Applications

Customer stories, use cases, and experiences of MongoDB

From Relational Databases to AI: An Insurance Data Modernization Journey

Imagine you’re a data architect, a developer, or a data engineer at an insurance company. Management has asked you and your team to build a new AI claim adjustment system, a customer-facing LLM-powered chatbot, and an application to streamline the underwriting process. However, doing so is far from straightforward due to the challenges you face on a daily basis. The bulk of your time is spent navigating your company’s outdated legacy systems, which were built in the 1970s and 1980s. Some of these legacy platforms were written in COBOL and CICS, and today very few people on your team know how to develop and maintain those technologies. Moreover, the data models you work with are another source of frustration. Every interaction with them is a reminder of the intricate structures that have evolved over time, making data manipulation and analysis a nightmare. In sum, legacy systems are preventing your team—and your company—from innovating and keeping up with both your industry and customer demands. Whether you’re trying to modernize your legacy systems to improve operational efficiency, or to boost developer productivity, or if you want to build AI-powered apps that integrate with large language models (LLMs), MongoDB has a solution for that. In this post, we’ll walk you through a journey that starts with a relational data model refactored into MongoDB collections, vectorization and querying of unstructured data and, finally, retrieval augmented generation (RAG) : asking large language models (LLMs) questions about data in natural language. Identifying, modernizing, and storing the data Our journey starts with an assessment of the data sources we want to work with. As shown below, we can bucket the data into three different categories: Structured legacy data: Tables of claims, coverages, billings, and more. Is your data locked in rigid relations schemas? This tutorial is a step-by-step guide on how to migrate a real-life insurance relational model with the help of MongoDB Relational Migrator , refactoring 21 tables to only five MongoDB collections. Structured data (JSON): You might have files of policies, insurance products, or forms in JSON format. Check out our docs to learn how to insert those into a MongoDB collection. Unstructured data (PDFs, Audios, Images, etc.): If you need to create and store a numerical representation (vector embedding) of, for instance, claim-related photos of accidents or PDFs of policy guidelines, you can have a look at this blog that will walk you through the process of generating embeddings of pictures of car crashes and persisting them alongside existing fields in a MongoDB collection. Figure 1: Storing different types of data into MongoDB Regardless of the original format or source, our data has finally landed into MongoDB Atlas into what we call a Converged AI Data Store, which is a platform that centrally integrates and organizes enterprise data, including vectors, that enable the development of ML- and AI-powered applications. Accessing, experimenting and interacting with the data It’s time to put the data to work. The Converged AI Data Store unlocks a plethora of use cases and efficiency gains, both for the business and for developers. The next step of the journey is about the different ways we can interact with our data: Database and Full Text Search: Learn how to run database queries, start from the basics and move up to advanced features such as facets, fuzzy search, autocomplete, highlighting, and more with Atlas Search . Vector Search: We can finally leverage unstructured data. The Image Search blog we mentioned earlier also explains how to create a Vector Search index and run vector queries against embeddings of photos. RAG: Combining Vector Search and the power of LLMs, it is possible to interact in natural language with our data (see Figure 2 below), asking complex questions and getting detailed answers. Follow this tutorial to become a RAG expert. Figure 2: Retrieval augmented generation (RAG) diagram where we dynamically combine our custom data with the LLM to generate reliable and relevant outputs Having explored all the different ways we can ask questions of the data, we made it to the end of our journey. You are now ready to modernize your company’s systems and finally be able to keep up with the business’ demands. What will you build next? If you would like to discover more about Converged AI and Application Data Stores with MongoDB, take a look at the following resources: AI, Vectors, and the Future of Claims Processing: Why Insurance Needs to Understand The Power of Vector Databases Build a ML-Powered Underwriting Engine in 20 Minutes with MongoDB and Databricks

March 14, 2024
Applied

How MongoDB Enables Digital Twins in the Industrial Metaverse

The integration of MongoDB into the metaverse marks a pivotal moment for the manufacturing industry, unlocking innovative use cases across design and prototyping, training and simulation, and maintenance and repair. MongoDB's powerful capabilities — combined with Augmented Reality (AR) or Virtual Reality (VR) technologies — are reshaping how manufacturers approach these critical aspects of their operations, while also enabling the realization of innovative product features. But first: What is the metaverse, and why is it so important to manufacturers? We often use the term, "digital twin" to refer to a virtual replication of the physical world. It is commonly used for simulations and documentation. The metaverse goes one step further: Not only is it a virtual representation of a physical device or a complete factory, but the metaverse also reacts and changes in real time to reflect a physical object’s condition. The advent of the industrial metaverse over the past decade has given manufacturers an opportunity to embrace a new era of innovation, one that can enhance collaboration, visualization, and training. The industrial metaverse is also a virtual environment that allows geographically dispersed teams to work together in real time. Overall, the metaverse transforms the way individuals and organizations interact to produce, purchase, sell, consume, educate, and work together. This paradigm shift is expected to accelerate innovation and affect everything from design to production across the manufacturing industry. Here are some of the ways the metaverse — powered by MongoDB — is having an impact manufacturing. Design and prototyping Design and prototyping processes are at the core of manufacturing innovation. Within the metaverse, engineers and designers can collaborate seamlessly using VR, exploring virtual spaces to refine and iterate on product designs. MongoDB's flexible document-oriented structure ensures that complex design data, including 3D models and simulations, is efficiently stored and retrieved. This enables real-time collaboration, accelerating the design phase while maintaining the precision required for manufacturing excellence. Training and simulation Taking a digital twin and connecting it to physical assets enables training beyond traditional methods and provides immersive simulations in the metaverse that enhance skill development for manufacturing professionals. VR training, powered by MongoDB's capacity to manage diverse data types — such as time-series, key-values and events — enables realistic simulations of manufacturing environments. This approach allows workers to gain hands-on experience in a safe virtual space, preparing them for real-world challenges without affecting production cycles. Gamification is also one of the most effective ways to learn new things. MongoDB's scalability ensures that training data, including performance metrics and user feedback, is efficiently handled to continuously enlarge the training modules and the necessary resources for the ever-increasing amount of data. Maintenance and repair Maintenance and repair operations are streamlined through AR applications within the metaverse. The incorporation of AR and VR technologies into manufacturing processes amplifies the user experience, making interactions more intuitive and immersive. Technicians equipped with AR devices can access real-time information overlaid onto physical equipment, providing step-by-step guidance for maintenance and repairs. MongoDB's support for large volumes of diverse data types, including multimedia and spatial information, ensures a seamless integration of AR and VR content. This not only enhances the visual representation of data from the digital twin and the physical asset but also provides a comprehensive platform for managing the vast datasets generated during AR and VR interactions within the metaverse. Additionally, MongoDB's geospatial capabilities come into play, allowing manufacturers to manage and analyze location-based data for efficient maintenance scheduling and resource allocation. The result is reduced downtime through more efficient maintenance and improved overall operational efficiency. From the digital twin to metaverse with MongoDB The advantages of a metaverse for manufacturers are enormous, and according to Deloitte many executives are confident the industrial metaverse “ will transform research and development, design, and innovation, and enable new product strategies .” However, the realization is not easy for most companies. Challenges include managing system overload, handling vast amounts of data from physical assets, and creating accurate visualizations. The metaverse must also be easily adaptable to changes in the physical world, and new data from various sources must be continuously implemented seamlessly. Given these challenges, having a data platform that can contextualize all the data generated by various systems and then feed that to the metaverse is crucial. That is where MongoDB Atlas , the leading 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 1. Figure 1: MongoDB connecting to a physical & virtual factory Generative AI with Atlas Vector Search With MongoDB Atlas, customers can combine three systems — database, search engine, and sync mechanisms — into one, delivering application search experiences for metaverse users 30% to 50% faster . Atlas powers use cases such as similarity search, recommendation engines, Q&A systems, dynamic personalization, and long-term memory for large language models (LLMs). Vector data is integrated with application data and seamlessly indexed for semantic queries, enabling customers to build easier and faster. MongoDB Atlas enables developers to store and access operational data and vector embeddings within a single unified platform. With Atlas Vector Search , users can generate information for maintenance, training, and all the other use cases from all possible information that is accessible. This information can come from text files such as Word, from PDFs, and even from pictures or sound streams from which an LLM then generates an accurate semantic answer. It’s no longer necessary to keep dozens of engineers busy, just creating useful manuals that are outdated at the moment a production line goes through first commissioning. Figure 2: Atlas Vector Search Transforming the manufacturing industry with MongoDB In the digital twin and metaverse-driven future of manufacturing, MongoDB emerges as a linchpin, enabling cost-effective virtual prototyping, enhancing simulation capabilities, and revolutionizing training processes. The marriage of MongoDB with AR and VR technologies creates a symbiotic relationship, fostering innovation and efficiency across design, training, and simulation. As the manufacturing industry continues its journey into the metaverse, the partnership between MongoDB and virtual technologies stands as a testament to the transformative power of digital integration in shaping the future of production. Learn more about how MongoDB is helping organizations innovate with the industrial metaverse by reading how we Build a Virtual Factory with MongoDB Atlas in 5 Simple Steps , how IIoT data can be integrated in 4 steps into MongoDB, or how MongoDB drives Innovations End-To-End in the whole Manufacturing Chain .

March 12, 2024
Applied

Reducing Bias in Credit Scoring with Generative AI

This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . Credit scoring plays a pivotal role in determining who gets access to credit and on what terms. Despite its importance, however, traditional credit scoring systems have long been plagued by a series of critical issues from biases and discrimination, to limited data consideration and scalability challenges. For example, a study of US loans showed that minority borrowers were charged higher interest rates (+8%) and rejected loans more often (+14%) than borrowers from more privileged groups. The rigid nature of credit systems means that they can be slow to adapt to changing economic landscapes and evolving consumer behaviors, leaving some individuals underserved and overlooked. To overcome this, banks and other lenders are looking to adopt artificial intelligence to develop increasingly sophisticated models for scoring credit risk. In this article, we'll explore the fundamentals of credit scoring, the challenges current systems present, and delve into how artificial intelligence (AI), in particular, generative AI (genAI) can be leveraged to mitigate bias and improve accuracy. From the incorporation of alternative data sources to the development of machine learning (ML) models, we'll uncover the transformative potential of AI in reshaping the future of credit scoring. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. What is credit scoring? Credit scoring is an integral aspect of the financial landscape, serving as a numerical gauge of an individual's creditworthiness. This vital metric is employed by lenders to evaluate the potential risk associated with extending credit or lending money to individuals or businesses. Traditionally, banks rely on predefined rules and statistical models often built using linear regression or logistic regression. The models are based on historical credit data, focusing on factors such as payment history, credit utilization, and length of credit history. However, assessing new credit applicants poses a challenge, leading to the need for more accurate profiling. To cater to the underserved or unserved segments traditionally discriminated against, fintechs and digital banks are increasingly incorporating information beyond traditional credit history with alternative data to create a more comprehensive view of an individual's financial behavior. Challenges with traditional credit scoring Credit scores are integral to modern life because they serve as a crucial determinant in various financial transactions, including securing loans, renting an apartment, obtaining insurance, and even sometimes in employment screenings. Because the pursuit of credit can be a labyrinthine journey, here are some of the challenges or limitations with traditional credit scoring models that often cloud the path to credit application approval. Limited credit history: Many individuals, especially those new to the credit game, encounter a significant hurdle – limited or non-existent credit history. Traditional credit scoring models heavily rely on past credit behavior, making it difficult for individuals without a robust credit history to prove their creditworthiness. Roughly 45 million Americans lack credit scores simply because those data points do not exist for them. Inconsistent income: Irregular income, typical in part-time work or freelancing, poses a challenge for traditional credit scoring models, potentially labeling individuals as higher risk and leading to application denials or restrictive credit limits. In 2023 in the United States , data sources differ on how many people are self-employed. One source shows more than 27 million Americans filed Schedule C tax documents, which cover net income or loss from a business – highlighting the need for different methods of credit scoring for those self-employed. High utilization of existing credit: Heavy reliance on existing credit is often perceived as a signal of potential financial strain, influencing credit decisions. Credit applications may face rejection or approval with less favorable terms, reflecting concerns about the applicant's ability to judiciously manage additional credit. Lack of clarity in rejection reasons: Understanding the reasons behind rejections hinders applicants from addressing the root causes – in the UK, a study between April 2022 and April 2023 showed the main reasons for rejection included “poor credit history” (38%), “couldn’t afford the repayments” (28%), “having too much other credit" (19%) and 10% said they weren’t told why. The reasons even when given are often too vague which leaves applicants in the dark, making it difficult for them to address the root cause and enhance their creditworthiness for future applications. The lack of transparency is not only a trouble for customers, it can also lead to a penalty for banks. For example, a Berlin bank was fined €300k in 2023 for lacking transparency in declining a credit card application. Lack of flexibility: Shifts in consumer behavior, especially among younger generations preferring digital transactions, challenge traditional models. Factors like the rise of the gig economy, non-traditional employment, student loan debt, and high living costs complicate assessing income stability and financial health. Traditional credit risk predictions are limited during unprecedented disruptions like COVID-19, not taking this into account in scoring models. Recognizing these challenges highlights the need for alternative credit scoring models that can adapt to evolving financial behaviors, handle non-traditional data sources, and provide a more inclusive and accurate assessment of creditworthiness in today's dynamic financial landscape. Credit scoring with alternative data Alternative credit scoring refers to the use of non-traditional data sources (aka. alternative data) and methods to assess an individual's creditworthiness. While traditional credit scoring relies heavily on credit history from major credit bureaus, alternative credit scoring incorporates a broader range of factors to create a more comprehensive picture of a person's financial behavior. Below are some of the popular alternative data sources: Utility payments: Beyond credit history, consistent payments for utilities like electricity and water offer a powerful indicator of financial responsibility and reveal a commitment to meeting financial obligations, providing crucial insights beyond traditional metrics. Rental history: For those without a mortgage, rental payment history emerges as a key alternative data source. Demonstrating consistent and timely rent payments paints a comprehensive picture of financial discipline and reliability. Mobile phone usage patterns: The ubiquity of mobile phones unlocks a wealth of alternative data. Analyzing call and text patterns provides insights into an individual's network, stability, and social connections, contributing valuable information for credit assessments. Online shopping behavior: Examining the frequency, type, and amount spent on online purchases offers valuable insights into spending behaviors, contributing to a more nuanced understanding of financial habits. Educational and employment background: Alternative credit scoring considers an individual's educational and employment history. Positive indicators, such as educational achievements and stable employment, play a crucial role in assessing financial stability. These alternative data sources represent a shift towards a more inclusive, nuanced, and holistic approach to credit assessments. As financial technology continues to advance, leveraging these alternative data sets ensures a more comprehensive evaluation of creditworthiness, marking a transformative step in the evolution of credit scoring models. Alternative credit scoring with artificial intelligence Besides the use of alternative data, the use of AI as an alternative method has emerged as a transformative force to address the challenges of traditional credit scoring, for a number of reasons: Ability to mitigate bias: Like traditional statistical models, AI models, including LLMs, trained on historical data that are biased will inherit biases present in that data, leading to discriminatory outcomes. LLMs might focus on certain features more than others or may lack the ability to understand the broader context of an individual's financial situation leading to biased decision-making. However, there are various techniques to mitigate the bias of AI models: Mitigation strategies: Initiatives begin with the use of diverse and representative training data to avoid reinforcing existing biases. Inadequate or ineffective mitigation strategies can result in biased outcomes persisting in AI credit scoring models. Careful attention to the data collected and model development is crucial in mitigating this bias. Incorporating alternative data for credit scoring plays a critical role in reducing biases. Rigorous bias detection tools, fairness constraints, and regularization techniques during training enhance model accountability: Balancing feature representation and employing post-processing techniques and specialized algorithms contribute to bias mitigation. Inclusive model evaluation, continuous monitoring, and iterative improvement, coupled with adherence to ethical guidelines and governance practices, complete a multifaceted approach to reducing bias in AI models. This is particularly significant in addressing concerns related to demographic or socioeconomic biases that may be present in historical credit data. Regular bias audits: Conduct regular audits to identify and mitigate biases in LLMs. This may involve analyzing model outputs for disparities across demographic groups and adjusting the algorithms accordingly. Transparency and explainability: Increase transparency and explainability in LLMs to understand how decisions are made. This can help identify and address biased decision-making processes. Trade Ledger , a lending software as a service (SaaS) tool, uses a data-driven approach to make informed decisions with greater transparency and traceability by bringing data from multiple sources with different schemas into a single data source. Ability to analyze vast and diverse datasets: Unlike traditional models that rely on predefined rules and historical credit data, AI models can process a myriad of information, including non-traditional data sources, to create a more comprehensive assessment of an individual's creditworthiness, ensuring that a broader range of financial behaviors is considered. AI brings unparalleled adaptability to the table: As economic conditions change and consumer behaviors evolve, AI-powered models can quickly adjust and learn from new data. The continuous learning aspect ensures that credit scoring remains relevant and effective in the face of ever-changing financial landscapes. The most common objections from banks to not using AI in credit scoring are transparency and explainability in credit decisions. The inherent complexity of some AI models, especially deep learning algorithms, may lead to challenges in providing clear explanations for credit decisions. Fortunately, the transparency and interpretability of AI models have seen significant advancements. Techniques like SHapley Additive exPlanations (SHAP) values and Local Interpretable Model-Agnostic Explanations (LIME) plots and several other advancements in the domain of Explainable AI (XAI) now allow us to understand how the model arrives at specific credit decisions. This not only enhances trust in the credit scoring process but also addresses the common critique that AI models are "black boxes." Understanding the criticality of leveraging alternative data that often comes in a semi or unstructured format, financial institutions work with MongoDB to enhance their credit application processes with a faster, simpler, and more flexible way to make payments and offer credit: Amar Bank, Indonesia's leading digital bank , is combatting bias by providing microloans to people who wouldn’t be able to get financial services from traditional banks (unbanked and underserved). Traditional underwriting processes were inadequate for customers lacking credit history or collateral so they have streamlined lending decisions by harnessing unstructured data. Leveraging MongoDB Atlas, they developed a predictive analytics model integrating structured and unstructured data to assess borrower creditworthiness. MongoDB's scalability and capability to manage diverse data types were instrumental in expanding and optimizing their lending operations. For the vast majority of Indians, getting credit is typically challenging due to stringent regulations and a lack of credit data. Through the use of modern underwriting systems, Slice, a leading innovator in India’s fintech ecosystem , is helping broaden the accessibility to credit in India by streamlining their KYC process for a smoother credit experience. By utilizing MongoDB Atlas across different use cases, including as a real-time ML feature store, slice transformed their onboarding process, slashing processing times to under a minute. slice uses the real-time feature store with MongoDB and ML models to compute over 100 variables instantly, enabling credit eligibility determination in less than 30 seconds. Transforming credit scoring with generative AI Besides the use of alternative data and AI in credit scoring, GenAI has the potential to revolutionize credit scoring and assessment with its ability to create synthetic data and understand intricate patterns, offering a more nuanced, adaptive, and predictive approach. GenAI’s capability to synthesize diverse data sets addresses one of the key limitations of traditional credit scoring – the reliance on historical credit data. By creating synthetic data that mirrors real-world financial behaviors, GenAI models enable a more inclusive assessment of creditworthiness. This transformative shift promotes financial inclusivity, opening doors for a broader demographic to access credit opportunities. Adaptability plays a crucial role in navigating the dynamic nature of economic conditions and changing consumer behaviors. Unlike traditional models which struggle to adjust to unforeseen disruptions, GenAI’s ability to continuously learn and adapt ensures that credit scoring remains effective in real-time, offering a more resilient and responsive tool for assessing credit risk. In addition to its predictive prowess, GenAI can contribute to transparency and interpretability in credit scoring. Models can generate explanations for their decisions, providing clearer insights into credit assessments, and enhancing trust among consumers, regulators, and financial institutions. One key concern however in making use of GenAI is the problem of hallucination, where the model may present information that is either nonsensical or outright false. There are several techniques to mitigate this risk and one approach is using the Retrieval Augment Generation (RAG) approach. RAG minimizes hallucinations by grounding the model’s responses in factual information from up-to-date sources, ensuring the model’s responses reflect the most current and accurate information available. Patronus AI for example leverages RAG with MongoDB Atlas to enable engineers to score and benchmark large language models (LLMs) performance on real-world scenarios, generate adversarial test cases at scale, and monitor hallucinations and other unexpected and unsafe behavior. This can help to detect LLM mistakes at scale and deploy AI products safely and confidently. Another technology partner of MongoDB is Robust Intelligence . The firm’s AI Firewall protects LLMs in production by validating inputs and outputs in real-time. It assesses and mitigates operational risks such as hallucinations, ethical risks including model bias and toxic outputs, and security risks such as prompt injections and personally identifiable information (PII) extractions. As generative AI continues to mature, its integration into credit scoring and the broader credit application systems promises not just a technological advancement, but a fundamental transformation in how we evaluate and extend credit. A pivotal moment in the history of credit The convergence of alternative data, artificial intelligence, and generative AI is reshaping the foundations of credit scoring, marking a pivotal moment in the financial industry. The challenges of traditional models are being overcome through the adoption of alternative credit scoring methods, offering a more inclusive and nuanced assessment. Generative AI, while introducing the potential challenge of hallucination, represents the forefront of innovation, not only revolutionizing technological capabilities but fundamentally redefining how credit is evaluated, fostering a new era of financial inclusivity, efficiency, and fairness. If you would like to discover more about building AI-enriched applications with MongoDB, take a look at the following resources: Digitizing the lending and leasing experience with MongoDB Deliver AI-enriched apps with the right security controls in place, and at the scale and performance users expect Discover how slice enables credit approval in less than a minute for millions

February 20, 2024
Applied

Safety Champion Builds the Future of Safety Management on MongoDB Atlas, with genAI in Sight

Safety Champion was born in Australia in 2015, out of an RMIT university project aiming to disrupt the safety management industry, still heavily reliant on paper-based processes and lagging in terms of digitisation, and bring it to the cloud. Most companies today need to comply with strict workplace safety policies. This is true for industries reliant on manual workers, such as manufacturing, transport and logistics, construction, and healthcare, but also for companies dealing with digital workers, both working in the office or remotely. To do this, organisations rely on safety management processes and systems that help them comply with government and industry-led regulations, as well as keep their employees safe. Whether it’s legal obligations about safety reporting, management of employees and contractors, or ways to implement company-wide safety programs, Safety Champion’s digital platform provides customers more visibility and tracking over safety programs, and a wealth of data to help make evidence-based safety decisions. "Data is core to our offering, as well as core to how next-generation safety programs are being designed and implemented. With paper-based processes, you simply can’t get access to rich data, connect data sets easily, or uncover organisation-wide insights and patterns that can help drive efficiencies and improve safety outcomes," explains Craig Salter, Founder of Safety Champion. MongoDB Atlas: Unlocking the power of data and analytics to improve safety outcomes for customers Safety Champion started using the self-managed version of MongoDB, and shortly after that in 2017 moved onto MongoDB Atlas which was more cost-effective, meant less overhead and not having to manage the day-to-day tasks required to keep a database up and running. The main challenge is that industry standards and policies around safety vary significantly for every company - the safety risks of an office-based business of digital workers are widely different from the risks workers on a manufacturing plant are exposed to, making data collection and itemisation for deeper insights very complex. MongoDB’s document model, its flexibility, and its ability to handle complex sets of data combined with Atlas’ ease of use for developers made it the perfect fit for Safety Champion. "It was very easy to get started on MongoDB, but also super easy and quick to get applications developed and brought to market," says Sid Jain, Solution Architect for Safety Champion. "The performance optimisation we saw using MongoDB Atlas was significant, and it freed up a lot of time from our developers so they could focus on what matters most to our business, instead of worrying about things like patching, setting up alerts, handling back-ups, and so on." The use of MongoDB Charts also gives Safety Champions’ customers access to important analytics that can be presented in visual forms, fitting very specific use cases and internal audiences. This helps organisations using Safety Champion improve decision-making by presenting very concrete data and graphs that can fuel evidence-based safety decisions. "MongoDB Atlas helps drive efficiencies for our clients, but it also helps safety and operations managers to have a voice in making important safety decisions because they are backed by strong data-led evidence. Connecting data sets means the ability to have a much deeper, richer view of what’s happening and what needs to be done," says Salter. Managing exponential growth: 2024, the year of scaling up, generative AI, Search, and much more Before 2020, Safety Champions was still a small start-up, with its platform managing about 5,000 documents a month - these include incident records, checklists, inspection reports, actionable tasks, task completion reports, and more. The COVID pandemic forced organisations to move their safety processes online and comply with a whole new set of safety measures and policies, which saw the company’s business explode: triple-digit annual growth between 2021 and 2023, a dev team that tripled in size, over 2,000 customers, and now up to 100,000 documents handled per month. "As our company kept growing, with some of our new customers handling tens of thousands of safety documents every month - we knew we needed to enable even more scale and future proof ourselves for the next years to come," explains Salter. "We also knew that if we wanted to take advantage of MongoDB’s capabilities in generative AI, search, multi-region, and more, which a lot of our customers were asking for, we needed to set some strong data foundations." Safety Champion is now in the process of upgrading to MongoDB 6.0, which will offer its clients more speed, especially when handling larger and more complex queries. MongoDB Search is now also available system-wide, allowing search queries to be performed across all the modules a client has added records for. "Since many modules allow linking records to each other, allowing a single search query to find and return records from multiple modules makes a world of difference. Developers no longer have to maintain other data systems and the extraction, transformation, and sync of data between MongoDB and search index happens seamlessly, greatly reducing the Ops burden on dev teams," explains Jain. The use of multi-region functionalities within MongoDB Atlas means customers, especially global ones operating in multiple geographic regions, will be able to segregate data and ensure they meet regulatory requirements around data hosting and security. Lastly, Safety Champion is exploring the potential of generative AI with plans to start using MongoDB Vector Search , later in 2024. Some of the use cases the company is already investigating include semantic insights, understanding textual data that employees enter in forms, applying LLMs to that data, and extracting helpful information from it. "Every client wants more analytics, more insights, and more high-level meaning out of data. It’s not just about making it easier to enter data and seeing safety incidents, it’s about what it means and decisions that can be made from a safety perspective," says Salter. "The new version of the Safety Champion platform powered by MongoDB Atlas means we are fully ready to dive into the next phase of our evolution as a business and offer features such as generative AI which will take both Safety Champions and our customers to the next era of safety management."

February 14, 2024
Applied

Spotlight on Two Aussie Start-Ups Building AI Services on MongoDB Atlas

Australian-based Eclipse AI and Pending AI are using the power of MongoDB Atlas to bring their AI ideas to life and blaze new trails in fields including pharmaceutical R&D and customer retention. With the recent advancements in the fields of AI and generative AI, innovation has been unleashed to new heights. Many organisations are taking advantage of technologies such as Natural Language Processing (NLP), Large Language Models (LLMs), and more to create AI-driven products, services, and apps. Amongst those that are blazing new trails in the AI space are two Australian start-ups: Pending AI , which is helping scientists and researchers in the pharmaceutical space improve early research & development stages, and Eclipse AI , a company that unifies and analyses omnichannel voice-of-customer data to give customers actionable intelligence to drive retention. What they have in common is their choice to use MongoDB Atlas . This multi-cloud, developer data platform unifies operational, analytical, and generative AI data services to streamline building AI-enriched applications. Here is how we are helping these two Australian start-ups create the next generation of AI products faster, with less complexity, and without breaking the bank. Pending AI improves pharmaceutical R&D by leveraging next-generation technologies Pending AI has developed a suite of artificial intelligence and quantum mechanics-based capabilities to solve critical problem statements within the earliest stages of pharmaceutical research and development. The Pending AI platform is capable of dramatically improving the efficiency and effectiveness of the compound discovery pipeline, meaning stakeholders can obtain better, commercially viable scaffolds for further clinical development in a fraction of the time and cost. Building its two artificial intelligence-based capabilities - Generative Molecule Designer and Retrosynthesis Engine - was a mammoth task. The known number of pharmacologically relevant molecules in chemical space is exceptionally large, and there are over 50 million known chemical reactions and billions of molecular building blocks - expert scientists have to undergo cost- and time-inefficient trial-and-error processes to design desired molecules and identify optimal synthesis routes to them. Pending AI needed a database that could handle a very large number of records, and be highly performant at that scale, as required by the vastness of chemical space. A few databases were considered by Pending AI, but MongoDB kept standing out as a battle-tested, reliable, and easy-to-implement solution, enabling Pending AI’s team to build a highly performant deployment on MongoDB Atlas. “As a startup, getting started with the community edition of MongoDB and being able to run a reliable cluster at scale was a huge benefit. Now that we’re starting to leverage the AWS infrastructure in our platform, MongoDB Atlas provides us with a fully managed solution at a low cost, and with a Private Endpoint between our AWS deployment and MongoDB cluster, we have kept latency to a minimum, and our data secure,” said Dr. David Almeida Cardoso , Vice President, Business Development at Pending AI. Output of Pending AI's Generative Molecule Designer Pending AI’s Generative Molecule Designer has been built as a machine learning model on MongoDB Atlas, trained to understand the language of pharmaceutical structures, which allows for automated production of novel compound scaffolds that can be focused and tailored to outputs of biological and/or structural studies. The Retrosynthesis Engine is also built using a set of machine learning models and MongoDB Atlas, trained to understand chemical reactions, which allows for the prediction of multiple, valid synthetic routes within a matter of minutes. “We’re also excited to explore the new Atlas Search index feature in MongoDB 7.0. We hope this will allow us to integrate some of the search functionality, which is currently complex to manage and maintain, directly into MongoDB, rather than relying on a separately maintained Elasticsearch cluster,” added Cardoso. Being part of the MongoDB AI Innovator program also allowed Pending AI to explore leveraging cloud infrastructure to scale its platform and test newer versions of MongoDB quickly and easily. Eclipse AI turns customer interaction insights into revenue Eclipse AI is a SaaS platform that turns siloed customer interactions from different sources - these can be customer calls, emails, surveys, reviews, support tickets, and more - into insights that drive retention and revenue. It was created to address the frustration of customer experience (CX) teams around the number of hours and man-weeks of effort needed to consolidate and analyse customer feedback data from different channels. Eclipse AI took on the challenge of solving this issue and worked hard to find a way to offer customers faster and more efficient ways to turn customer feedback into actionable insights. The first problem was consolidating the voice-of-customer data which was so fragmented; the second was analysing that data and turning it into specific improvement actions to improve the customer experience and prevent customer churn. Because MongoDB Atlas is a flexible document database that also can store and index vector embeddings for unstructured data, it was a perfect fit for Eclipse AI and enabled its small dev team to focus on building the product very efficiently and quickly, without being burdened with managing infrastructure. MongoDB Atlas also comes with key features such as MongoDB Atlas Device SDKs (formerly Realm) and MongoDB Atlas Search that were instrumental in bringing Eclipse AI’s platform to life. "For us, MongoDB is more than just a database, it is data-as-a-service. This is thanks to tools like Realm and Atlas Search that are seamlessly built into the platform. With minimum effort, we were able to add a relevance-based full-text search on top of our data. Without MongoDB Atlas we would not have been able to iterate quickly and ship new features fast,” commented Saad Irfani, co-founder of Eclipse AI. “Best of all, horizontal scaling is a breeze with single-click sharding that doesn't require setting up config servers or routers, reducing costs along the way. The unified monitoring and performance advisor recommendations are just the cherry on top.” Eclipse AI - MongoDB dashboard G2 rated Eclipse AI as the #1 proactive customer retention platform globally for SMEs, a recognition that wouldn’t have been possible without the use of MongoDB Atlas. Exploring your AI potential with MongoDB MongoDB Atlas is built for AI . Why? Because MongoDB specialises in helping companies and their developer teams manage richly structured data that doesn't neatly fit into the rigid rows and columns of traditional relational databases, and turn that into meaningful and actionable insights that help operationalise AI. More recently, we have added Vector Search - enabling developers to build intelligent applications powered by semantic search and generative AI over any type of data - and enhanced AWS CodeWhisperer coding assistant to our list of tools companies can use to further their AI exploration. These are just a handful of examples of what is possible in the realm of AI today. Many of our customers around the world, from start-ups to large enterprises like banks and telcos are investing in MongoDB Atlas and capabilities such as Atlas Search , Vector Search , and more to create what the future of AI and generative AI will look like in the next decade. If you want to learn more about how you can get started with your AI project, or take your AI capabilities to the next level, you can check out our MongoDB for Artificial Intelligence resources page for the latest best practices that get you started in turning your idea into an AI-driven reality.

February 5, 2024
Applied

Connected Vehicles: Accelerate Automotive Innovation With MongoDB Atlas and AWS

Capgemini's Trusted Vehicle Solution heralds a new era in driver and fleet management experiences. This innovative platform leverages car-to-cloud connectivity, unlocking a world of possibilities in fleet management, electric vehicle charging, predictive maintenance, payments, navigation, and consumer-facing mobile applications. Bridging the gap between disparate systems, Trusted Vehicle fast-tracks the development of software-defined vehicles and ushers in disruptive connectivity, autonomous driving, shared mobility, and electrification (CASE) technologies. In this post, we will explore how MongoDB Atlas and AWS work together to power Capgemini's Trusted Vehicle solution. What is Trusted Vehicle? Capgemini’s Trusted Vehicle solution accelerates time-to-market with a secure and scalable platform of next-generation driver and fleet-management experiences. Trusted Vehicle excels in fleet management, EV charging, navigation, and more while also accelerating software-defined vehicle development. By seamlessly connecting disparate systems, it paves the way for disruptive advancements in automotive technologies. AWS for Automotive empowers OEMs, mobility providers, parts suppliers, automotive software companies, and dealerships to effectively utilize AWS, providing them with tailored solutions and capabilities in many areas such as autonomous driving, connected mobility, digital customer engagement, software-defined vehicle, manufacturing, supply chain, product engineering, sustainability, and more. Based on its cloud mobility solution expertise and immense experience in successfully implementing Trusted Vehicle for its clients, Capgemini has developed repeatable and customizable modules for OEMs and mobility companies to accelerate their connected mobility journey. These quick-start modules can be swiftly customized for any organization by adding capabilities. Here are a few examples of the modules: Diagnostics trouble-code tracker for fleet maintenance that bolsters safety and efficiency Fleet management software with keyless vehicle remote control for convenience and security Predictive maintenance for connected vehicles to detect anomalies and ensure proactive interventions For automotive OEMs, innovation through digitization of their products and services is of paramount importance. The development of connected and smart vehicles requires cutting-edge technologies. Capgemini recognizes the significance of robust data platforms in shaping the future of connected vehicles. At the core of the Trusted Vehicle solution lies the MongoDB Atlas developer data platform. This strategic partnership and integration ensures that automotive OEMs can harness the power of a modern, scalable, and secure data platform, enabling efficiency, secure and robust connectivity, and seamless user experiences. Benefits of MongoDB Atlas for Capgemini Trusted Vehicle solution Faster time-to-market and developer velocity MongoDB Atlas’ core value proposition is to offer a unified data platform for developers to build applications. With MongoDB Atlas, Capgemini built the core data processing, from sensor data to valuable business insights, with one API. Limiting the number of infrastructure components helps developers spend less time writing orchestration code and the corresponding automated tests, setting up the infrastructure with all the disaster recovery requirements, and monitoring that stack. Absolving developers from those responsibilities allows them to deliver more features, bringing business value to the customers rather than spending precious time on technical plumbing. Cloud agnosticism and customized Trusted Vehicles for customers MongoDB Atlas is a fully managed database as a service that offers features like multi-cloud clusters, automated data tiering, continuous backups, and many more. With a multi-cloud cluster in MongoDB Atlas, customers can: use data from an application running in a single cloud and analyze that data on another cloud without manually managing data movement. use data stored in different clouds to power a single application. easily migrate an application from one cloud provider to another. Multi-cloud enables improved governance by accommodating customers who require data to be stored in a specific country for legal or regulatory reasons. It also allows for performance optimization by deploying resources in regions nearest to where users are located. Implementing Atlas for the Edge Atlas for the Edge provides a solution that streamlines the management of data generated across various sources at the edge, including connected cars and user applications. Two key components of this solution are Atlas Device Sync and SDKs . Together, they provide a fully managed backend that facilitates secure data synchronization between devices and the cloud. This also includes out-of-the-box network handling, conflict resolution, authentication, and permissions. To successfully implement MongoDB’s Atlas for the Edge solution, AWS Greengrass was used to facilitate over-the-air updates and manage the software deployment onto the vehicles, while Device Sync and SDKs handled the transmission of data from the car back to the cloud. Greengrass allows executing code through lambda functions, utilizing data received via MQTT or from the connected device. Device SDKs, however, overcome AWS Lambda's temporary file system storage limitation by offering a significantly enhanced data storage capacity. Greengrass can now locally store the telematics data in the database provided by the SDKs. Therefore, the data will be stored even if the device is offline. Following the restoration of network connectivity, the locally stored telematics data can be synchronized with the MongoDB Atlas cluster. The storage capabilities of the Device SDKs help ensure that processes run smoothly and continuously. Syncing telemetry data to Atlas Dynamic queries with flexible sync Device Sync lets developers control exactly what data moves between their client(s) and the cloud. This is made possible by flexible sync, a configuration that allows for the definition of a query in the client and synchronization of only the objects that match the query. These dynamic queries can be executed based on user inputs, eliminating developers' need to discern which query parameters to assign to an endpoint. Moreover, with Device SDKs, developers can integrate seamlessly with their chosen platform, directly interfacing with its native querying system. This synergy streamlines the development process for enhanced efficiency. Data ingest for IoT Data ingest , a sync configuration for applications with heavy client-side insert-only workloads facilitates seamless data streaming from the Trusted Vehicle software to a flexible sync-enabled app. This unidirectional data sync is useful in IoT applications, like when a weather sensor transmits data to the cloud. In the case of vehicles, information specific to each car — such as speed, tire pressure, and oil temperature — is transmitted to the cloud. Data ingest is also helpful in writing other types of immutable data where conflict resolution is unnecessary. This includes tasks like generating invoices through a retail application or logging events in an application. Data lifecycle management with Device Sync Atlas Device Sync completely manages the lifecycle of this data. Data ingest and flexible sync handles the writing and synchronization processes, including removing data that is no longer needed from devices. On-device storage, network handling, and conflict resolution ensure that clients retain data even when offline. Once reconnected to a network, data seamlessly and automatically synchronizes with MongoDB Atlas. Processing and accessing data with aggregation pipelines The raw data gathered from individual vehicles, like metrics such as speed, direction, and tire pressure, lacks meaningful interpretation on its own. MongoDB’s aggregation pipeline transforms these individual records into contextualized information like driver profiles, usage patterns, trip specifics, and more, yielding actionable insights. For optimal storage and performance efficiency, MongoDB automatically archives individual records after they are processed, ensuring they remain accessible for future retrieval. Overview of Atlas for the Edge - AWS architecture The implementation of Atlas for the Edge for Trusted Vehicle’s solution shifts the responsibility of collecting, syncing, and processing data from AWS components to Atlas Device Sync and SDKs. The Device SDK for Node.js is used in the lambda function, which runs as soon as the Greengrass core device boots up and stores the vehicle telematics data every two seconds in the Realm DB. Using flexible sync with data ingests, the vehicle will automatically sync the telemetry data from the device to the MongoDB Atlas cluster on AWS into a time series collection. An aggregated document representing the vehicle’s or drivers’ data can be computed with the aggregation pipeline and stored in a collection or as a materialized view and accessed via an API endpoint. Historical telemetric data that gets cold can be automatically archived into cold storage using Online Archive, native to the time series collection. This archived data is still accessible if needed on a specific API endpoint using the federated query feature of MongoDB Atlas. Trusted Vehicle with AWS and MongoDB Atlas MongoDB Atlas offers a trifecta of benefits when utilized within Capgemini's Trusted Vehicle solution. First, it accelerates time-to-market and enhances developer efficiency by streamlining and simplifying the technology stack. Second, MongoDB Atlas proves to be more cost-effective as the fleet of vehicles expands. The reduction in cost per vehicle, especially as scale reaches 1,000 and 10,000, results in a substantial decrease in the total cost of ownership. Keeping efficiencies of scale in mind, the OEMs running millions of cars on the road will certainly benefit from this solution. Third, MongoDB's cloud-agnostic components pave the way for a more flexible and adaptable implementation, breaking free from the constraints of specific cloud environments. Ultimately, MongoDB Atlas not only expedites development and reduces costs but also provides a more versatile solution catering to a wider range of clients. For more information on our partnership with Capgemini, please visit our partner webpage . Additionally, visit our MongoDB in Manufacturing and Automotive page to understand our value proposition for the automotive industry and take a look at our connected vehicle solution video .

January 26, 2024
Applied

Pledging Ourselves to the Future

As MongoDB’s sustainability manager, you could say I think about the climate a lot. After all, doing so is my job. But because it’s January and a time of reflection, I’ve been thinking about climate change more than usual — particularly about the progress we’ve made, but also the work that remains to be done. For example, in December the annual U.N. Climate Change Conference (COP 28) ended with a landmark agreement to transition away from fossil fuels, and the aim of reaching net zero carbon dioxide emissions by 2050. The COP 28 agreement also calls on countries to triple their renewable energy capacity and reduce other forms of emissions. The agreement was very welcome because before COP 28 began the U.N. released a stark report that showed national plans are, "insufficient to limit global temperature rise." As worried as I might be some days, I’m also buoyed by the climate action of the last few years. According to the U.S. Energy Information Administration, in 2022 more energy was generated by renewable sources than by coal for the first time. There have also been several regulations passed globally that make the measurement and disclosure of emissions mandatory, a key step in understanding — and reducing — emissions. MongoDB joins The Climate Pledge In the same spirit of optimism, I’m delighted to announce that MongoDB recently signed The Climate Pledge joining hundreds of leading organizations in publicly affirming our commitment to sustainability. The Climate Pledge’s hundreds of signatories commit to regularly report on their emissions and reach net-zero emissions by 2040 through decarbonization strategies and carbon offsets. “We’re thrilled to join the world’s leading companies — like MongoDB customers Verizon and Telefónica — in signing The Climate Pledge,” said MongoDB chief product officer, Sahir Azam. “MongoDB looks forward to working with the Climate Pledge team to ensure a more sustainable future for everyone.” Signing the The Climate Pledge is hardly the first step MongoDB has taken toward ensuring a more sustainable future. In 2023, MongoDB committed to being 100% powered by renewable energy by 2026, and achieving net-zero carbon emissions by 2030. To meet those targets, we’re working to reduce our carbon footprint through product innovation, by adding new sources of renewable energy, and by making MongoDB employees’ commutes more sustainable. Goodbye waste, hello (energy) savings In 2023, we also announced MongoDB’s new Sustainable Procurement Policy , which aims to ensure that sustainability is considered at all levels of our supply chain. The policy covers everything from the coffee we purchase (100% sustainably sourced) to the single-use items we use (restrictions leading to a 58% waste reduction in 2023). How MongoDB’s workloads are powered falls under our sustainable procurement efforts. Specifically, we’re currently working with our cloud partners — all of whom share MongoDB’s aim to be 100% powered by renewable energy by 2026 — to reduce our carbon footprint. "MongoDB takes its commitment to carbon reduction seriously, and we're fortunate to work with partners who share our enthusiasm for sustainability,” said MongoDB Lead Performance Engineer Ger Hartnett. “We look forward to continuing to collaborate with our partners on groundbreaking, energy-saving technology that makes real reductions in our carbon intensity." To meet our renewable energy target, we’ve focused our efforts on several areas, such as preferring buildings with renewable energy contracts or on-site solar when considering new office space. We’ve also entered into several virtual purchase power agreements (VPPAs). Virtual purchase power agreements are a great way for companies like MongoDB to invest in renewable energy without building anything on-site and are a proven method of adding renewable energy to the grid. Since 2022, MongoDB has worked with the enterprise sustainability platform Watershed to support renewable energy projects through VPPAs. Our first project helped build a solar plant in Texas that Watershed notes, “will avoid 13,000 tons of CO2, equivalent to taking nearly 3,000 gas-powered cars off the road each year.” And MongoDB recently signed a new VPPA that will support the development of solar panels for a factory in India. Solar energy is currently responsible for about 16% of global renewable energy, and only about 3.4% of overall energy in the U.S. Those numbers are sure to change, however. In the last fifteen years, global solar power generation has grown from 11.36 terawatt-hours to 1289.87 terawatt hours. What’s more, coal accounts for about 70% of India’s power — versus 20% in the United States — so projects like this will help reduce emissions across Asia. And because many MongoDB employees are directly impacted by air pollution in India , we see VPPAs as a way of benefitting the health and well-being of our employees, as well as the planet. MongoDB's stubborn optimism In the early months of the pandemic, Tom Rivett-Carnac, founding partner of Global Optimism — which launched The Climate Pledge with Amazon in 2019 — shared a video about shifting one’s mindset and changing the world . In the face of larger-than-life problems (like climate change), “stubborn optimism,” he said, “animates action, and infuses it with meaning.” “When the optimism leads to a determined action, then they can become self-sustaining … the two together can transform an entire issue and change the world,” he noted. “Stubborn optimism can fill our lives with meaning and purpose.” Composting is an example of a stubbornly optimistic action that’s both easy to adopt and one that (if enough of us do it) can change the world. Food waste accounts for 6% of global greenhouse emissions, and composting can help reduce those emissions. To put food waste emissions in perspective, 6% of global greenhouse emissions is roughly three times higher than annual global aviation emissions. In 2023, we also began tracking MongoDB’s waste and landfill diversion, and we’re working to improve how we dispose of waste by adding composting services to MongoDB’s hub offices. More than 80% of MongoDB’s offices already have composting services, and we aim to hit 100% in 2024. Not only have composting and single-use purchase reduction helped to decrease waste emissions, but both are highly visible to MongoDB employees. MongoDB employees are increasingly excited about sustainability, inspiring the creation of a mini-garden in our New York office, and the use of more sustainable commuting methods like biking. Though I tend to bike more for exercise than commuting these days (I’ve racked up more than 1,000 miles on my bike pass!), more and more MongoDB team members get to work in sustainable ways. For example, we’re rolling out electric vehicle commuting in India, an e-bike program was recently introduced in our Dublin office, and the bike locker in MongoDB’s New York HQ is generally packed. “I love biking to the office,” said Perry Taylor, a New York-based Information Technology Lead at MongoDB. “In addition to being a great way to stay fit, it’s awesome that how I commute helps the environment.” Looking back on 2023, I’m pleased with how much we accomplished toward MongoDB’s sustainability goals. At the same time, I recognize that more needs to be done. MongoDB enters 2024 with a renewed commitment to sustainability, and we look forward to furthering our progress. To learn more about MongoDB’s sustainability progress, please check out our Sustainability webpage and our latest Corporate Sustainability Report . For more information about fellow Climate Pledge signatories and an interactive timeline of progress made, visit The Climate Pledge .

January 23, 2024
Applied

Evolve Your Data Models as You Modernize with Hackolade and Relational Migrator

Application modernization has always been a constant. For many developers and database administrators, the realization that their legacy relational databases that have served their apps well to this point are no longer as easy and fast to work with has become glaringly apparent as they strive to incorporate emerging use cases like generative AI, search, and edge devices into their customer experience at an increasing rate. While many are turning to MongoDB Atlas for the flexible document model and wide range of integrated data services, migrations are often seen as daunting projects. MongoDB Relational Migrator has simplified several of the key tasks required to successfully migrate from today's popular relational databases to MongoDB. With Relational Migrator, teams can design their target MongoDB schema using their existing relational one as a blueprint, migrate their data to MongoDB while transforming it to their newly designed schema, and get a head start on app code modernization through code template generation and query conversion. But as organizations scale their MongoDB footprint through migrations and new app launches, a new challenge emerges: managing and evolving data models with more teams and stakeholders. Sooner or later, modernization becomes as much about change management as it does technology — keeping teams aligned is critical for keeping everyone moving forward. This is where Hackolade comes in. Hackolade Studio is a visual data modeling and schema design application that enables developers to design and document their MongoDB data models, and more importantly, use those entity-relationship diagrams (ERDs) to collaborate with their counterparts in other areas of the business, like database administration, architecture, and product management. MongoDB data model in Hackolade Studio No database is an island, and the teams working with MongoDB cannot afford to work in isolation. With Hackolade Studio, database teams can use these ERDs to translate their point-of-view to others, making hand-offs and handshakes with other teams like operations more seamless, driving developer productivity, and accelerating new feature builds. Jump from Relational Migrator to Hackoldate Studio with ease Hackolade Studio is now making it even easier to transition to their application after using MongoDB Relational Migrator to complete their migrations. Teams can now use Hackolade Studio’s reverse-engineering feature to import their Relational migrator project (.relmig) files, bringing their MongoDB schema directly over into Hackolade Studio. With this integration, teams can start with Relational Migrator to build their initial schema and execute their data migration, then transition to Hackolade Studio to document, manage, and evolve their schema going forward - giving them a greater degree of control, visibility, and collaboration needed to support modernization initiatives that include many migrations across several applications, teams, and legacy relational environments. MongoDB Relational Migrator, showing a relational schema on the left and its transformed MongoDB schema on the right Getting started is incredibly easy. First, you’ll need your Relational Migrator project file, which can be exported from Relational Migrator to your local device. Then in Hackolade Studio, use the reverse-engineering workflow to import your .relmig file into a new or existing data model. For a detailed walkthrough, dive into Hackolade’s documentation for this integration. Importing Relational Migrator files in Hackolade Studio As MongoDB adoption grows within your organization, more apps and more teams will need to interact with your MongoDB data models. With Relational Migrator and Hackolade together, you will have the tools at your disposal to not only kickstart migration projects but also manage MongoDB data models at scale, giving your teams the insights and visibility needed to drive performance and guide app modernization initiatives. Learn more about how Hackolade can maximize developer productivity and support your modernization to MongoDB initiatives. Download MongoDB Relational Migrator for free to get started with migrating your first databases.

January 17, 2024
Applied

Integrate OPC UA With MongoDB - A Feasibility Study With Codelitt

Open Platform Communications Unified Architecture (OPC UA) is a widely recognized and important communication standard for Industry 4.0 and industrial IoT. It enables interoperability across different machines and equipment, ensuring reliable and secure information sharing within the Operational Technology (OT) layer. By providing a standard framework for communication, OPC UA enhances data integrity, security, and accessibility of data enabling many use cases for Industry 4.0. OPC UA focuses on standard data transmission and information modeling. It uses multiple data encoding methods such as binary or JavaScript Object Notation (JSON) and leverages different levels of security encryption to address security concerns. For information modeling, it adopts an object-oriented approach to abstract and model specific industrial assets such as robots, machines, and processes. Rich data models and object types can be created for a description of machine attributes and composition. Using OPC UA, the traditional context-less time-series machine data is transformed into a semantic-based information model. MongoDB's document model offers a straightforward and compelling approach for storing OPC UA semantic information models due to its flexibility and compatibility with complex data structures. The document model is a superset of all other types of data models, which makes it very popular in the developer community. OPC UA information models contain detailed relationships and hierarchies, making the dynamic schema of MongoDB a natural fit. Fields in the document are extensible at run time making dynamic updates and efficient querying a breeze. For example, consider an OPC UA information model representing an industrial robot. This model will encompass information about the robot's status, current task, operational parameters, and maintenance history. Example OPC UA information model for an Industrial Robot Robot RobotName (Variable) Status (Variable) CurrentTask (Variable) OperationalParameters (Object) MaxSpeed (Variable) PayloadCapacity (Variable) Reach (Variable) MaintenanceHistory (Array of Objects) Timestamp (Variable) Description (Variable) With MongoDB, this model can be easily represented in a document with nested fields. { "_id": ObjectId("654321ab12345abcd6789"), "RobotName": "Robot1", "Status": "Running", "CurrentTask": "Assembling Component ABC", "OperationalParameters": { "MaxSpeed": 80, // in cm/s "PayloadCapacity": 150, // in kg "Reach": 2.65 // in m }, "MaintenanceHistory": [ { "Timestamp": "2023-08-25T10:00:00", "Description": "Routine checkup" }, { "Timestamp": "2023-06-25T14:30:00", "Description": "Replaced worn-out gripper" } ] } This MongoDB document easily captures the complexities of the OPC UA information model. Hierarchical attributes in the model are maintained as objects and arrays can represent historical data and log files. As the robot runs during the production shift, the document can be easily updated with real-time status information. Instead of worrying about creating a complicated Entity Relationship diagram with SQL databases, MongoDB offers a superior alternative to represent digital shadows of industrial equipment. Now that we have seen how easy it is to model OPC UA data in MongoDB, let's talk about how to connect an OPC UA server to MongoDB. One of our partners, Codelitt is developing a connector that can ingest time-series OPC UA data into MongoDB in real time. Codelitt is a custom software strategy, engineering, and design company. The architecture of the end-to-end solution is shown in Figure 1. Figure 1: High-level architecture and data flow In Figure 1: Industrial equipment and controllers will transmit data to local servers using the OPC UA protocol. OPC UA servers will listen to these devices and broadcast them to all subscribed clients. Clients will listen to specific events/variables and queue the event to be stored. The message broker will provide the queue system to digest a large amount of data and provide reliability between the event source and the data storage. MongoDB Atlas will provide the final destination of data, and the ability to do analytics using the aggregation framework and visualization using Atlas Charts. Technical details It is assumed that the user already has machines that have OPC UA server enabled. For the OPC UA client, depending on the client's preferences, the Codelitt solution can switch between a custom-built OPC UA client based on the Node-OPCUA open source project, AWS IoT SiteWise Edge , or a Confluent-based OPC UA source connector . In the case of a custom-built client, it will connect to the machine's OPC UA server using OPC TCP and extract the necessary data that is then transmitted to a broker. The message broker could be any cloud-provided solution (Azure Event Hub, Amazon Kinesis, etc.) or any form of Kafka implementation from Confluence for example. In the case of Kafka, MongoDB Kafka connector can be leveraged to push data to the database. Finally, leveraging the aggregation framework , the operating parameters of each device are queried for visualization via MongoDB Atlas Charts . In summary, the MongoDB document model elegantly mirrors OPC UA information and there are multiple options available to users who would like to push data from their OPC UA servers to MongoDB. To learn more about MongoDB’s role in the manufacturing sector, please visit our manufacturing webpage . To learn more about how Codelitt is digitally transforming industries, please visit their website .

January 16, 2024
Applied

Introducing the Full Stack FastAPI App Generator for Python Developers

We are thrilled to announce the release of the Full Stack FastAPI, React, MongoDB (FARM) base application generator, coinciding with FastAPI's emerging status as a leading modern Python framework. Known for its high performance and ease of use, FastAPI is quickly becoming a top choice for Python developers. This launch is a significant advancement for Python developers eager to build and maintain progressive web applications using the powerful combination of FastAPI and MongoDB. Bridging the Development Gap While it's always been easy and quick to start building modern web applications with MongoDB and FastAPI, in the past developers still had to make many decisions about other parts of the stack, such as authentication, testing, integration etc., and manually integrate these components in their application. Our new app generator aims to simplify some of this process further. It enables you to quickly spin up a production-grade, full-stack application, seamlessly integrating FastAPI and MongoDB, thereby significantly enhancing the developer experience. Simplifying the Development Journey Now, with the launch of our Full Stack FastAPI App Generator, MongoDB dramatically simplifies the initial stages of project setup for production-grade applications by providing a well-structured app skeleton and reduces the initial learning curve and the time spent setting up the project. For new learners and seasoned developers alike, this means less time figuring out the basics and more time building differentiated experiences for their users. Key Features Included in the App Generator: Complete Web Application Stack: Generates a foundation for your project development, integrating both front-end and back-end components. Docker Compose Integration: Optimized for local development. Built-in Authentication System: Includes user management schemas, models, CRUD, and APIs, with OAuth2 JWT token support and magic link authentication. FastAPI Backend Features: MongoDB Motor for database operations . MongoDB ODMantic for ODM creation . Common CRUD support via generic inheritance. Standards-based architecture, fully compatible with OpenAPI and JSON Schema . Next.js/React Frontend: Middleware authorization for page access control. Form validation using React Hook Form . State management with Redux . CSS and templates with TailwindCSS , HeroIcons , and HeadlessUI . Operational and Monitoring Tools: Includes Celery for task management, Flower for job monitoring, Traefik for seamless load balancing and HTTPS certificate automation, and Github Actions for comprehensive front-end and back-end testing. Start now Accelerate your web application development with MongoDB and FastAPI today. Visit our Github repository for the app generator and start transforming your web development experience. Please note: This tool is an experimental project and not yet officially supported by MongoDB.

January 11, 2024
Applied

Panel: How MongoDB is Helping Drive Innovation for Indian Organisations

At MongoDB.local Delhi a panel of CXOs and IT leaders discussed the strategies and challenges of using software to drive innovation in their organisations. Here are the key lessons they shared. Fostering innovation: tips and challenges Our panel, which included representatives from Appy Pie, Autodit, Formidium, and Tech Mahindra, agreed that the rapid development of data analytics technology, and the scarcity of trained talent on the ground, were key challenges when it comes to driving innovation. To stay on top, Tech Mahindra has a dedicated talent acquisition engine to keep tabs on those technologies, and customer requirements. “We imbibe these learnings, so we’re equipped to deliver solutions on the ground,” explained Shuchi Agrawal, Global Head for Data Analytics, Pre-Sales, and Solutioning at Tech Mahindra (IT services and consulting). “When I think of data and MongoDB, I think MongoDB BIRT (business intelligence reporting tools),” says Shuchi. To accelerate their customers’ journey of transformation, for Tech Mahindra, automation must be based on innovations on the baseline of analytics workloads, i.e. data. “That’s why MongoDB is one of the key elements in most of our solution designs when we’re looking for some of the advanced analytics workloads,” says Shuchi. Choosing technology and evaluating products For Vaibhav Agrawal, Executive Vice President of Formidium, selecting technology to drive innovation comes with key caveats: it must be easy to implement, the talent must exist in the market to do the implementation, zero-trust is essential — as data security is paramount for customers — and it must perform in terms of scalability, efficiency, optimization, and monitoring. “If those things are there, you never have to go to other technology,” says Vaibhav. “And MongoDB Atlas comes in on all those check marks perfectly — that's why we chose it.” Enhancing innovation strategies with database features Vaibhav observed two aspects to any innovation: a) having the idea and creating something, and b) re-innovating it. So, for innovation to perpetuate, ideas must be adapted according to your experience, market changes, and changes in technology — and that means reviewing your products’ performance. “MongoDB Atlas has that amazing 360-degree view of your database activities, and the monitoring of your resources,” says Vaibhav. “Plus, it's very easy to get analytics out of it and change the course of your innovation.” “You always need to keep watch on the performance of a database," he adds. "Then you will be able to keep pace with innovation for years.” To see more announcements and get the latest product updates, visit our What's New page.

January 10, 2024
Applied

Leveraging MongoDB Atlas in your Internal Developer Platform (IDP)

DevOps, a portmanteau of “Developer” and “Operations”, rose to prominence around the early 2010s and established a culture of incorporating automated processes and tools designed to deliver applications and services to users faster than the traditional software development process. A significant part of that was the movement to "shift left" by empowering developers to self-serve their infrastructure needs, in theory offering them more control over the application development lifecycle in a way that reduced the dependency on central operational teams. While these shifts towards greater developer autonomy were occurring, the proliferation of public clouds, specific technologies (like GitHub, Docker, Kubernetes, Terraform), and microservices architectures entered the market and became standard practice in the industry. As beneficial as these infrastructure advancements were, these technical shifts added complexity to the setups that developers were using as a part of their application development processes. As a result, developers needed to have a more in-depth, end-to-end understanding of their toolchain, and more dauntingly, take ownership of a growing breadth of infrastructure considerations. This meant that the "shift left" drastically increased the cognitive load on developers, leading to inefficiencies because self-managing infrastructure is time-consuming and difficult without a high level of expertise. In turn, this increased the time to market and hindered innovation. Concurrently, the increasing levels of permissions that developers needed within the organization led to a swath of compliance issues, such as inconsistent security controls, improper auditing, unhygienic data and data practices increased overhead which ate away at department budgets, and incorrect reporting. Unsurprisingly, the desire to enable developers to self-serve to build and ship applications hadn't diminished, but it became clear that empowering them without adding friction or a high level of required expertise needed to become a priority. With this goal in mind, it became clear that investment was required to quickly and efficiently abstract away the complexities of the operational side of things for developers. From this investment comes the rise of Platform Engineering and Internal Developer Platforms (whether companies are labeling it as such or not). Platform engineering and the rise of internal developer platforms Within a developer organization, platform engineering (or even a central platform team) is tasked with creating golden paths for developers to build and ship applications at scale while keeping infrastructure spend and cognitive load on developers low. At the core of the platform engineering ethos is the goal of optimizing the developer experience to accelerate the delivery of applications to customers. Like teaching someone to fish, platform teams help pave the way for greater developer efficiency by providing them with pipelines that they can take and run with, reducing time to build, and paving the way for greater developer autonomy without burdening developers with complexity. To do this, platform teams strive to design toolchains and workflows based on the end goals of the developers in their organization. Therefore, it’s critical for the folks tasked with platform engineering to understand the needs of their developers, and then build a platform that is useful to the target audience. The end result is what is often (but not exclusively) known as an Internal Developer Platform. What is an IDP? An IDP is a collection of tools and services, sourced and stitched together by central teams to create golden paths for developers who will then use the IDP to simplify and streamline application building. IDPs reduce complexity and lower cognitive load on developers - often by dramatically simplifying the experience of configuring infrastructure and services that are not a direct part of the developer's application. They encourage developers to move away from spending excess time managing the tools they use and allow them to focus on delivering applications at speed and scale. IDPs enable developers the freedom to quickly and easily build, deploy, and manage applications while reducing risk and overhead costs for the organization by centralizing oversight and iteration of development practices. An IDP is tailored with developers in mind and will often consist of the following tools: Infrastructure platform that enabled running a wide variety of workloads with the highest degree of security, resilience, and scalability, and a high degree of automation (eg. Kubernetes) Source code repository system that allows teams to establish a single source of truth for configurations, ensuring version control, data governance, and compliance. (eg. Github, Gitlab, BitBucket) Control interface that enables everyone working on the application to interact with and manage its resources. (eg. Port or Backstage) Continuous integration and continuous deployment (CI/CD) pipeline that applies code and infrastructure configuration to an infrastructure platform. (eg. ArgoCD, Flux, CircleCI, Terraform, CloudFormation) Data layer that can handle changes to schemas and data structures. (eg. MongoDB Atlas) Security layer to manage permissions in order to keep compliance. Examples of this are roles-based compliance tools or secrets management tools (eg. Vault). While some tools have overlap and not all of them will be a part of a specific IDP, the goal of platform engineering efforts is to build an IDP for their developers that is tightly integrated with infrastructure resources and services to maximize automation, standardization, self-service, and scale for developers, as well as maximizing security whilst minimizing overhead for the enterprise. While there will be many different terms that different organizations and teams use to refer to their IDP story, at its core, an IDP is a tailored set of tech, tools, and processes , built and managed by a central team, and used to provide developers with golden paths that enable greater developer self-service, lower cognitive load, and reduce risk. How does MongoDB Atlas fit into this story? Developers often cite working with data as one of the most difficult aspects of building applications. Rigid and unintuitive data technologies impede building applications and can lead to project failure if they don’t deliver the data model flexibility and query functionality that your applications demand. A data layer that isn’t integrated into your workflows slows deployments, and manual operations are a never-ending drag on productivity. Failures and downtime lead to on-call emergencies – not to mention the enormous potential risk of a data breach. Therefore, making it easy to work with data is critical to improving the developer experience. IDPs are in part about giving developers the autonomy to build applications. For this reason, MongoDB’s developer data platform is a natural fit for an IDP because it serves as a developer data platform that can easily fit into any team’s existing toolstack and abstracts away the complexities associated with self-managing a data layer. MongoDB’s developer data platform is a step beyond a traditional database in that it helps organizations drive innovation at scale by providing a unified way to work with data that address transactional workloads, app-driven analytics, full-text search, vector search, stream data processing, and more, prioritizing an intuitive developer experience and automating security, resilience, and performance at scale. This simplification and broad coverage of different use cases make a monumental difference to the developer experience. By incorporating MongoDB Atlas within an IDP, developer teams have a fully managed developer data platform at their disposal that enables them to build and underpin best-in-class applications. This way teams won’t have to worry about adding the overhead and manual work involved in self-hosting a database and then building all these other supporting functionality that come out of the box with MongoDB Atlas. Lastly, MongoDB Atlas can be hosted on more cloud regions than any other cloud database in the market today with support for AWS, Azure, and Google Cloud. How can I incorporate MongoDB Atlas into my IDP? MongoDB Atlas’ Developer Data Platform offers many ways to integrate Atlas into their IDP through many tools that leverage the MongoDB Atlas Admin API. The Atlas Admin API can be used independently or via one of these tools/integrations and provides a programmatic interface to directly manage and automate various aspects of MongoDB Atlas, without needing to switch between UIs or incorporate manual scripts. These tools include: Atlas Kubernetes Operator HashiCorp Terraform Atlas Provider AWS CloudFormation Atlas Resources Atlas CDKs Atlas CLI Atlas Go SDK Atlas Admin API With the Atlas Kubernetes Operator, platform teams are able to seamlessly integrate MongoDB Atlas into the current Kubernetes deployment pipeline within their IDP allowing their developers to manage Atlas in the same way they manage their applications running in Kubernetes. First, configurations are stored and managed in a git repository and applied to Kubernetes via CD tools like ArgoCD or Flux. Then, Atlas Operator's custom resources are applied to Atlas using the Atlas Admin API and support all the building blocks you need, including projects, clusters, database users, IP access lists, private endpoints, backup, and more. For teams that want to take the IaC route in connecting Atlas to their IDP, Atlas offers integrations with HashiCorp Terraform and AWS CloudFormation which can also be used to programmatically spin up Atlas services off the IaC integrations built off the Atlas Admin API in the Cloud environment of their choice.. Through provisioning with Terraform, teams can deploy, update, and manage Atlas configurations as code with either the Terraform Provider or the CDKTF. MongoDB also makes it easier for Atlas customers who prefer using AWS CloudFormation to easily manage, provision, and deploy MongoDB Atlas services in three ways: through resources from the CloudFormation Public Registry, AWS Quick Starts, and the AWS CDK. Other programmatic ways that Atlas can be incorporated into an IDP are through Atlas CLI, which interacts with Atlas from a terminal with short and intuitive commands and accomplishes complex operational tasks such as creating a cluster or setting up an access list interactively Atlas Go SDK which provides platform-specific and Go language-specific tools, libraries, and documentation to help build applications quickly and easily Atlas Admin API provides a RESTful API, accessed over HTTPS, to interact directly with MongoDB Atlas control plane resources. Get started with MongoDB Atlas today The fastest way to get started is to create a MongoDB Atlas account from the AWS Marketplace , Azure Marketplace , or Google Cloud Marketplace . Go build with MongoDB Atlas today!

January 4, 2024
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