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Nokia Corteca Scales Wi-Fi Connectivity to Millions of Devices With MongoDB Atlas
Nokia’s home Wi-Fi connectivity cloud platform was launched in 2019 as the Nokia WiFi Cloud Controller (NWCC). In 2023, it was renamed and relaunched as the Corteca Home Controller, becoming part of the Corteca software suite that delivers smarter broadband for a better experience. The Corteca Home Controller can be hosted on Amazon Web Services, Google Cloud, or Microsoft Azure, and is the industry’s first platform to support three management services—device management, Wi-Fi management, and application management. Supporting TR-369 (a standardized remote device management protocol) also allows the Home Controller to work in a multi-vendor environment, managing both Nokia broadband devices and third-party broadband devices. By solving connectivity issues before the end-user detects them, and by automatically optimizing Wi-Fi performance, the Home Controller helps deliver excellent customer experiences to millions of users, 24/7. During the five years that Nokia Corteca has been a MongoDB Atlas customer, the Home Controller has successfully scaled from 500,000 devices to over 4.5 million. There are now 75 telecommunications customers of Home Controller spread across all regions of the globe. Having the stability, efficiency, and performance to scale Nokia Corteca's solution is end-to-end, from applications embedded in the device, through the home, and into the cloud. Algorithms assess data extracted from home networks, based on which performance parameters automatically adjust as needed—changing Wi-Fi channels to avoid network interference, for example—thereby ensuring zero downtime. The Home Controller processes real-time data sent from millions of devices, generating massive volumes of data. With a cloud optimization team tasked with deploying the solution across the globe to ever more customers, the Home Controller needed to store and manage its vast dataset and to onboard new telecommunication organizations more easily without incurring any downtime. Prior to Nokia Corteca moving to MongoDB Atlas, its legacy relational database lacked stability and required both admin and application teams to manage operations. A flexible model with time series capabilities That's where MongoDB Atlas came in. Nokia was familiar with the MongoDB Atlas database platform, having already worked with it as part of a previous company acquisition and solution integration. As Nokia's development team had direct experience with the scalability, manageability, and ease of use offered by MongoDB Atlas, they knew it had the potential to address the Home Controller’s technical and business requirements. There was another key element: Nokia wanted to store time-series data—a sequence of data points in which insights are gained by analyzing changes over time. MongoDB Atlas has the unique ability to store operational and time series data in parallel and provides robust querying capabilities on that data. Other advantages include MongoDB's flexible schema, which helps developers store data to match the application's needs and adapt as data changes over time. MongoDB Atlas also provides features such as Performance Advisor that monitors the performance of the database and makes intelligent recommendations to optimize and improve the performance and resource consumption Fast real time data browsing and scalability made easy Previously, scaling the database had been time-consuming and manual. With MongoDB Atlas, the team can easily scale up as demand increases with very little effort and no downtime. This also means it is much more straightforward to add new clients, such as large telecommunications companies. Having started with 100GB of data, the team now has more than 1.3 terabytes, and can increase the disc space in a fraction of a second, positioning the team to be able to scale with the business. As the Home Controller grows and onboards more telcos, the team anticipates a strengthening relationship with MongoDB. “We have a very good relationship with the MongoDB team,” said Jaisankar Gunasekaran, Head of Cloud Hosting and Operations at Nokia. “One of the main advantages is their local presence—they’re accessible, they’re friendly, and they’re experts. It makes our lives easier and lets us concentrate on our products and solutions.” To learn more about how MongoDB can help drive innovation and capture customer imaginations, check out our MongoDB for Telecommunications page.
Building Gen AI-Powered Predictive Maintenance with MongoDB
In today’s fast-evolving industrial landscape, digital transformation has become a necessity. From manufacturing plants to connected vehicles, the push towards predictive maintenance excellence is driving organizations to embrace smarter, more efficient ways of managing operations. One of the most compelling advancements in this domain is predictive maintenance powered by generative AI , a cutting-edge approach that will revolutionize how industries maintain and optimize their equipment. For manufacturers seeking maintenance excellence, a unified data store and a developer data platform are key enablers. These tools provide the foundation for integrating AI applications that can analyze sensor data, predict failures, and optimize maintenance schedules. MongoDB Atlas is the only multi-cloud developer data platform available that is designed to streamline and speed up developers' data handling. With MongoDB Atlas, developers can enhance end-to-end value chain optimization through AI/ML, advanced analytics, and real-time data processing, supporting cutting-edge mobile, edge, and IoT applications. In this post, we’ll explore the basics of predictive maintenance and how MongoDB can be used for maintenance excellence. Understanding the need for predictive maintenance Predictive maintenance is about anticipating and addressing equipment failures before they occur, ensuring minimal disruption to operations. Traditional maintenance strategies, like time-based or usage-based maintenance, are less effective than predictive maintenance because they don’t account for the varying conditions and complexities of machinery. Unanticipated equipment breakdown can result in line stoppage and substantial throughput losses, potentially leading to millions of dollars in revenue loss. Since the pandemic, many organizations have begun significant digital transformations to improve efficiency and resilience. However, a concerning gap exists between tech adoption and return on investment. While 89% of organizations have begun digital and AI transformations, only 31% have seen the expected revenue lift, and only 25% have realized the expected cost savings. These numbers highlight the importance of implementing new technologies strategically. Manufacturers need to carefully consider how AI can address their specific challenges and then integrate them into existing processes effectively. Predictive maintenance boosts efficiency and saves money Predictive maintenance uses data analysis to identify problems in machines before they fail. This allows organizations to schedule maintenance at the optimal time, maximizing machine reliability and efficiency. Indeed, according to Deloitte , predictive maintenance can lead to a variety of benefits, including: 3-5% reduction in new equipment costs 5-20% increase in labor productivity 15-20% reduction in facility downtime 10-30% reduction in inventory levels 5-20% reduction in carrying costs Since the concept was introduced, predictive maintenance has constantly evolved. We've moved beyond basic threshold-based monitoring to advanced techniques like machine learning (ML) models. These models can not only predict failures but also diagnose the root cause, allowing for targeted repairs. The latest trend in predictive maintenance is automated strategy creation. This involves using AI to not only predict equipment breakdowns but also to generate repair plans, ensuring the right fixes are made at the right time. Generative AI in predictive maintenance To better understand how gen AI can be used to build robust predictive maintenance solutions, let's dig into the characteristics of organizations that have successfully implemented AI. They exhibit common traits across five key areas: Identifying high-impact value drivers and AI use cases: Efforts should be concentrated on domains where artificial intelligence yields maximal utility rather than employing it arbitrarily. Aligning AI strategy with data strategy: Organizations must establish a strong data foundation with a data strategy that directly supports their AI goals. Continuous data enrichment and accessibility: High-quality data, readily available and usable across the organization, is essential for the success of AI initiatives. Empowering talent and fostering development: By equipping their workforce with training and resources, organizations can empower them to leverage AI effectively. Enabling scalable AI adoption: Building a strong and scalable infrastructure is key to unlocking the full potential of AI by enabling its smooth and ongoing integration across the organization. Implementing predictive maintenance using MongoDB Atlas When combined with a robust data management platform like MongoDB Atlas, gen AI can predict failures with remarkable accuracy and suggest optimal maintenance schedules. MongoDB Atlas is the only multi-cloud developer data platform designed to accelerate and simplify how developers work with data. Developers can power end-to-end value chain optimization with AI/ML, advanced analytics, and real-time data processing for innovative mobile, edge, and IoT applications. MongoDB Atlas offers a suite of features perfectly suited for building a predictive maintenance system, as shown in Figure 1 below. Its ability to handle both structured and unstructured data allows for comprehensive condition monitoring and anomaly detection. Here’s how you can build a generative AI-powered predictive maintenance software using MongoDB Atlas: Machine prioritization: This stage prioritizes machines for the maintenance excellence program using a retrieval-augmented generation (RAG) system that takes in structured and unstructured data related to maintenance costs and past failures. Generative AI revolutionizes this process by reducing manual analysis time and minimizing investment risks. At the end of this stage, the organization knows exactly which equipment or assets are well-suited for sensorization. Utilizing MongoDB Atlas, which stores both structured and unstructured data, allows for semantic searches that provide accurate context to AI models. This results in precise machine prioritization and criticality analysis. Failure prediction: MongoDB Atlas provides the necessary tools to implement failure prediction, offering a unified view of operational data, real-time processing, integrated monitoring, and seamless machine learning integration. Sensors on machines, like milling machines, collect data (e.g., air temperature and torque) and process it through Atlas Stream Processing , allowing continuous, real-time data handling. This data is then analyzed by trained models in MongoDB, with results visualized using Atlas Charts and alerts pushed via Atlas Device Sync to mobile devices, establishing an end-to-end failure prediction system. Repair plan generation: To implement a comprehensive repair strategy, generating a detailed maintenance work order is crucial. This involves integrating structured data, such as repair instructions and spare parts, with unstructured data from machine manuals. MongoDB Atlas serves as the operational data layer, seamlessly combining these data types. By leveraging Atlas Vector Search and aggregation pipelines , the system extracts and vectorizes information from manuals and past work orders. This data feeds into a large language model (LLM), which generates the work order template, including inventory and resource details, resulting in an accurate and efficient repair plan. Maintenance guidance generation: Generative AI is used to integrate service notes and additional information with the repair plan, providing enhanced guidance for technicians. For example, if service notes in another language are found in the maintenance management system, we extract and translate the text to suit our application. This information is then combined with the repair plan using a large language model. The updated plan is pushed to the technician’s mobile app via Atlas Device Sync. The system generates step-by-step instructions by analyzing work orders and machine manuals, ensuring comprehensive guidance without manually sifting through extensive documents. Figure 1: Achieving end-to-end predictive maintenance with MongoDB Atlas Developer Data Platform In the quest for operational excellence, predictive maintenance powered by generative AI and MongoDB Atlas stands out as a game-changer. This innovative approach not only enhances the reliability and efficiency of industrial operations but also sets the stage for a future where AI-driven insights and actions become the norm. By leveraging the advanced capabilities of MongoDB Atlas, manufacturers can unlock new levels of performance and productivity, heralding a new era of smart manufacturing and connected systems. If you would like to learn more about generative AI-powered predictive maintenance, visit the following resources: [Video] How to Build a Generative AI-Powered Predictive Maintenance Software [Whitepaper] Generative AI in Predictive Maintenance Applications [Whitepaper] Critical AI Use Cases in Manufacturing and Motion: Realizing AI-powered innovation with MongoDB Atlas
Unlock PDF Search in Insurance with MongoDB & SuperDuperDB
As industries go, the insurance industry is particularly document-driven. Insurance professionals, including claim adjusters and underwriters, spend considerable time handling documentation with a significant portion of their workday consumed by paperwork and administrative tasks. This makes solutions that speed up the process of reviewing documents all the more important. Retrieval-augmented generation (RAG) applications are a game-changer for insurance companies, enabling them to harness the power of unstructured data while promoting accessibility and flexibility. This is especially true for PDFs, which despite their prevalence are difficult to search, leading claim adjusters and underwriters to spend hours reviewing contracts, claims, and guidelines in this common format. By combining MongoDB and SuperDuperDB you can build a RAG-powered system for PDF search, thus bringing efficiency and accuracy to this cumbersome task. With a PDF search application, users can simply type a question in natural language and the app will sift through company data, provide an answer, summarize the content of the documents, and indicate the source of the information, including the page and paragraph where it was found. In this blog, we will dive into the architecture of how this PDF search application can be created and what it looks like in practice. Why should insurance companies care about PDF Search? Insurance firms rely heavily on data processing. To make investment decisions or handle claims, they leverage vast amounts of data, mostly unstructured. As previously mentioned, underwriters and claim adjusters need to comb through numerous pages of guidelines, contracts, and reports, typically in PDF format. Manually finding and reviewing every piece of information is time-consuming and can easily lead to expensive mistakes, such as incorrect risk estimations. Quickly finding and accessing relevant content is key. Combining Atlas Vector Search and LLMs to build RAG apps can directly impact the bottom line of an insurance company. Behind the scenes: System architecture and flow As mentioned, MongoDB and SuperDuperDB underpin our information retrieval system. Let’s break down the process of building it: The user adds the PDFs that need to be searched. A script scans them, creates the chunks, and vectorizes them (see Figure 1). The chunking step is carried out using a sliding window methodology, which ensures that potentially important transitional data between chunks is not lost, helping to preserve continuity of context. Vectors and chunk metadata are stored in MongoDB, and an Atlas Vector Search index is created (see Figure 3). The PDFs are now ready to be queried. The user selects a customer, asks a question, and the system returns an answer, where it was found and highlights the section with a red frame (see Figure 3). Figure 1: PDF chunking, embedding creation, and storage orchestrated with SuperDuperDB Each customer has a guidelines PDF associated with their account based on their residency. When the user selects a customer and asks a question, the system runs a Vector Search query on that particular document, seamlessly filtering out the non-relevant ones. This is made possible by the pre-filtering field included in the search query. Atlas Vector Search also takes advantage of MongoDB’s new Search Nodes dedicated architecture, enabling better optimization for the right level of resourcing for specific workload needs. Search Nodes provide dedicated infrastructure for Atlas Search and Vector Search workloads, allowing you to optimize your compute resources and fully scale your search needs independent of the database. Search Nodes provide better performance at scale, delivering workload isolation, higher availability, and the ability to optimize resource usage. Figure 2: PDF querying flow, orchestrated with SuperDuperDB SuperDuperDB SuperDuperDB is an open-source Python framework for integrating AI models and workflows directly with and across major databases for more flexible and scalable custom enterprise AI solutions. It enables developers to build, deploy, and manage AI on their existing data infrastructure and data, while using their preferred tools, eliminating data migration and duplication. With SuperDuperDB, developers can: Bring AI to their databases, eliminate data pipelines and moving data, and minimize engineering efforts, time to production, and computation resources. Implement AI workflows with any open and closed source AI models and APIs, on any type of data, with any AI and Python framework, package, class or function. Safeguard their data by switching from APIs to hosting and fine-tuning your own models, on your own existing infrastructure, whether on-premises or in the cloud. Easily switch between embedding models and LLMs, to other API providers as well as hosting your own models, on HuggingFace, or elsewhere just by changing a small configuration. Build next-generation AI apps on your existing database SuperDuperDB provides an array of sample use cases and notebooks that developers can use to get started, including vector search with MongoDB, embedding generation, multimodal search, retrieval-augmented generation (RAG), transfer learning, and many more. The demo showcased in this post is adapted from an app previously developed by SuperDuperDB. Let's put it into practice To show you how this could work in practice, let’s look at, an underwriter handling a specific case. The underwriter is seeking to identify the risk control measures as shown in Figure 3 below but needs to look through documentation. Analyzing the guidelines PDF associated with a specific customer helps determine the loss in the event of an accident or the new premium in the case of a policy renewal. The app assists by answering questions and displaying relevant sections of the document. Figure 3: Screenshot of the UI of the application, showing the question asked, the LLM’s answer, and the reference document where the information is found By integrating MongoDB and SuperDuperDB, you can create a RAG-powered system for efficient and accurate PDF search. This application allows users to type questions in natural language, enabling the app to search through company data, provide answers, summarize document content, and pinpoint the exact source of the information, including the specific page and paragraph. If you would like to learn more about Vector Search powered apps and SuperDuperDB, visit the following resources: PDF Search in Insurance Github repository Search PDFs at Scale with MongoDB and Nomic SuperDuperDB Github, includes notebooks and examples
Atlas Vector Search Once Again Voted Most Loved Vector Database
The 2024 Retool State of AI report has just been released, and for the second year in a row, MongoDB Atlas Vector Search was named the most loved vector database. Atlas Vector Search received the highest net promoter score (NPS), a measure of how likely a user is to recommend a solution to their peers. The Retool State of AI report is a global annual survey of developers, tech leaders, and IT decision-makers that provides insights into the current and future state of AI, including vector databases, retrieval-augmented generation (RAG) , AI adoption, and challenges innovating with AI. MongoDB Atlas Vector Search commanded the highest NPS in Retool’s inaugural 2023 report, and it was the second most widely used vector database within just five months of its release. This year, Atlas Vector Search came in a virtual tie for the most popular vector database, with 21.1% of the vote, just a hair behind pgvector (PostgreSQL), which received 21.3%. The survey also points to the increasing adoption of RAG as the preferred approach for generating more accurate answers with up-to-date and relevant context that large language models ( LLMs ) aren't trained on. Although LLMs are trained on huge corpuses of data, not all of that data is up to date, nor does it reflect proprietary data. And in those areas where blindspots exist, LLMs are notorious for confidently providing inaccurate "hallucinations." Fine-tuning is one way to customize the data that LLMs are trained on, and 29.3% of Retool survey respondents leverage this approach. But among enterprises with more than 5,000 employees, one-third now leverage RAG for accessing time-sensitive data (such as stock market prices) and internal business intelligence, like customer and transaction histories. This is where MongoDB Atlas Vector Search truly shines. Customers can easily utilize their stored data in MongoDB to augment and dramatically improve the performance of their generative AI applications, during both the training and evaluation phases. In the course of one year, vector database utilization among Retool survey respondents rose dramatically, from 20% in 2023 to an eye-popping 63.6% in 2024. Respondents reported that their primary evaluation criteria for choosing a vector database were performance benchmarks (40%), community feedback (39.3%), and proof-of-concept experiments (38%). One of the pain points the report clearly highlights is difficulty with the AI tech stack . More than 50% indicated they were either somewhat satisfied, not very satisfied, or not at all satisfied with their AI stack. Respondents also reported difficulty getting internal buy-in, which is often complicated by procurement efforts when a new solution needs to be onboarded. One way to reduce much of this friction is through an integrated suite of solutions that streamlines the tech stack and eliminates the need to onboard multiple unknown vendors. Vector search is a native feature of MongoDB's developer data platform, Atlas, so there's no need to bolt on a standalone solution. If you're already using MongoDB Atlas , creating AI-powered experiences involves little more than adding vector data into your existing data collections in Atlas. If you're a developer and want to start using Atlas Vector Search to start building generative AI-powered apps, we have several helpful resources: Learn how to build an AI research assistant agent that uses MongoDB as the memory provider, Fireworks AI for function calling, and LangChain for integrating and managing conversational components. Get an introduction to LangChain and MongoDB Vector Search and learn to create your own chatbot that can read lengthy documents and provide insightful answers to complex queries. Watch Sachin Smotra of Dataworkz as he delves into the intricacies of scaling RAG (retrieval-augmented generation) applications. Read our tutorial that shows you how to combine Google Gemini's advanced natural language processing with MongoDB, facilitated by Vertex AI Extensions to enhance the accessibility and usability of your database. Browse our Resources Hub for articles, analyst reports, case studies, white papers, and more. Want to find out more about recent AI trends and adoption? Read the full 2024 Retool State of AI report .
Exact Nearest Neighbor Vector Search for Precise Retrieval
With its ability to efficiently handle high-dimensional, unstructured data, vector search delivers relevant results even when users don’t know what they’re looking for and uses machine learning models to find similar results across any data type. Rapidly emerging as a key technology for modern applications, vector search empowers developers to build next-generation search and generative AI applications faster and easier. MongoDB Atlas Vector Search goes beyond the approximate nearest neighbor (ANN) methods with the introduction of exact nearest neighbor (ENN) vector search . This innovative capability guarantees retrieval of the absolute closest vectors to your query, eliminating the accuracy limitations inherent in ANN. In sum, ENN vector search can help you unleash a new level of precision for your search and generative AI applications, improving benchmarking and moving to production faster. When exact nearest neighbor (ENN) vector search benefits developers While ANN shines in searching across large datasets, ENN vector search offers advantages in specific scenarios: Small-scale vector data: For datasets under 10,000 vectors, the linear time complexity of ENN vector search makes it a viable option, especially considering the added development complexity of tuning ANN parameters. Recall benchmarking of ANN queries: ANN queries are fast, particularly as the scale of your indexed vectors increases, but it may not be easy to know whether the retrieved documents by vector relevance correspond to the guaranteed closest vectors in your index. Using ENN can help provide that exact result set for comparison with your approximate result set, using jaccard similarity or other rank-aware recall metrics. This will allow you to have much greater confidence that your ANN queries are accurate since you can build quantitative benchmarks as your data evolves. Multi-tenant architectures: Imagine a scenario with millions of vectors categorized by tenants. You might search for the closest vectors within a specific tenant (identified by a tenant ID). In cases where the overall vector collection is large (in the millions) but the number of vectors per tenant is small (a few thousand), ANN's accuracy suffers when applying highly selective filters. ENN vector search thrives in this multi-tenant scenario, delivering precise results even with small result sets. Example use cases The small dataset size allows for exhaustive search within a reasonable timeframe, making exact nearest neighbor approach a viable option for finding the most similar data point, improving accuracy confidence in a number of use cases, such as: Multi-tenant data service: You might be building a business providing an agentic service that understands your customers’ data and takes actions on their behalf. When retrieving relevant proprietary data for that agent, it is critical that the right metadata filter be applied and that ENN be executed to retrieve the right sets of documents only corresponding to the appropriate data tenant IDs. Proof of concept development: For instance, a new recommendation engine might have a limited library compared to established ones. Here, ENN vector search can be used to recommend products to a small set of early adopters. Since the data is limited, an exhaustive search becomes practical, ensuring the user gets the most relevant recommendations from the available options. How ENN vector search works on MongoDB Atlas The ENN vector search feature in Atlas integrates seamlessly with the existing $vectorSearch stage within your Atlas aggregation pipelines. Its key characteristics include: Guaranteed accuracy: Unlike ANN, ENN always returns the closest vectors to your query, adhering to the specified limit. Eventual consistency: Similar to approximate vector search, ENN vector search follows an eventual consistency model. Simplified configuration: Unlike approximate vector search, where tuning numCandidates is crucial, ENN vector search only requires specifying the desired limit of returned vectors. Scalable recall evaluation: Atlas allows querying a large number of indexed vectors, facilitating the calculation of comprehensive recall sets for effective evaluation. Fast query execution: ENN vector search query execution can maintain sub-second latency for unfiltered queries up to 10,000 documents. It can also provide low-latency responses for highly selective filters that restrict a broad set of documents into 10,000 documents or less, ordered by vector relevance. Build more with ENN vector search ENN vector search can be a powerful tool when building a proof of concept for retrieval-augmented generation (RAG), semantic search, or recommendation systems powered by vector search. It simplifies the developer experience by minimizing overhead complexity and latency while giving you the flexibility to implement and benchmark precise retrieval. Explore more use cases and build applications faster, start experimenting with ENN vector search.
Unified Namespace Implementation with MongoDB and MaestroHub
In the complex world of modern manufacturing, a crucial challenge has long persisted: how to seamlessly connect the physical realm of industrial control systems with the digital landscape of enterprise operations. The International Society of Automation's ISA-95 standard, often visualized as the automation pyramid, has emerged as a guiding light. As shown below, this five-level hierarchical model empowers manufacturers to bridge the gap between these worlds, unlocking a path toward smarter, more integrated operations. Figure 1: In the automation pyramid, data moves up or down one layer at a time, using point-to-point connections. Manufacturing organizations face a number of challenges when implementing smart manufacturing applications due to the sheer volume and variety of data generated. An average factory produces terabytes of data daily, including time series data from machines stored in process historians and accessed by supervisory control and data acquisition (or SCADA) systems. Additionally, manufacturing execution systems (MES), enterprise resource planning (ERP) systems, and other operations software generate vast amounts of structured and unstructured data. Globally, the manufacturing industry generates an estimated 1.9 petabytes of data annually . Manufacturing leaders are eager to leverage their data for AI and generative AI projects, but a Workday Global Survey reveals that only 4% of the survey’s respondents believe their data is fully accessible for such applications. Data silos are a significant hurdle, with data workers spending an average of 48% of their time on data search and preparation. A popular approach to making data accessible is consolidating it in a cloud data warehouse and then adding context. However, this can be costly and inefficient, as dumping data without context makes it difficult for AI developers to understand its meaning and origin, especially for operational technology time series data. Figure 2: Pushing uncontextualized data to a data warehouse and then adding context is expensive and inefficient. All these issues underscore the need for a new approach—one that not only standardizes data across disparate shop floor systems, but also seamlessly weaves context into the fabric of this data. This is where the Unified Namespace (UNS) comes in. Figure 3: Unified Namespace provides the right data and context to all the applications connected to it. Unified Namespace is a centralized, real-time repository for all production data. It provides a single, comprehensive view of the business's current state. Using an event-driven architecture, applications publish real-time updates to a central message broker, which subscribers can consume asynchronously. This creates a flexible, decoupled ecosystem where applications can both produce and consume data as needed. Figure 4: UNS enables all the enterprise systems to have one centralized location to get the data they need for what they want to accomplish. MaestroHub and MongoDB: Solving the UNS challenge Initially introduced in 2011 at the Hannover Fair of Industrial Technologies, the core idea behind Industry 4.0 is to establish seamless connectivity and interoperability between disparate systems used in manufacturing. And UNS aims to solve this. Over the past five years, we have seen interest in UNS ramping up steadily, and now manufacturers are looking for practical ways to implement it. In particular, a question we’re frequently asked is where does UNS actually live. To answer that question, we need to look at popular architecture patterns, and the pros and cons of each. The most common pattern is implementing UNS in an MQTT broker. An MQTT broker will act as an intermediary entity that receives messages published by clients, filters the messages by topic, and distributes them to subscribers. The reason most manufacturers choose MQTT is it is an open architecture that is easy to implement. However, the challenge with just using the MQTT broker is that the clients don't get historical data access (which will be required to build the analytical and AI applications). Another approach can be to just dump all the data in a data warehouse and then add context to it. This can solve the problem of historical data access but it is an inefficient approach to standardize messages after they have been landed in the data warehouse in the cloud. A superior solution for comprehensive, real-time data access is combining a single source of truth (SSoT) Unified Namespace platform like MaestroHub with a flexible multi-cloud data platform like MongoDB Atlas. MaestroHub creates SSoT for industrial data, resulting in an up to 80% reduction in integration effort for brownfield facilities. Figure 5: MaestroHub SSoT creates a unified data integration layer, saving up to 50% of time in data contextualization (Source: MaestroHub). MaestroHub provides the connectivity layer to all data sources on the factory floor, along with contextualization and data orchestration. This makes it easy to connect the data needed for the UNS, enrich it with more context, and then publish it to consumers using the protocol that works best for them. Under the hood, MaestroHub stores metadata of connections, instances, and flows, and uses MongoDB as the database to store all this data. MongoDB’s flexible data modeling patterns reduce the complexity of mapping and transforming data when it's shared across different clients in the UNS. Additionally, scalable data indexing overcomes performance concerns as the UNS grows over time. Figure 6: MaestroHub and MongoDB together enable a real-time UNS plus long-term storage. MongoDB: The foundation for intelligent industrial UNS In the quest to build a unified namespace system (UNS) for the modern industrial landscape, the choice of database becomes paramount. So why turn to MongoDB? Scalability and high availability: It scales effortlessly, both vertically and horizontally (sharding), to handle the torrent of data from sensors, machines, and processes. Operational Technology (OT) systems generate vast amounts of data from these sources, and MongoDB ensures seamless management of that information. Document data model: Its adaptable document model accommodates diverse data structures, ensuring a harmonious flow of information. A Unified Namespace (UNS) must handle data from any factory source, accommodating structure variations. MongoDB's flexible schema design allows different data models to coexist in a single database, with schema extensibility at runtime. This flexibility facilitates the seamless integration of new data sources and types into the UNS. Real-time data processing: MongoDB Change Streams and Atlas Device Sync empower third-party applications to access real-time data updates. This is essential for monitoring, alerting, and real-time analysis within a UNS, enabling prompt responses to critical events. Gen AI application development with ease: Atlas Vector Search efficiently performs semantic searches on vector embeddings stored in MongoDB Atlas. This capability seamlessly integrates with large language models (LLMs) to provide relevant context in retrieval-augmented generation (RAG) systems. Given that the Universal Name Service (UNS) functions as a single source of truth for industrial applications, connecting gen AI apps to retrieve context from the UNS database ensures accurate and reliable information retrieval for these applications. With the foundational database established, let's explore MaestroHub, a platform designed to leverage the power of MongoDB in industrial settings. The MaestroHub platform MaestroHub is a provider of a SSoT for industrial data, specifically tailored for manufacturers. It achieves this through: Data connectors: MaestroHub connects to diverse data sources using 38 different industrial communication protocols, encompassing OT drivers, files, databases (SQL, NoSQL, time series), message brokers, web services, cloud systems, historians, and data warehouses. The bi-directional nature of 90% of these protocols ensures comprehensive data integration, leaving no data siloed. Data contextualization based on ISA-95: Leveraging ISA-95 Part 2, MaestroHub employs a semantic hierarchy and a clear naming convention for easy navigation and understanding of data topics. The contextualization of the payload is not just limited to the unit of measure AND definitional but also contains Enterprise/Site/Area/Line/Cell details, which are invaluable for analytics studies. Data contextualization is an important feature of a UNS platform. Logic flows/rule engine: Adhering to the UNS principle "Do not make any assumptions on how the data will be consumed," the data should flow flexibly from sources to brokers and from brokers to consumers in terms of rules, frequencies, and multiple endpoints. MaestroHub allows you to set rules such as Always, OnChange, OnTrue, and WhileTrue, where you can dynamically determine the conditions using events and inputs via JavaScript. Insights created by MaestroHub: MaestroHub provides real-time diagnostics of data health by leveraging Prometheus, Elasticsearch, Fluentd, and Kibana. Network problems, changed endpoints, and changed data types are automatically diagnosed and reported as insights. Additionally, MaestroHub uses NATS for queue management and stream analytics, buffering data in the event of a network outage. This allows IT and OT teams to monitor, debug, and audit logs with full data lineage. Conclusion The ISA-95 automation pyramid presents significant challenges for the manufacturing industry, including a lack of flexibility, limited scalability, and difficulty integrating new technologies. By adopting a Unified Namespace architecture with MaestroHub and MongoDB, manufacturers can overcome these challenges and achieve real-time visibility and control over their operations, leading to increased efficiency and improved business outcomes. Read more on how MongoDB enables Unified Namespace via its multi-cloud developer data platform. We are actively working with our clients on solving Unified Namespace challenges. Take a look at our Manufacturing and Industrial IoT page for more stories or contact us through the web form in the link.
Announcing MongoDB Server 8.0 Platform Support Improvements
Last month at MongoDB.local NYC 2024, we announced the preview of MDB 8.0 , the next evolution of MongoDB’s modern database. With MongoDB 8.0, we’re focused on delivering the unparalleled performance, scalability, and operational resilience necessary to support the creation of next-generation applications. For that to be possible, users must be able to deploy MongoDB on industry-standard operating systems. As a result, we are updating our Server Platform Policy to ensure that customers have the best possible experience when using MongoDB. Starting in MongoDB 8.0, there will be two new changes: When a new major version of MongoDB is released, we will only release it operating system (OS) versions that are fully supported by the vendor for the duration of the MongoDB version’s life. In short, we will support an operating system if the operating system’s Extended Lifecycle Support (ELS) date is after the MongoDB Server’s End of Life (EOL) date. We will release new MongoDB Server versions (both major and minor) on the minimum supported minor version of the OS (defined by the OS vendor). Once an OS minor version is no longer supported by the vendor, we will update future MongoDB Server versions to the next supported OS minor version. As always, MongoDB reserves the right to discontinue support for platforms based on lack of user demand and/or technical difficulties (e.g., if a platform doesn’t support required libraries or compiler features). Ensuring best-in-class security MongoDB routinely updates our documentation to indicate which platforms a new version of the MongoDB Server will be available on with the general availability release of that new server version. To ensure that MongoDB customers can meet strong regulatory and security requirements, our software is developed, released, and distributed in accordance with industry security best practices. Given the mission-critical nature of MongoDB’s business—providing a highly secure, performant data platform to tens of thousands of customers in over 100 countries—we strive to provide strong and consistent security assurances across all of our products. In addition, MongoDB partners also need guarantees about the security development lifecycle of our products so they can provide the best experience to their customers. By ensuring that our software runs only on platform versions that are receiving security patches, we aim to limit the vulnerabilities that might be introduced by customers running EOL operating systems. The significance of this change With every major server release, MongoDB determines the supported builds for that general availability (GA) release according to the planned vendor platform’s end of life date —meaning the MongoDB major release will not support the operating system if the operating system’s extended lifecycle support ends before the MongoDB EOL date. This also applies to server container images delivered to our customers. Furthermore, to guarantee security assurances for operating systems that have a minimum minor version, we will only build new versions of MongoDB Server software on a vendor-supported major/minor version of the operating system. Concretely, we will build new versions of MongoDB on a minimum minor version until it hits a maintenance event (defined on a per-vendor basis), and at that point future MongoDB server builds will be updated to the new supported minor version. Separately, when a vendor publishes a new major version of an operating system after a given version of MongoDB reaches GA, we will evaluate whether the latest MongoDB release will run on this new OS version, or we will wait for the next major MongoDB release before documenting formal platform support on our website. Walkthrough: How it could work for you Consider the RHEL 9 Planning Guide below and the hypothetical release cadence of MongoDB version X.0. As long as version X.0 is released three years before the end of RHEL 9 support, which as noted by RHEL is 2032 , we will provide support on RHEL 9. This means that 2029 will be the last year that MongoDB releases a server version on RHEL 9. Next, consider that version X.0 will be released at the end of 2025. Following the Extended Update Support Plan, we will build version X.0 on RHEL 9.6 until the start of 2026 when RHEL 9.8 becomes available. And then for future versions, MDB X.Y will begin being built on RHEL 9.8 until we require the minimum version to be 9.10 in 2027. RHEL 9 planning guide Building the future Overall, these coming changes to the MongoDB Server Platform Policy underscore MongoDB’s commitment to helping developers innovate quickly and easily while providing an even more highly secure and performant data platform. Stay tuned for additional updates about MongoDB 8.0—which will provide optimal performance by dramatically increasing query performance, improving resilience during periods of heavy load, making scalability easier and more cost-effective, and making time series collections faster and more efficient. For more information about the Server Platform Policy updates, please refer to our documentation .
AI-Powered Media Personalization: MongoDB and Vector Search
In recent years, the media industry has grappled with a range of serious challenges, from adapting to digital platforms and on-demand consumption, to monetizing digital content, and competing with tech giants and new media upstarts. Economic pressures from declining sources of revenue like advertising, trust issues due to misinformation, and the difficulty of navigating regulatory environments have added to the complexities facing the industry. Additionally, keeping pace with technological advancements, ensuring cybersecurity, engaging audiences with personalized and interactive content, and addressing globalization issues all require significant innovation and investment to maintain content quality and relevance. In particular, a surge in digital content has saturated the media market, making it increasingly difficult to capture and retain audience attention. Furthermore, a decline in referral traffic—primarily from social media platforms and search engines—has put significant pressure on traditional media outlets. An industry survey from a sample of more than 300 digital leaders from more than 50 countries and territories shows that traffic to news sites from Facebook fell 48% in 2023, with traffic from X/Twitter declining by 27%. As a result, publishers are seeking ways to stabilize their user bases and to enhance engagement sustainably, with 77% looking to invest more in direct channels to deal with the loss of referrals. Enter artificial intelligence: generative AI-powered personalization has become a critical tool for driving the future of media channels. The approach we discuss here offers a roadmap for publishers navigating the shifting dynamics of news consumption and user engagement. Indeed, using AI for backend news automation ( 56% ) is considered the most important use of the technology by publishers. In this post, we’ll walk you through using MongoDB Atlas and Atlas Vector Search to transform how content is delivered to users. The shift in news consumption Today's audiences rarely rely on a single news source. Instead, they use multiple platforms to stay informed, a trend that's been driven by the rise of social media, video-based news formats, and skepticism towards traditional media due to the prevalence (or fear) of "fake news." This diversification in news sources presents a dilemma for publishers, who have come to depend on traffic from social media platforms like Facebook and Twitter. However, both platforms have started to deprioritize news content in favor of posts from individual creators and non-news content, leading to a sharp decline in media referrals. The key to retaining audiences lies in making content personalized and engaging. AI-powered personalization and recommendation systems are essential tools for achieving this. Content suggestions and personalization By drawing on user data, behavior analytics, and the multi-dimensional vectorization of media content, MongoDB Atlas and Atlas Vector Search can be applied to multiple AI use cases to revolutionize media channels and improve end-user experiences. By doing so, media organizations can suggest content that aligns more closely with individual preferences and past interactions. This not only enhances user engagement but also increases the likelihood of converting free users into paying subscribers. The essence of leveraging Atlas and Vector Search is to understand the user. By analyzing interactions and consumption patterns, the solution not only grasps what content resonates but also predicts what users are likely to engage with in the future. This insight allows for crafting a highly personalized content journey. The below image shows a reference architecture highlighting where MongoDB can be leveraged to achieve AI-powered personalization. To achieve this, you can integrate several advanced capabilities: Content suggestions and personalization: The solution can suggest content that aligns with individual preferences and past interactions. This not only enhances user engagement but also increases the likelihood of converting free users into paying subscribers. By integrating MongoDB's vector search to perform k-nearest neighbor (k-NN) searches , you can streamline and optimize how content is matched. Vectors are embedded directly in MongoDB documents, which has several advantages. For instance: No complexities of a polyglot persistence architecture. No need to extract, transform, and load (ETL) data between different database systems, which simplifies the data architecture and reduces overhead. MongoDB’s built-in scalability and resilience can support vector search operations more reliably. Organizations can scale their operations vertically or horizontally, even choosing to scale search nodes independently from operational database nodes, flexibly adapting to the specific load scenario. Content summarization and reformatting: In an age of information overload, this solution provides concise summaries and adapts content formats based on user preferences and device specifications. This tailored approach addresses the diverse consumption habits of users across different platforms. Keyword extraction: Essential information is drawn from content through advanced keyword extraction, enabling users to grasp key news dimensions quickly and enhancing the searchability of content within the platform. Keywords are fundamental to how content is indexed and found in search engines, and they significantly influence the SEO (search engine optimization) performance of digital content. In traditional publishing workflows, selecting these keywords can be a highly manual and labor-intensive task, requiring content creators to identify and incorporate relevant keywords meticulously. This process is not only time-consuming but also prone to human error, with significant keywords often overlooked or underutilized, which can diminish the content's visibility and engagement. With the help of the underlying LLM, the solution extracts keywords automatically and with high sophistication. Automatic creation of Insights and dossiers: The solution can automatically generate comprehensive insights and dossiers from multiple articles. This feature is particularly valuable for users interested in deep dives into specific topics or events, providing them with a rich, contextual experience. This capability leverages the power of one or more Large Language Models (LLMs) to generate natural language output, enhancing the richness and accessibility of information derived from across multiple source articles. This process is agnostic to the specific LLMs used, providing flexibility and adaptability to integrate with any leading language model that fits the publisher's requirements. Whether the publisher chooses to employ more widely recognized models (like OpenAI's GPT series) or other emerging technologies, our solution seamlessly incorporates these tools to synthesize and summarize vast amounts of data. Here’s a deeper look at how this works: Integration with multiple sources: The system pulls content from a variety of articles and data sources, retrieved with MongoDB Atlas Vector Search. Found items are then compiled into dossiers, which provide users with a detailed and contextual exploration of topics, curated to offer a narrative or analytical perspective that adds value beyond the original content. Customizable output: The output is highly customizable. Publishers can set parameters based on their audience’s preferences or specific project requirements. This includes adjusting the level of detail, the use of technical versus layman terms, and the inclusion of multimedia elements to complement the text. This feature significantly enhances user engagement by delivering highly personalized and context-rich content. It caters to users looking for quick summaries as well as those seeking in-depth analyses, thereby broadening the appeal of the platform and encouraging deeper interaction with the content. By using LLMs to automate these processes, publishers can maintain a high level of productivity and innovation in content creation, ensuring they remain at the cutting edge of media delivery. Future directions As media consumption habits continue to evolve, AI-powered personalization stands out as a vital tool for publishers. By using AI to deliver tailored content and to automate back end processes, publishers can address the decline in traditional referrals and build stronger, more direct relationships with their audiences. If you would like to learn more about AI-Powered Media Personalization, visit the following resources: AI-Powered Personalization to Drive Next-Generation Media Channels AI-Powered Innovation in Telecommunications and Media GitHub Repository : Create a local version of this solution by following the instructions in the repository
Helping MongoDB Customers Unlock Potential with Industry Solutions
Gabriela Preiss is a senior manager within Industry Solutions at MongoDB and was instrumental in building out the team in Barcelona. She’s now relocated to Austin, Texas to build a team in Mexico City and continue expanding the Industry Solutions footprint. In this article, Gabriela shares more about Industry Solutions at MongoDB and how they’re making a difference for both customers and our internal go-to-market teams. The impact of industry solutions teams is not lost on the tech industry. I see our competitors and Big Tech alike becoming more verticalized and providing industry-specific solutions to meet their customers’ needs. In 2019, MongoDB established an Industry Solutions team to understand and address our customers’ industry-specific needs and challenges. In the past two years, our Industry Solutions team has increased by roughly 380% in size and touched over 1,100 customer accounts around the globe. Our tailored, industry-specific solutions and messaging give MongoDB a competitive advantage and lead to higher market penetration, higher customer retention, and increased sales. Not to mention, industry-specific insights gleaned from the field drive internal innovation and product development to propel MongoDB forward. Industry Solutions at MongoDB The Industry Solutions team messages MongoDB as a solution or part of a solution to specific industries. We speak the customer's language and understand their industry needs, industry roadblocks, market trends, and competitors. Our industry experts have been in the shoes of the customers and know how to guide them through modernization. We're an extremely cross-functional team, constantly collaborating with sales, marketing, product, product marketing, and engineering. We work in parallel with sales, and everything we do is ultimately to support them and help drive revenue. This means helping with account prepping, speaking one-on-one with customers, and a lot of sales enablement. Our team holds frequent sales trainings in the form of internal content, office hours, and weekly sessions to coach on industry knowledge. We also create content to help drive MongoDB’s go-to-market messaging externally. This means highlighting MongoDB as an industry solution through blogs, white papers, video content, and, most frequently, interactive solution demos that allow customers to really get their hands on our products. Sharing industry knowledge at MongoDB.local events Skills for success While you don’t necessarily need to be a MongoDB expert to join our Industry Solutions team (we’ll train you on that); it’s beneficial to have foundational technical skills and knowledge, like understanding the concept of a database and how it works. As long as you have the will to learn, we’ll shape you into a MongoDB subject matter expert. I often look for people who have a technical knowledge base, but are also interested in the business solution space. Our team is open for anyone who wants to be part of industry solutions and has the willingness to learn all of the ins and outs of it. In terms of specific skills, I personally think soft skills are the most essential for success. For example, having a willingness to learn, boundless curiosity, a sense of urgency, and a true passion for your work. However, things like strong project management and time management skills come in handy as our team covers many different industries and regions. Each industry has its own nuances, and each team member is involved in many different projects. The ability to own your projects from end-to-end and juggle multiple projects at any given time is crucial. Working with AI AI has become a hot topic, and it's not going away. As a team, we work with industries from financial services to manufacturing, automotive to airline, insurance to telecommunications, and everything in between. AI is affecting every industry, and it's impacting every region. It’s our job to keep our finger on the pulse of industry trends to better enable our sales team and have discussions of modernization with customers. As customers look to implement gen AI applications, it’s important for our team to be able to confidently answer their questions and create relevant content. For the Industry Solutions team, working with gen AI is all about educating ourselves and keeping up with the industry trends to create a competitive advantage for MongoDB. Collaboration never ends! Opportunities for learning and growth There are so many, I wouldn't even know where to begin or end. As far as opportunities for learning, you’ll certainly become a subject matter expert in MongoDB. You’ll have the chance to speak with different customers, participate as a presenter at MongoDB and third-party events, hold internal sales enablement trainings, hone in on your content creation skills, and more. You’ll learn some teaching skills if you join the Industry Solutions team, too. A big part of the role is explaining technical concepts to different audiences and sharing information in a way that enables people to learn. At the end of the day, you’ll be building your brand as an industry expert, building your skills, and building your resume. In my personal experience, I started as a consultant on the team and am now a senior manager. I was able to build out our team in Barcelona, Spain, and I've recently relocated to Austin, Texas to build out a team in Mexico City, which I'm so excited for. It's been amazing to see the team grow. In terms of career advancement, there are tons of opportunities for our team members to grow linearly as individual contributors or into team lead and people manager roles. Plus, because you're getting exposure to so many different teams, it's not uncommon for folks from Industry Solutions to transfer into other departments within MongoDB, or vice versa. It’s something that’s not taboo within the company; it's just a matter of having a conversation with your manager. Rooftop views from MongoDB Barcelona Building a team in Mexico City and beyond It’s a really exciting time as we aim to replicate the team that we've created in Barcelona in Mexico City. For prospective candidates, this is a great opportunity to help us build and shape a team from scratch while working on a diverse set of projects in key regions of the business. I would love to see the Mexico City office grow in the same way our Barcelona office has, which, a few years ago was only 12 people and now contains hundreds. We’ll continue to hire for our team in Barcelona and other locations around the world, too. I’m looking forward to bringing on people who will add unique perspectives to our team and grow their careers at MongoDB for years to come. Join our talent community to stay up to date with MongoDB culture content and career opportunities.
Building Gen AI with MongoDB & AI Partners: May 2024
Since I joined MongoDB last September, each month has seemed more action-packed than the last. But it’s possible that May was the busiest of all: May 2024 was a month of big milestones for MongoDB! First, we held MongoDB.local NYC on May 2, our biggest .local event so far, with 2,500 attendees from around the world. It was the first MongoDB.local event I attended since joining the company, and suffice it to say I was thrilled to meet with so many colleagues and partners in person. I was particularly excited to discuss the impact of MongoDB Atlas on the generative AI space, since we also announced the new MongoDB AI Applications Program (MAAP) in May. MongoDB’s CEO, Dev Ittycheria, on the MongoDB .Local NYC keynote stage MAAP was launched to help organizations quickly build, integrate, and deploy gen AI-enriched applications at scale. We do this by providing customers a complete package that includes strategic advisory, professional services, and a robust tech stack through MongoDB and our amazing partners: Anthropic, Anyscale, Amazon Web Services (AWS), Cohere, Credal.ai, Fireworks.ai, Google Cloud, gravity9, LangChain, LlamaIndex, Microsoft Azure, Nomic, PeerIslands, Pureinsights, and Together AI. I really look forward to seeing how MAAP will empower customers to create secure, reliable, and high-performing gen AI applications after the program becomes publicly available in July. Stay tuned for more! And if you’re interested in hearing more about MongoDB’s approach to AI partnerships, and how MAAP will help organizations of all sizes build gen AI applications, check out my interview with theCUBE at MongoDB.local NYC alongside Benny Chen, co-founder of Fireworks.ai. Upcoming AI partner events Are you in San Francisco in late June? We’re proud to sponsor the AI Engineer World’s Fair this year! Stop by the MongoDB booth to chat about gen AI development, and make sure to attend our panel “Building Your AI Stack with MongoDB, Cohere, LlamaIndex, and Together AI” on June 27. Welcoming new AI partners In addition to .local NYC and announcing MAAP in May, we also welcomed four AI partners that offer product integrations with MongoDB: Haystack, Mixpeek, Quotient AI, and Radiant. Read on to learn more about each great new partner. Haystack is an open source Python framework for building custom apps with large language models (LLMs). It allows users to try out the latest models in natural language processing (NLP) while being flexible and easy to use. “We’re excited to partner with MongoDB to help developers build top-tier LLM applications,” said Malte Pietsch, co-founder and CTO of deepset , makers of Haystack and deepset Cloud. “The new Haystack and MongoDB Atlas integration lets developers seamlessly use MongoDB data in Haystack, a reliable framework for creating quality LLM pipelines for use cases like RAG, QA, and agentic pipelines. Whether you're an experienced developer or just starting, your gen AI projects can quickly progress from prototype to adoption, accelerating value for your business and end-users." Learn more about Haystack’s MongoDBAtlasDocumentStore to improve your AI applications. Mixpeek is a multimodal indexing pipeline that gets a database ready for generative AI. It allows developers to treat an object store and a transactional database as a single entity. Ethan Steininger, founder of Mixpeek, explained the value of the MongoDB-Mixpeek integration. “With MongoDB, developers store vectors, metadata, text and all the indexes needed for hyper-targeted retrieval,” he said. “Combined with Mixpeek, they can ensure their S3 buckets and all the documents, images, video, audio and text objects are always consistent with their transactional database, accelerating the path to production by instilling confidence that multimodal RAG results will always be up-to-date." Read more about our partnership and learn how to build real-time multimodal vectors in a MongoDB cluster. Quotient AI is a solution that offers developers the capability to evaluate their AI products with specialized datasets and frameworks to accelerate the experimentation cycle. Julia Neagu, CEO of Quotient AI, highlighted the importance of our partnership. "We are excited to join forces with MongoDB and revolutionize how developers and enterprises are building AI products,” she said. “We share the common goal of helping developers get their ideas to market faster with a first-class developer experience. MongoDB Atlas scalable and versatile vector database technology complements Quotient's mission to ship high-quality, reliable AI applications through rapid, domain-specific evaluation." Learn more how Quotient AI enables evaluation and refinement of RAG-powered AI products built on MongoDB Atlas. Radiant offers a monitoring and evaluation framework for production AI use cases. Nitish Kulnani, CEO of Radiant, shared his excitement about the partnership with MongoDB to enhance the reliability of AI applications. “By combining Radiant's anomaly detection with MongoDB Atlas Vector Search, we enable developers to swiftly identify and mitigate risks, and quickly deploy high-quality AI solutions, delivering real value to customers faster,” he said. “MongoDB trusts Radiant to accelerate its own AI applications, and we're excited to deliver the same experience to MongoDB customers.'' Read more about how to deploy Radiant with MongoDB Atlas to accelerate your journey from development to production. But wait, there’s more! To learn more about building AI-powered apps with MongoDB, check out our AI Resources Hub , and stop by our Partner Ecosystem Catalog to read about our integrations with MongoDB’s ever-evolving AI partner ecosystem.
Spinning Up Innovation: How MongoDB is Driving New Solutions at QSSTUDIO
Building bespoke innovations that address the needs of the future is the driving force behind the unique tech consultancy QSSTUDIO. With global venture capital funding forecast by EY to reach $12 billion this year for generative AI startups alone, the ability to bring ideas to life requires the right combination of technology and understanding of the app creation process. Led by Robin Tang, QSSTUDIO is a bespoke app and web platform development consultancy that provides professional digital expertise to dozens of small businesses, digital agencies, and startups in Australia and abroad. The flexibility of MongoDB Atlas, an integrated suite of data services, gives QSSTUDIO an instant launch pad for new ideas, allowing the team to easily spin-up new solutions and prototypes for clients. From skincare AI, to mining safety tools, and the petrol price tracking app ServoTrack, the team’s technological expertise combines with an understanding of data to bring these new ideas to market. QSSTUDIO has been able to keep its development team small, in part by using MongoDB Atlas Cluster, a NoSQL Database-as-a-Service in the cloud, which enables the team to easily test proof-of-concept designs and then migrate successful apps to Atlas without the need to reprovision the product. “We build bespoke solutions, and the flexibility and scalability of MongoDB Atlas has made it so much easier for us to quickly innovate and iterate, from concept inception to deployment,” Tang says. Tracking the ups and downs of Australia’s petrol prices With petrol prices soaring past AU$2.50/L, the ServoTrack app, founded by Robin Tang, is one of the best ways Australians can look for lower prices as they fluctuate during the week. Almost all of Australia’s state governments now supply unstructured data sources of real-time petrol price information. Tang wanted to create an app that allowed regular people to set alerts and be notified in real time of rises and falls in price. MongoDB Atlas provides the perfect solution to broadcast the data from these reputable sources. “The flexible data structure of MongoDB allows us to use different data schemas for data from different states. With geospatial indexing as well as Atlas Search Indexing, it provides a speedy way to allow users to find their target service station,” Tang says. “Atlas and MongoDB App services provide a few additional benefits for ServoTrack,” he added. “As expected for any cloud provider, the ability to scale whilst retaining automated backups means I could develop without worrying too much about our infrastructure.” Tang can leverage built-in features such as authentication, which allows users to sign in and store app preferences no matter how many devices they owned, whether it is on Android or iOS, as well as provide updates to users. “We use MongoDB Atlas to enable triggers on price changes at set service stations or general locations depending on the data available. This has helped to alert thousands of users of pending price increases, allowing them to save hundreds of dollars per year,” Tang says. RockShield: an innovative solution for miner safety QSSTUDIO created RockSHIELD software that has gone into the smart geotechnical instrumentation solutions delivered by SCT Operations. Recently, the underground mining and civil tunneling industry has undergone a digital transformation, with geotechnical instrumentation evolving from analogue to digital, resulting in more accurate field data that is accessed more frequently. Mining is an inherently high-risk operation, so it was essential to use this improved monitoring data to detect rock movement early and maintain a safer working environment for miners. Using MongoDB, Tang and his team created RockSHIELD’s software, which uses sensors to maintain mine safety by detecting the slightest movement in rocks. “We took the data on displacement, rotation, and angles from the rock sensors and put it into the MongoDB database which can handle flexible data sets,” Tang said. “Using MongoDB, we have the flexibility to store and receive data from a variety of sensors in different formats. The moment the numbers hit a threshold, the dashboard receives a notification that there is an issue. “The clients adopting this solution are very security conscious, and MongoDB’s security features make it attractive for our clients,” Tang says. “Our use of x.509 certificates for authentication, TLS/SSL communication, and Role Based Access Control let us create a hierarchy of access suitable for SCT Operations as well as RockSHIELD’s clients.” It was important to the team to create a system that works and is sustainable in the long-term to improve and maintain as high safety standards as possible. If you want to level-up your organization's innovation, or you’re a startup looking to build proof of concept, it's easy to get started with MongoDB Atlas today.
Microservices: Realizing the Benefits Without the Complexity
The microservice architecture has emerged as the preferred, modern approach for developers to build and deploy applications on the cloud. It can help you deliver more reliable applications, and address the scale and latency concerns for System Reliability Engineers (SREs) and operations. But microservices aren't without their hangups. For developers, microservices can lead to additional complexity and cognitive overhead, such as cross-service coordination, shared states across multiple services, and coding and testing failure logic across disconnected services. While the monolith was suboptimal for compute and scale efficiencies, the programming model was simple. So the question is, can we get the best of both worlds? In addition, how do we make the individual services easier to build and adapt to changing requirements? Since, at their core, microservices provide access to and perform operations on data, how do we architect services so that developers can easily work with data? How can we make it easier for developers to add new types of data and data sources and perform a wide variety of data operations without the complexity of managing caches and using multiple query languages (SQL, full-text and vector search, time series, geospatial, etc.) The development complexity associated with microservice architectures occurs at two levels: service orchestration and service data management. The diagram below depicts this complexity. At the orchestration level, a typical application may support tens or hundreds of processes, and each may have thousands or millions of executions. To make this work, services are often connected by a patchwork of queues. Developers spend quite a bit of time tracking and managing all the various workflows. The sheer scale necessitates a central mechanism to manage concurrent tasks and sharded databases to manage the state of millions of concurrent workflow instances. To add more complexity, each microservice is deployed using a set of data platforms including RDBMS, caches, search engines, and vector and NoSQL databases. Developers must work with multiple query languages, write code to keep data in sync among these platforms and write code to deal with edge cases when invariably data or indexes are not in sync. Finally, developer productivity is inhibited by the brittleness of RDBMS, which lacks flexibility when trying to incorporate new or changing data types. As a result, microservice applications often end up with complex architectures that are difficult to develop against and maintain in terms of both the individual microservices and the service orchestration. Realizing the benefits without the complexity One approach to address these microservice challenges is to combine two technologies: Temporal and MongoDB. Both give you the benefits of microservices while simplifying the implementation of service orchestration. Together, they allow developers to build services that can easily handle a wide variety of data, eliminate the need to code for complex infrastructure and reduce the likelihood of failure. They simplify the data model and your code. In one real-world example, open-source indexing company Mixpeek leverages the combination of MongoDB and Temporal to provide a platform enabling organizations to easily incorporate multi-modal data sources in AI applications. Mixpeek’s CEO Ethan Steininger states, “Temporal’s durable execution guarantees and MongoDB's flexible data model are core components of Mixpeek’s multimodal data processing and storage. Combined, they enable our users to run high volume ML on commodity hardware without worrying about dropped jobs.” MongoDB and Temporal: Build like a monolith with durable microservices Both MongoDB and Temporal were built by developers, for developers. They both use a code-first approach to solving the complex infrastructure needs of our modern applications within our application code and empower developers to be more productive. They are part of an emerging development stack that greatly simplifies data and all the cross-functional coordination we need in our cloud applications. Ultimately, the combination of these two developer-focused platforms allows you to simplify design, development, and testing of microservice-based applications. With the document model of MongoDB, you model data as real world objects and not tables, rows, and columns. With Temporal, you design your end-to-end service flows as workflows as described by domain experts without having to explicitly identify every edge case and exception (Temporal handles those implicitly). Temporal and MongoDB provide the same benefits that, when combined, multiply in value. You become more agile, as not only can everyone understand your code better, but you are no longer challenged by the cognitive overload of trying to coordinate, comprehend, and test a web of disconnected and complex services. Together, they allow us to reliably orchestrate business processes within apps that are all speaking the language of the data itself. Combining Temporal and MongoDB results in the simplified architecture shown below. Temporal enables orchestration to be implemented at a higher level of abstraction, eliminating much of the event management and queuing complexity. MongoDB, in turn, provides a single integrated data platform with a unified query language thereby eliminating much of the data management complexity. Let’s examine MongoDB and Temporal in more depth to better understand their capabilities and why they facilitate the rapid development of microservices-based applications. MongoDB: Simplifying microservice data MongoDB's features align well with the principles of microservices architectures. It reduces the need for niche databases and the associated costs of deploying and maintaining a complicated sprawl of data technologies. More explicitly, MongoDB delivers key benefits for microservice development: Flexible schema, flexible services: Unlike relational databases with rigid schemas, MongoDB's document model allows microservices to easily evolve as data requirements change. Distributed scale for data-heavy, distributed services: MongoDB scales horizontally by adding more partitions to distribute the load. This aligns with the modular nature of microservices, where individual services can scale based on their specific needs. Unified query language reduces microservice sprawl: MongoDB supports a diverse set of data operations without requiring multiple data platforms (caches, vector, and text search engines, time series, geospatial, etc.) Operational efficiency: MongoDB Atlas, the cloud-based version of MongoDB, simplifies managing databases for microservices. It handles provisioning, backups, and patching, freeing developers to focus on core responsibilities. Integrated developer data platform: The integrated developer data platform delivers an intuitive set of tools to build services that support mobile clients, real-time analytics, data visualization, and historical analysis across many service databases. With MongoDB, development teams use one interface for all their services and run it anywhere, even across clouds. Also, it provides a data foundation for your microservices that is highly available, scalable, and secure. It greatly simplifies microservices development so that you can focus on your business problems and not data. Temporal: Don't coordinate services, orchestrate them Temporal delivers an open-source, durable execution solution that removes the complexity of building scalable distributed microservices. It presents a development abstraction that preserves the complete application state so that in the case of a host or software failure, it can seamlessly migrate execution to another machine. This means you can develop applications as if failures—like network outages or server crashes—do not exist. Temporal handles these issues, allowing you to focus on implementing business logic rather than coding complex failure detection and recovery routines. Here's how Temporal simplifies application development: Durable workflows: Temporal maintains the state and progress of a defined workflow across multiple services, even in the face of server crashes, network partitions, and other types of failures. This durability ensures that your application logic can resume where it left off, making your overall application more resilient. Simplifies failure handling: Temporal abstracts away the complex error handling and retry logic that developers typically have to implement in distributed systems. This abstraction allows developers to focus on business logic rather than the intricacies of ensuring their end-to-end services can handle failures gracefully. Scale: Temporal applications are inherently scalable and capable of handling billions of workflow executions. Long-running services: Temporal supports long-running operations, from seconds to years, with the same level of reliability and scalability. By providing a platform that handles the complexities of distributed systems, Temporal allows developers to concentrate on implementing business logic in their services. This focus can lead to faster development times and more reliable applications, as developers are not bogged down by the intricacies of state management, retries, and error handling. The next generation of microservices development is here Developers want to code. They want to solve business problems. They do not want to be bogged down by the complexity of infrastructure failures. They want to model their apps and data so that it is aligned with the real-world entities and domains they are solving for. Using MongoDB and Temporal together solves these complexities. Together, they simplify design, development, and testing of microservice-based applications so that you can focus on business problems and deliver more features faster. Getting started with Temporal and MongoDB Atlas We can help you design the best architecture for your organization’s needs. Feel free to connect with your MongoDB and Temporal account teams or contact us to schedule a collaborative session and explore how Temporal and MongoDB can optimize your AI development process.