Arnaldo Vera

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The Journey of MongoDB with COVESA in the Connected Vehicle Landscape

There’s a popular saying: “If you want to go fast, go alone; if you want to go far, go together.” I would argue The Connected Vehicle Systems Alliance (COVESA) in partnership with their extensive member network, turns this saying on its head. They have found a way to go fast, together and also go far, together. COVESA is an industry alliance focused on enabling the widespread adoption of connected vehicle systems. This group aims to accelerate the development of these technologies through collaboration and standardization. It's made up of various stakeholders in the automotive and technology sectors, including car manufacturers, suppliers, and tech companies. COVESA’s collaborative approach allows members to accelerate progress. Shared solutions eliminate the need for individual members to reinvent the wheel. This frees up their resources to tackle new challenges, as the community collectively builds, tests, and refines foundational components. As vehicles become more connected, the data they generate explodes in volume, variety, and velocity. Cars are no longer just a mode of transportation, but a platform for advanced technology and data-driven services. This is where MongoDB steps in. MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More MongoDB and COVESA As the database trusted for mission-critical systems by enterprises such as Cathay Pacific , Volvo Connect , or Cox Automotive ; MongoDB has gained expertise in automotive, along with many other industries, building cross-industry knowledge in handling large-scale, diverse data sets. This in turn enables us to contribute significantly to vehicle applications and provide a unique view, especially in the data architecture discussions within COVESA. MongoDB solutions support these kinds of innovations, enabling automotive companies to leverage data for advanced features. One of the main features we provide is Atlas Device SDKs : a low-footprint, embedded database directly living on ECUs. It can synchronize data automatically with the cloud using Atlas Device Sync , our data transfer protocol that compresses the data handles conflict resolution, and only syncs delta changes, making it extremely efficient in terms of operations and maintenance. VSS: The backbone of connected vehicle data An important area of COVESA’s work is the Vehicle Signal Specification (VSS). VSS is a standardized framework used to describe data of a vehicle, such as speed, location, and diagnostic information. This standardization is essential for interoperability between different systems and components within a vehicle, as well as for external communication with other vehicles and infrastructure. VSS has been gaining more and more adoption, and it’s backed by ongoing contributions from BMW, Volvo Cars, Jaguar LR, Robert Bosch and Geotab, among others. MongoDB’s BSON and our Object-oriented Device SDKs uniquely position us to contribute to VSS implementation. The VSS data structured maps 1 to 1 to documents in MongoDB and objects in Atlas Device SDKs , which simplifies development, and speeds up applications by completely skipping any Relational Mapper layer. For every read or write, there is no need to transform the data between relational and VSS. Our insights into data structuring, querying, and management can help optimize the way data is stored and accessed in connected vehicles, making it more efficient and robust. Where MongoDB contributes MongoDB, within COVESA, finds its most meaningful contributions in areas where data complexities and community collaboration intersect. First, we can share insights into managing vast and varied data emerging from connected vehicles generating data on everything from engine performance to driver behavior. Second, we have an important role in supporting the standardization efforts, crucial for ensuring different systems within vehicles can communicate seamlessly. Our inputs can help ensure these standards are robust and practical, considering the real-world scenarios of data usage in vehicles. Some of our contributions include an Over the Air update architectural review presented at Troy COVESA’s AMM in October 2023; sharing insights about the Data Middleware PoC with BMW; and weekly contributions at the Data Expert Group. You can find some of our contributions on COVESA’s Wiki page . In essence, MongoDB's role in COVESA is about providing a unique perspective from the database management point of view, offering our understanding from different industries and use cases to support the developments towards more connected and intelligent vehicles. MongoDB, COVESA, and AWS together at CES2024 MongoDB’s most recent collaboration with COVESA was at the Consumer Electronics Show CES 2024 during which MongoDB’s Connected Vehicle solution was showcased. This solution leverages Atlas Device SDKs, such as the SDK for C++ , which enables local data storage, in-vehicle data synchronization, and also uni and bi-directional data transfer with the cloud. Below is a schematic illustrating the integration of MongoDB within the software-defined vehicle: Schema 1: End to end integration for the connected vehicle At CES 2024, MongoDB also teamed up with AWS for a compelling presentation, " AI-powered Connected Vehicles with MongoDB and AWS " led by Dr. Humza Akhtar and Mohan Yellapantula, Head of Automotive Solutions & Go To Market at AWS. The session delved into the intricacies of building connected vehicle user experiences using MongoDB Atlas. It showcased the combined strengths of MongoDB's expertise and AWS's generative AI tools, emphasizing how Atlas Vector Search unlocks the full lifecycle value of connected vehicle data. During the event, MongoDB also engaged in a conversation with The Six Five, exploring various aspects of mobility, self-driving vehicles (SDVs), and the MongoDB and AWS partnership. This discussion extended to merging IT and OT, GenAI, Atlas Edger Server, and Atlas Device SDK. Going forward At the end of the road, it’s all about enhancing the end-user experience and providing unique value propositions. Defect diagnosis based on the acoustics of the engine, improved crash assistance with mobile and vehicle telemetry data, just-in-time food ordering while on the road, in-vehicle payments, and much, much more. What all these experiences have in common is the combination of interconnected data from different systems. At MongoDB, we are laser-focused on empowering OEMs to create, transform, and disrupt the automotive industry by unleashing the power of software and data. We enable this by: Partnering with alliances such as COVESA to build a strong ecosystem or collaboration. Having one single API for In-vehicle Data Storage, Edge to Cloud Synchronization, Time Series storage, and more, improves the developer experience. Focusing on having a robust, scalable, and secure suite of services trusted by tens of thousands of customers in more than 100 countries. Together with COVESA’s vision for connected vehicles, we’re driving a future where this industry is safer, more efficient, and seamlessly integrated into the digital world. The journey is just beginning. To learn more about MongoDB-connected mobility solutions, visit the MongoDB for Manufacturing & Motion webpage . Achieving fast, reliable and compressed data exchange is one of the pillars of Software Defined Vehicles, learn how MongoDB Atlas and Edge Server can help in this short demo .

April 15, 2024

How MongoDB and Alibaba Cloud are Powering the Era of Autonomous Driving

The emergence of autonomous driving technologies is transforming how automotive manufacturers operate, with data taking center stage in this transformation. Manufacturers are now not only creators of physical products but also stewards of vast amounts of product and customer data. As vehicles transform into connected vehicles, automotive manufacturers are compelled to transform their business models into software-first organizations. The data generated by connected vehicles is used to create better driver assistance systems and paves the way for autonomous driving applications. It has to be noted that the journey toward autonomous vehicles is not just about building reliable vehicles but harnessing the power of connected vehicle data to create a new era of mobility that seamlessly integrates cutting-edge software with vehicle hardware. The ultimate goal of autonomous vehicle makers is to produce cars that are safer than human-driven vehicles. Since 2010, investors have poured over 200 billion dollars into autonomous vehicle technology. Even with this large amount of investment, it is very challenging to create fully autonomous vehicles that can drive safer than humans. Some experts estimate that the technology to achieve level 5 autonomy is about 80% developed but the last 20% will be extremely hard to achieve and will take a lot of time to perfect. Unusual events such as extreme weather, wildlife crossings, and highway construction are still enigmas for many automotive companies to solve. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. The answer to these challenges is not straightforward. AI-based image and object recognition still has a long way to go to deal with uncertainties on the road. However, one thing is certain, automotive manufacturers need to make use of data captured by radar, LiDAR, camera systems, and the whole telemetry system in the vehicle in order to train their AI models better. A modern vehicle is a data powerhouse. It constantly gathers and processes information from onboard sensors and cameras. The Big Data generated as a result presents a formidable challenge, requiring robust storage and analysis capabilities. Additionally, this time series data needs to be analyzed in real-time and decisions have to be made instantaneously in order to guarantee safe navigation. Furthermore, ensuring data privacy and security is also a hurdle to cross since self-driving vehicles need to be shielded from cyber attacks as such an attack can cause life-threatening events. The development of high-definition (HD) maps to help the vehicle ‘see’ what is on the road also poses technical challenges. Such maps are developed using a combination of different data sources such as Global Navigation Satellite Systems (GNSS), radar, IMUs, cameras, and LiDAR. Any error in any one of these systems aggregates and ultimately impacts the accuracy of the navigation. It is required to have a data platform in the middle of the data source (vehicle systems) and the AI platform to accommodate and consolidate this diverse information while keeping this data secure. The data platform should be able to preprocess this data as well as add additional context to it before using it to train or run the AI modules such as object detection, semantic segmentation, and path planning. MongoDB can play a significant role in addressing above mentioned data-related challenges posed by autonomous driving. The document model is an excellent way to accommodate diverse data types such as sensor readings, telematics, maps, and model results. New fields to the documents can be added at run time, enabling the developers to easily add context to the raw telemetry data. MongoDB’s ability to handle large volumes of unstructured data makes it suitable for the constant influx of vehicle-generated information. MongoDB is not only an excellent choice for data storage but also provides comprehensive data pre-processing capabilities through its aggregation framework. Its support for time series window functions allows data scientists to produce calculations over a sorted set of documents. Time series collections also dramatically reduce storage costs. Column compression significantly improves practical compression, reduces the data's overall storage on disk, and improves read performance. MongoDB offers robust security features such as role-based access control, encryption at rest and in transit, comprehensive auditing, field-level redaction and encryption, and down to the level of client-side field-level encryption that can help shield sensitive data from potential cyber threats while ensuring compliance with data protection regulations. For challenges related to effectively storing and querying HD maps, MongoDB’s geospatial features can aid in querying location-based data and also combining the information from maps with telemetry data fulfilling the continuous updates and accuracy requirements for mapping. Furthermore, MongoDB's horizontal scaling or sharding allows for the seamless expansion of storage and processing capabilities as the volume of data grows. This scalability is essential for handling the data streams generated by fleets of self-driving vehicles. During the research and development of autonomous driving projects, scalable infrastructure is required to quickly and steadily collect and process massive data. In such projects, data is generated at the terabyte level every day. To meet these needs, Alibaba Cloud provides a solution that integrates data collection, transmission, storage, and computing. In this solution, the data collected daily by sensors can be simulated and collected using Alibaba Cloud Lightning Cube and sent to the Object Storage Service (OSS) . Context is added to this data using a translator and then this contextualized information can be pushed to MongoDB to train models. MongoDB and Alibaba Cloud recently announced a four-year extension to their strategic global partnership that has seen significant growth since being announced in 2019. Through this partnership, automotive manufacturers can easily set up and use MongoDB-as-a-service-ApsaraDB for MongoDB from Alibaba Cloud’s data centers globally. Figure 1: Data collection and model training data link with MongoDB on Alibaba Cloud. When the vehicle is on the road, the telemetry data is captured through an MQTT gateway, converted into Kafka, and then pushed into MongoDB for data storage and archiving. This data can be used for various applications such as real-time status updates for engine and battery, accident analysis, and regulatory reporting. Figure 2: Mass Production vehicles data link with MongoDB on Alibaba Cloud For a company that is looking to build autonomous driving assistance systems, Alibaba Cloud and ApsaraDB for MongoDB is an excellent technology partner to have. ApsaraDB for MongoDB can handle TBs of diverse sensor data from cars on a daily basis, which doesn't conform to a fixed format. MongoDB provides reliable and highly available data storage for this heterogenous data enabling companies to rapidly expand their system within minutes resulting in time savings when processing and integrating autonomous driving data. By leveraging Alibaba Cloud's ApsaraDB for MongoDB, the R&D team can focus on innovation rather than worrying about data storage and scalability, contributing to faster innovation in the field of autonomous driving. In summary, MongoDB's flexibility, versatility, scalability, real-time capabilities, and strong security framework make it well-suited to address the multifaceted data requirements and challenges that autonomous driving presents. By efficiently managing and analyzing the Big Data generated, MongoDB and Alibaba Cloud are paving the path toward reliable and safe self-driving technology. To learn more about MongoDB’s role in the automotive industry, please visit our manufacturing and automotive webpage .

September 11, 2023

Real-Time Inventory Tracking with Computer Vision & MongoDB Atlas

In today’s rapidly evolving manufacturing landscape, digital twins of factory processes have emerged as a game-changing technology. But why are they so important? Digital twins serve as virtual replicas of physical manufacturing processes, allowing organizations to simulate and analyze their operations in a virtual environment. By incorporating artificial intelligence and machine learning, organizations can interpret and classify objects, leading to cost reductions, faster throughput speeds, and improved quality levels. Real-time data, especially inventory information, plays a crucial role in these virtual factories, providing up-to-the-minute insights for accurate simulations and dynamic adjustments. In the first blog , we covered a 5-step high level plan to create a virtual factory. In this blog, we delve into the technical aspects of implementing a real-time computer vision inventory inference solution as seen in Figure 1 below. Our focus will be on connecting a physical factory with its digital twin using MongoDB Atlas, which facilitates real-time interaction between the physical and digital realms. Let's get started! Figure 1: High Level Overview Part 1: The physical factory sends data to MongoDB Atlas Let’s start with the first task of transmitting data from the physical factory to MongoDB Atlas. Here, we focus on sending captured images of raw material inventory from the factory to MongoDB for storage and further processing as seen in Figure 2. Using the MQTT protocol, we send images as base64 encoded strings. AWS IoT Core serves as our MQTT broker, ensuring secure and reliable image transfer from the factory to MongoDB Atlas. Figure 2: Sending images to MongoDB Atlas via AWS IoT Core For simplicity purposes, in this demo, we directly store the base64 encoded image strings in MongoDB documents. This is because each image received from the physical factory is small enough to fit into one document. However, this is not the only method to work with images (or generally large files) in MongoDB. Within our developer data platform , we have various storage methods, including GridFS for larger files or binary data for smaller ones (less than 16MB). Moreover, object storage services like AWS S3 or Google Cloud Storage, coupled with MongoDB data federation are commonly used in production scenarios. In this real-world scenario, integrating object storage services with MongoDB provides a scalable and cost-efficient architecture. MongoDB is excellent for fast and scalable reads and writes of operational data, but when retrieving images with very low latency is not a priority, the storage of these large files in ‘buckets’ helps reduce costs while getting all the benefits of working with MongoDB Atlas. Robert Bosch GmbH , for instance, uses this architecture for Bosch's IoT Data Storage , which helps service millions of devices worldwide efficiently. Coming back to our use case, to facilitate communication between AWS IoT Core and MongoDB, we employ Rules defined in AWS IoT Core, which helps us send data to an HTTPS endpoint. This endpoint is configured directly in MongoDB Atlas and allows us to receive and process incoming data. If you want to learn more about MongoDB Data APIs, check this blog from our Developer Center colleagues. Part 2: MongoDB Atlas to AWS SageMaker for CV prediction Now it’s time for the inference part! We’ve trained a built-in multi-label classification model provided by Sagemaker, using images like in Figure 3. The images were annotated with using an .lst file format following the schema: So in an image where only the red and white pieces are present, but no blue is present in the warehouse, we would have an annotation such as: Figure 3: Sample image used for the Computer Vision model The model was built using 24 training images and 8 validation images, which was a simplicity-based decision to demonstrate the capabilities of the implementation rather than building a powerful model. Regardless of the extremely low training/validation sample, we managed to achieve a 0.97 validation accuracy. If you want to learn more about how the model was built, check out the Github repo . With a model trained and ready to predict, we created a model endpoint in Sagemaker where we send new images through a POST request so it answers back with the predicted values. We use an Atlas Function to drive this functionality. Every minute, it grabs the latest image stored in MongoDB and sends it to the Sagemaker endpoint. It then waits for the response. When the response is received, we get an array with three decimal values between 0 and 1 representing the likelihood of each piece (blue, red, white) being in the stock. We interpret the numeric values with a simple rule: if the value is above 0.85, we consider the piece being in stock. Finally, the same Atlas function writes the results in a collection (Figure 4) that keeps the current state of the inventory of the physical factory. More details about the function here . Figure 4: Collection storing real time stock status of the factory The beauty comes when we have MongoDB Realm incorporated on the Virtual Factory as seen in Figure 5. It’s automatically and seamlessly synced with MongoDB Atlas through Device Sync. The moment we update the collection with the inventory status of the physical factory in MongoDB Atlas, the virtual factory, with Realm, is automatically updated. The advantage here, besides not needing to include any additional lines of code for the data transfer, is that conflict resolution will be handled out of the box and when connection is lost, the data won’t be lost and rather updated as soon as the connection is re-established. This essentially enables a real-time synchronized digital twin without the hustle of managing data pipelines, configuring your code for edge cases and lose time in non-competitive work. Figure 5: Connecting Atlas and Realm via Device Sync Just as an example of how companies are implementing Realm and Device Sync for mission-critical applications: The airline Cathay Pacific revolutionized how pilots logged critical flight data such as wind speed, elevation, and oil pressure. Historically, it was done manually via pen and paper until they switched to a fully digital, tablet-based app with MongoDB, Realm, and Device Sync. With this, they eliminated all papers from flights and did one of the first zero-paper flights in the world in 2019. Check out the full article here . As you can see, the combination of these technologies is what enables the development of truly connected, highly performant digital twins within just one platform. Part 3: CV results are sent to Digital Twin via Device Sync In the process of sending data to the digital twin through device sync, developers can follow a straightforward procedure. First, we need to navigate to Atlas and access the Realm SDK section. Here, they can choose their preferred programming language and the data models will be automatically pre-built based on the schemas defined in the MongoDB collections. MongoDB Atlas simplifies this task by offering a copy-paste functionality as seen in Figure 6 , eliminating the need to construct data models from scratch. For this specific project, the C# SDK was utilized. However, developers have the flexibility to select from various SDK options, including Kotlin, C++, Flutter, and more, depending on their preferences and project requirements. Once the data models are in place, simply activating device sync completes the setup. This enables seamless bidirectional communication. Developers can now send data to their digital twin effortlessly. Figure 6: Realm C# SDK Object Model example One of the key advantages of using device sync is its built-in conflict resolution capability. Whether facing offline interruptions or any conflicting changes, MongoDB Atlas automatically manages conflict resolution. The "Always on '' feature is particularly crucial for Digital Twins, ensuring constant synchronization between the device and the MongoDB Atlas. This powerful feature saves developers significant time that would otherwise be spent on building custom conflict resolution mechanisms, error-handling functions, and connection-handling methods. With device sync handling conflict resolution out of the box, developers can focus on building and improving their applications. They can be confident in the seamless synchronization of data between the digital twin and MongoDB Atlas. Part 4: Virtual factory sends inventory status to the user For this demonstration, we built the Digital Twin of our physical factory using Unity so that it can be interactive through a VR headset. With this, the user can order a piece on the physical world by interacting with the Virtual Twin, even if the user is thousands of miles away from the real factory. In order to control the physical factory through the headset, it’s crucial that the app informs the user whether or not a piece is present in the stock, and this is where Realm and Device Sync come into play. Figure 7: User is informed of which pieces are not in stock in real time. In Figure 7, the user intended to order a blue piece on the Digital Twin and the app is informing that the piece is not in stock, and therefore not activating the order neither on the physical factory nor its digital twin. What’s happening behind on the backend is that the app is reading the Realm object that stores the stock status of the physical factory and deciding if the piece is orderable or not. Remember that this Realm object is in real-time sync with MongoDB Atlas, which in turn is constantly updating the stock status on the collection in Figure 4 based on Sagemaker inferences. Conclusion In this blog, we presented a four-part process demonstrating the integration of a virtual factory and computer vision with MongoDB Atlas. This solution enables transformative real-time inventory management for manufacturing companies. If you're interested in learning more and getting hands-on experience, feel free to explore our accompanying GitHub repository for further details and practical implementation.

August 1, 2023

Modernize Your Factory Operations: Build a Virtual Factory with MongoDB Atlas in 5 Simple Steps

Virtual factories are revolutionizing the manufacturing landscape. Coined as the "Revolution in factory planning" by BMW Group at NVIDIA, this cutting-edge approach is transforming the way companies like BMW and Hyundai operate, thanks to groundbreaking partnerships with technology companies such as NVIDIA and Unity. At the heart of this revolution lies the concept of virtual factories , computer-based replicas of real-world manufacturing facilities. These virtual factories accurately mimic the characteristics and intricacies of physical factories, making them a powerful tool for manufacturers to optimize their operations. By leveraging AI, they unlock a whole new world of possibilities, revolutionizing the manufacturing landscape, paving the way for improved productivity, cost savings, and innovation. In this blog we will explore the benefits of virtual factories and guide you through the process of building your own virtual factory using MongoDB Atlas. Let’s dive in! Unlocking digital transformation The digitalization of the manufacturing industry has given rise to the development of smart factories. These advanced factories incorporate IoT sensors into their machinery and equipment, allowing workers to gather data-driven insights on their manufacturing processes. However, the evolution does not stop at smart factories automating and optimizing physical production. The emergence of virtual factories introduces simulation capabilities and remote monitoring, leading to the creation of factory digital twins, as depicted in Figure 1. By bridging the concepts of smart and virtual factories, manufacturers can unlock greater levels of efficiency, productivity, flexibility, and innovation. Figure 1:  From smart factory to virtual factory Leveraging virtual factories in manufacturing organizations provides many benefits including: Optimization of production processes and identification of inefficiencies. This can lead to increased efficiency, reduced waste, and improved quality. Aiding quality control by contextualizing sensor data with the manufacturing process. This allows analysis of quality issues and implementation of necessary control measures while dealing with complex production processes. Simulating manufacturing processes and testing new products or ideas without the need for physical prototypes or real-world production facilities. This significantly reduces costs associated with research and development and minimizes the risk of product failure. However, setting up a virtual factory for complex manufacturing is difficult. Challenges include managing system overload, handling vast amounts of data from physical factories, and creating accurate visualizations. The virtual factory must also adapt to changes in the physical factory over time. Given these challenges, having a data platform that can contextualize all the data coming in from the physical factory and then feed that to the virtual factory and vice versa is crucial. And that is where MongoDB Atlas , our developer data platform, comes in, providing synchronization capabilities between physical and virtual worlds, enabling flexible data modeling and providing access to the data via a unified query interface as seen in Figure 2. Figure 2:  MongoDB Atlas as the Data Platform between physical and virtual Factories Now that we’ve discussed the benefits and the challenges of building virtual factories, let’s unpack how simple it is to build a virtual factory with MongoDB Atlas. How to build a virtual factory MongoDB Atlas 1. Define the business requirements The first step of the process is to define the business requirements for the virtual factory. Our team at MongoDB uses a smart factory model from Fischertechnik to demonstrate how easily MongoDB can be integrated to solve the digital transformation challenges of IIoT in manufacturing. This testbed serves as our foundational physical factory and the starting point of this project. Figure 3:  The smart factory testbed We defined our set of business requirements as the following: Implement a virtual run of the physical factory to identify layout and process optimizations. Provide real-time visibility of the physical factory conditions such as inventory for process improvements. This last requirement is critical; while standalone simulation models of factories can be useful, they typically do not take into account the real-time data from the physical factory. By connecting the physical and virtual factories, a digital twin can be created that takes into account the actual performance of the physical factory in real-time. This enables more accurate predictions of the factory's performance, which improves decision-making, process optimization, and enables remote monitoring and control, reducing downtime and improving response times. 2. Create a 3D model Based on the previous business requirements, we created a 3D-model of the factory in a widely used game engine, Unity . This virtual model can be visualized using a computer, tablet or any virtual reality headset. Figure 4:  3D-model of the smart factory in Unity Additionally, we also added four different buttons (red, white, blue, and “stop”) which enables users to submit production orders to the physical factory or stop the process altogether. 3. Connect the physical and virtual factories Once we created the 3D model, we connected the physical and virtual factories via MongoDB Atlas. Let’s start with our virtual factory software application. Regardless of where you deploy it, be it a headset or a tablet, you can use Realm by MongoDB to present data locally inside Unity and then synchronize it with MongoDB Atlas as the central data layer. Allowing us to have embedded databases where there's resource constrainment and MongoDB Atlas as a powerful and scalable cloud backend technology. And lastly, to ensure data synchronization and communication between these two components, we leveraged MongoDB Atlas Device Sync , providing bi-directional synchronization mechanism and network handling. Now that we have our virtual factory set-up, let’s have a look at our physical one. In a real manufacturing environment, many of the shopfloor connectivity systems can connect to MongoDB Atlas and for those who don't natively, it is very straightforward to build a connector. At the shopfloor layer you can have MongoDB set up so that you can analyze and visualize your data locally and set up materialized views. On the cloud layer, you can push data directly to MongoDB Atlas or use our Cluster-to-Cluster Sync functionality. A single IoT device, by itself, does not generate much data. But as the amount of devices grows, so does the volume of machine-generated data and therefore the complexity of the data storage architecture required to support it. The data storage layer is often one of the primary causes of performance problems as an application scales. A well-designed data storage architecture is a crucial component in any IoT platform. In our project, we have integrated AWS IoT Core to subscribe to MQTT messages from the physical factory. Once these messages are received and filtered, they are transmitted to MongoDB Atlas via an HTTP endpoint. The HTTP endpoint then triggers a function which stores the messages in the corresponding collection based on their source (e.g., messages from the camera are stored in the camera collection). With MongoDB Atlas, as your data grows you can archive it using our Atlas Online Archive functionality. Figure 5:  Virtual and physical factories data flow In figure 5, we can see everything we’ve put together so far, on the left hand side we have our virtual factory where users can place an order. The order information is stored inside Realm, synced with MongoDB Atlas using Atlas Device Sync and sent to the physical factory using Atlas Triggers . On the other hand, the physical factory sends out sensor data and event information about the physical movement of items within the factory. MongoDB Atlas provides the full data platform experience for connecting both physical and virtual worlds! 4. Data modeling Now that the connectivity has been established, let's look at modeling this data that is coming in. As you may know, any piece of data that can be represented in JSON can be natively stored in and easily retrieved from MongoDB. The MongoDB drivers take care of converting the data to BSON (binary JSON) and back when querying the database. Furthermore, you can use documents to model data in any way you need, whether it is key value pairs, time series data or event data. On the topic of time series data, MongoDB Time Series allows you to automatically store time series data in a highly optimized and compressed format, reducing customer storage footprint, as well as achieving greater query performance at scale. Figure 5:  Virtual and physical factories sample data It really is as simple as it looks, and the best part is that we are doing all of this inside MongoDB Atlas making a direct impact on developer productivity. 5. Enable computer vision for real-time inventory Once we have the data modeled and connectivity established, our last step is to run event-driven analytics on top of our developer data platform. We used computer vision and AI to analyze the inventory status in the physical factory and then pushed notifications to the virtual one. If the user tries to order a piece in the virtual factory that is not in stock, they will immediately get a notification from the physical factory. All this is made possible using MongoDB Atlas and its connectors to various AI platforms If you want to learn more, stay tuned for part 2 of this blog series where we’ll dive deep into the technical considerations of this last step. Conclusion By investing in a virtual factory, companies can optimize production processes, strengthen quality control, and perform cost-effective testing, ultimately improving efficiency and innovation in manufacturing operations. MongoDB, with its comprehensive features and functionality that cover the entire lifecycle of manufacturing data, is well-positioned to implement virtual factory capabilities for the manufacturing industry. These capabilities allow MongoDB to be in a unique position to fast-track the digital transformation journey of manufacturers. Learn more: MongoDB & IIoT: A 4-Step Data Integration Manufacturing at Scale: MongoDB & IIoT Manufacturing with MongoDB Thank you to Karolina Ruiz Rojelj for her contributions to this post.

June 20, 2023