowcasting onsite and a set of data APIs to allow our customers to pull their data to integrate with their custom solutions.


EDGE Next portal



The EDGE Next Portal is where most of our customers go to get actionable insights based on retrospective aggregated data, for example highlighting abnormal spikes in energy usage over the weekends where occupancy is at a minimum or suggest more optimized set-points for the HVAC to optimize energy usage. The portal is built on ASP.NET Core 3.1 and driven by reports and dashboards rendered from Power BI embedded. From the Azure Digital Twins instance, measurements are eventually sent to the Azure Data Lake storage, where a batch process is responsible for populating an enriched data model inside Power BI.


On-site digital signage



The digital signage solution provides a way to render data collected in rooms and areas in real-time on virtually any digital display. The solution is built with vanilla HTML and JavaScript and can run on any device that supports web pages. The mechanism that drives the delivery of the data, fed from the events generated from the Azure Digital Twins instance, and then uses Azure SignalR to push all the data in real-time to the displays. On our roadmap, we’re very excited to offer a Digital Signage SDK that will allow customers to build their own narrowcast experiences.


External Data APIs

The data APIs that we expose are the primary method for our customers to interact with their data on their terms. The Streaming API is responsible for pushing real-time telemetry to a wide variety of customer destinations (like Web Hook, Event Hub, Service Bus) and is often used to drive their custom solutions and dashboarding. The Data Extract API is used for ad-hoc data extract over a REST interface where customers can define entities in their environment and a timespan to receive a JSON payload with relevant data. Finally, the Data Share API allows customers to specify destination channels to receive bulk data transfers, powered by Azure Data Share.


Learnings from our journey


We’ve honed in on Azure Digital Twins to forward our goals of sustainability and employee well-being as the service offers our solution incredible flexibility. We’ve noted some key learnings in 3 major areas of the Azure Digital Twins development cycle which we hope the developer community can build off.


Optimizing our ontology for queries


To accomplish our goals of only utilizing necessary resources and building a cost-effective platform, we leveraged service metrics in the Azure Portal to monitor and understand our query and API operations usage. We learned that on average, a typical building running in production on the EDGE Next platform generated around two million telemetry messages per day, which resulted in almost sixty million daily API operations.


After assessing our topology at the time, we focused on reworking our digital twin to optimize for simplicity and reducing data usage. We reduced the amount of “hops” (or twin relationships to traverse) required in our most common queries first; JOINs add complexity to queries, so it’s most economical to keep related data fewer “hops” from each other. We also broke the larger twins into smaller, related twins to allow our queries to return only the data we need.





As you can imagine, the ontology design process is a big part of any digital twin solution, and it can be a time-consuming task to develop and maintain your own modeling foundation. To simplify this process, we referenced the open-source DTDL-based smart buildings ontology, based on the RealEstateCore standard, that Azure has released to help developers build on industry standards and best practices for their solutions. The great thing about using a standard framework is the flexibility to pick-and-choose only the components and concepts that are truly required for your solution. For example, we chose to utilize the room, asset and capability models in our ontology, but we haven’t yet implemented valves or fixtures. As our platform grows and requirements evolve, we’ll continue to cherry-pick critical concepts from the RealEstateCore ontology.


Streamlining our compute


At EDGE Next, we take sustainability very seriously. Solutions in the cloud need to be developed with mindfulness for the environment, and our engineers take great pride in the lightweight event-driven architecture that only lights up when needed and seamlessly scales as demand grows. With that said, it is important to pare down the massive amounts of data the buildings on our platform generate to limit unnecessary compute. Below, the diagram depicts how raw telemetry traffic is deliberately reduced through several different stages of the ingestion pipeline before it reaches the Azure Digital Twins instance. These steps are depicted in the “Data sources” diagram above as the Filters stage.




  1. Filtering – This stage ensures all duplicate messages are rejected and telemetry values within certain deviations are ignored. Due to the nature of the sources transmitting the messages, we do not have control on the throughput or what ends up on the IoT Hub, so we must rely on hashes and timestamps for detecting duplicate values as early in the pipeline as possible. AI-driven deviation filters validate incoming telemetry values against an expected range and drop those that don’t provide impact to current values.

  2. Caching – This stage includes smart caching mechanisms that reduce unnecessary GET calls to the Azure Digital Twins API by storing common existing relationships. This relationship cache is kept up to date by lifecycle events emitted by the Azure Digital Twins instance.

  3. Throttling – The throttling mechanism delays ingress logic to avoid spiky workloads by spreading the load out evenly over time. In scenarios where data ingress is delayed, we can see a backlog of unprocessed events that can cause huge activity spikes throughout the system. The throttling mechanism will kick in as a circuit breaker to ease the load and prevent overutilization of resources.

  4. Grouping – This stage recognizes messages that are targeting the same twin and combining them into minimal resulting API requests to reduce unnecessary updates and load.


Concentrating our query results


The Azure Digital Twins Query Language is used to express an SQL-like query to get live information about the twin graph. When building queries for sustainability and cost-effectiveness, it’s key to minimize the query complexity (quantified by Query Units in the service), which translates to reducing JOINs (query “hops”) and the amount of data the query must sift through. It’s also important to be intentional about how many API operations your request is consuming, meaning you should limit your query responses to only what’s critical for your solution.

A good example of the balance between Query Unit consumption and API Operation response sizes is the retrieval of information across multiple relationships in your twins graph. A scenario that we encountered multiple times during development was the retrieval of a parent with its children. You can write this into a “basic” query that would look like:


SELECT Parent, Child FROM digitaltwins Parent JOIN Child RELATED Parent.hasChild WHERE Parent.$dtId = ‘parent-id’


The “basic” query consumes 26 Query Units and 81 API Operations.


When using the response data, we discovered that retrieving all properties on the parent was unnecessary, which introduced excessive API consumption. In many scenarios it was better to execute two separate queries that projected only the properties that were required. This resulted in substantially fewer API Operations consumed, with a slight increase in Query Unit consumption. Our “optimized” query looks like:


SELECT valueA, valueB, valueC FROM digitaltins WHERE $dtId = ‘parent-id’ AND IS_PRIMITIVE(valueA) AND IS_PRIMITIVE(valueB) AND IS_PRIMITIVE(valueC)


The “optimized” query resulted in 4 Query Units and 1 API Operation.Implementing this operation resulted in an approximately 83% decrease in Query Units and 98% decrease in API Operations. In one of our processes, this change introduced an overall consumption reduction of 45%.


Moreover, you may be able to remove some queries altogether – Azure Digital Twins allows you to listen to lifecycle events and propagate resulting changes throughout your twins graph. If you capture the relevant lifecycle events, which carry information like updated properties and relationships in the payload, you can gather and react to the latest twin data without any queries at all. Our architecture that supports this optimization relies heavily on Azure Digital Twins’ eventing mechanism. Lookup caches in different forms and structures (like parent/child relationships, contextual metadata, etc.) are kept up to date by these events, allowing us to reduce API Operation consumption in the service.


EDGE Next + Azure Digital Twins


Azure Digital Twins gives us a head start in value proposition and time to market than our competitors. We’re able to deliver our customers with a seamless platform that offers quicker building onboarding times. Moreover, it offers us immense value by enabling development accelerators like our low-code ingestion pipeline, and endless integration possibilities with the API surface.


We are expecting to see a huge influx of building onboardings in the near future as our platform is already starting to gain massive commercial traction within the real estate and PropTech industries. Our platform is also constantly evolving with new features, and we look forward to leveraging cutting-edge Azure offerings like Azure Maps, Time Series Insights, IoT Hub and Azure Data Explorer to amplify the value proposition of our IoT Platform.


Learn more


Read about EDGE’s vital role in digital real estate



Brought to you by Dr. Ware, Microsoft Office 365 Silver Partner, Charleston SC.

%d bloggers like this: