MongoDB Blog
Announcements, updates, news, and more
Top 4 Reasons to Use MongoDB 8.0
October 2, 2024
Updates
MongoDB Database Observability: Integrating with Monitoring Tools
This post is the final in a three-part series on leveraging database observability. Welcome back to our series on Leveraging Database Observability! Our previous post showcased a real-world use case highlighting how MongoDB Atlas’s observability tools effectively tackle database performance challenges. Whether you’re a developer, DBA, or DevOps engineer, our mission is to empower you to harness the full potential of your data through our observability suite . Integrating Atlas metrics with your central enterprise observability tools can simplify your operations. By seamlessly working with popular observability tools, our approach helps teams streamline workflows and enhance visibility across systems. Integrating MongoDB Atlas with third-party monitoring tools MongoDB’s developer data platform combines all essential data services for building modern applications within a unified experience. Our purpose-built observability tools for Atlas environments offer automatic monitoring and optimization, guiding diagnostics tailored specifically for MongoDB. Additionally, we extend Atlas metrics into your existing enterprise observability stack, enabling seamless integration without replacing your current tools. This creates a consolidated, single-pane view that unifies Atlas telemetry with other tech and application metrics, ensuring comprehensive visibility into both database and full-stack performance. This integration empowers you to monitor, receive alerts, and make data-driven decisions within your existing workflows, driving greater efficiency. Below is a quick guide to modifying integration settings through the Atlas UI and the popular integrations we support: Navigate to the Project Integrations page in Atlas. Choose the organization and project you want to configure from the navigation bar. On the Project Integrations page, select the third-party services you’d like to integrate. Configure the chosen services with the required API keys and regions. Critical integrations for your observability platform With Atlas’s Datadog and Prometheus integrations, you can send critical MongoDB metrics to these platforms, empowering detailed, real-time monitoring. Through Datadog , you can track database operation counts, query efficiency, and resource usage, ideal for pinpointing bottlenecks and managing resources. Similarly, Prometheus enables you to monitor essential metrics like query times, connection rates, and memory usage, supporting flexible tracking of database health and performance. Both integrations facilitate proactive detection of issues, alert configuration for resource thresholds, and a cohesive view of Atlas data when visualized in Grafana. Atlas’s integration with PagerDuty streamlines incident management by sending metrics like performance alerts, billing anomalies, and security events directly to PagerDuty. This integration records incidents automatically, notifies teams upon alerts, and supports two-way syncing, ensuring resolved alerts in Atlas are reflected in PagerDuty. It enables efficient incident response and resource allocation to maintain system stability. With Atlas integrations for Microsoft Teams and Slack, you can route key metrics—such as query latency, disk usage, and throughput—to these channels for timely updates. Teams can use these insights for real-time performance monitoring, incident response, and collaboration. Notifications through these platforms ensure your team stays informed on database performance, storage health, and user activity changes as they occur. Use case: Centralized observability with MongoDB Atlas, Datadog, and Slack Let’s walk through a hypothetical scenario for ShopSmart, an e-commerce company that leverages MongoDB Atlas to manage its product catalog and customer data. As traffic surges, the DevOps team faces challenges in monitoring application performance and database health effectively. To tackle these challenges, the team leverages MongoDB Atlas’ integration with Datadog and Slack, creating a powerful observability ecosystem. Integrating MongoDB Atlas with Datadog: The team pushes key MongoDB Atlas metrics into Datadog, such as query performance, connection counts, and Atlas Vector Search metrics. With Datadog, they can visualize these metrics and correlate overall MongoDB performance with their other applications. Out-of-the-box monitors and dedicated dashboards allow the team to track metrics like throughput, average read/write latency, and current connections. This visibility helps pinpoint bottlenecks in real time, ensuring optimal database performance and improving overall application responsiveness. Setting up alerts in Datadog: The team configures alerts for critical metrics like high query latency and increased error rates. When thresholds are breached, Datadog instantly notifies the team. This proactive approach allows the team to address potential performance issues before they impact customers. Integrating Datadog with Slack: To ensure fast communication, alerts are sent directly to the dedicated Slack channel, “ShopSmart-Alerts.” This integration fosters seamless collaboration, enabling the team to discuss and resolve issues in real-time. With these integrations, ShopSmart’s engineering team can monitor performance quickly and address issues efficiently. The unified observability approach enhances operational efficiency, improves the customer experience, and supports ShopSmart’s competitive edge in the e-commerce industry. By leveraging MongoDB Atlas, Datadog, and Slack, the team ensures scalable performance and drives continuous innovation. Conclusion MongoDB Atlas empowers developers and organizations to achieve unparalleled observability and control over their database environments. By seamlessly integrating with central enterprise observability tools, Atlas enhances your ability to monitor performance metrics and ensures you can do so within your existing workflows. This means you can focus on building modern applications confidently, knowing you have the insights and alerts necessary to maintain optimal performance. Embrace the power of MongoDB Atlas and transform your approach to database management—because your applications can thrive when your data is observable. And that wraps up our Leveraging Database Observability series! We hope you learned something new and found value in these discussions. Sign up for MongoDB Atlas , our cloud database service, to see database observability in action. To dive deeper and expand your knowledge, check out this learning byte for more insights on the MongoDB observability suite and how it can enhance your database performance.
MongoDB, Microsoft Team Up to Enhance Copilot in VS Code
As modern applications grow increasingly complex, developers face the challenge of meeting market demands for faster, smarter solutions. To stay ahead, they need tools that streamline their workflows, available directly in the environments where they build. According to the 2024 Stack Overflow Developer Survey , Microsoft’s Visual Studio Code (VS Code) is the integrated development environment (IDE) of choice for 74% of professional developers, serving as a central hub for building, testing, and deploying applications. With the rise of AI-powered tools like GitHub Copilot—which is used by 44% of professional developers—there’s a growing demand for intelligent assistance in the development process without disrupting flow. At MongoDB, we believe that the future of development lies in democratizing the value of these experiences by incorporating domain-specific knowledge and capabilities directly into developer flows. That’s why we’re thrilled to announce the public preview of MongoDB’s extension to GitHub Copilot in VS Code. With this integration, developers can effortlessly generate MongoDB queries, inspect collection schemas, and get answers from the latest MongoDB docs—all without leaving their IDE. Our collaboration with MongoDB continues to bring powerful, integrated solutions to developers building the modern applications of the future. The new MongoDB extension for GitHub Copilot exemplifies a shared commitment to the developer experience, leveraging AI to ensure that workflows are optimized for developer productivity by keeping everything developers need within reach, without breaking their flow. Isidor Nikolic, Senior Product Manager for VS Code, Microsoft But we’re not stopping there. As AI continues to evolve, so will the ways developers interact with their tools. Stay tuned for more exciting developments next week at Microsoft Ignite , where we’ll unveil more ways we’re pushing the boundaries of what’s possible with AI through MongoDB and Microsoft’s partnership! What is MongoDB's Copilot extension? MongoDB’s Copilot extension supercharges your GitHub Copilot in VS Code with MongoDB domain knowledge. The Copilot integration is built into the MongoDB for VS Code extension , which has more than 1.8M downloads in the VS Code marketplace today. Type ‘@MongoDB’ in Copilot chat and take advantage of three transformative commands: Generate queries from natural language (/query) —this generates accurate MongoDB queries by passing collection schema as context to Github Copilot Query MongoDB documentation (/docs) —this answers any documentation questions using the latest MongoDB documentation through Retrieval-Augmented Generation (RAG) Browse collection schema (/schema) —this provides schema information for any collection and is useful for data modeling with the Copilot extension. Generate queries from natural language This command transforms natural language prompts into MongoDB queries, leveraging your collection schema to produce precise, valid queries. It eliminates the need to manually write complex query syntax, and allows developers to quickly extract data without taking their focus away from building applications. Whether you run the query directly from the Copilot chat or refine it in a MongoDB playground file, we’ve sped up the query-building process by deeply integrating these capabilities into the existing flow of MongoDB VS Code extension. Query MongoDB documentation The /docs command answers MongoDB documentation-specific questions, complemented by direct links to the official documentation site. There’s no need to switch back and forth between your browser and your IDE; the Copilot extension calls out to the MongoDB Documentation Chatbot API that leverages retrieval-augmented generation technology to generate responses that are informed by the most recent version of the MongoDB documentation. In the near future, these questions will be smartly routed to documentation for the specific server version of the cluster you are connected to in the MongoDB VS Code extension. Browse collection schema The /schema command offers quick access to collection schemas, making it easier for developers to access and interact with their data model in real-time. This can be helpful in situations where developers are debugging with Copilot or just want to know valid field names while developing their applications. Developers can additionally export collection schemas into JSON files or ask follow-up questions directly to brainstorm data modeling techniques with the MongoDB Copilot extension. On the Horizon This is just the start of our work on MongoDB’s Copilot extension. As we continue to improve the experience with new features—like translating and testing queries to and from popular programming languages, and in-line query generation in Playgrounds—we remain focused on democratizing AI-driven workflows, empowering developers to access the tools and knowledge they need to build smarter, faster, and more efficiently, right within their existing environments. Download MongoDB’s VS Code extension and enable the MongoDB chat experience to get started today.
MongoDB is a Leader in The Forrester Wave™: Translytical Data Platforms
We’re pleased to announce that MongoDB has been recognized as a Leader in the recently released Forrester Wave™: Translytical Data Platforms, Q4 2024. The report—which highlights “Leaders, Strong Performers, Contenders, and Challengers” and is “an assessment of the top vendors in the market”—notes that “MongoDB is an excellent choice for organizations looking to enhance their document and NoSQL platforms with real-time insights by leveraging translytical capabilities.” What are translytical capabilities? So what are translytical capabilities? In short, modern applications use a growing number of data types for transactional, operational, and analytical uses. Developers can silo different data types and workloads into separate systems, but this causes architectural complexity and reduced agility for teams. A better approach—and one that speeds development—is to leverage a single platform that can store and use multiple data types for different purposes. Forrester defines these “translytical data platforms” as “next-generation data solutions built on a single database engine to seamlessly support transactional, operational, and analytical workloads without compromising data integrity, performance, or real-time analytics.” That’s why we built MongoDB Atlas as a developer data platform. It brings data like documents, vectors, streaming, and time-series together in one system so that you can run transactional, operational, and analytics workloads in one place. How Forrester measured translytical capabilities To measure providers, Forrester evaluated 15 of the most significant translytical data platform vendors against 26 criteria. These criteria span current offering and strategy, to market presence. Being recognized as a Leader is based on an organization’s scores in both current offering and strategy categories for criteria like vision and innovation. Forrester gave MongoDB the highest possible scores across nine criteria, including: Multimodel 1 Search Development Tools / API Scale optimization Streaming Platform management Roadmap Adoption Number of customers According to the report, “MongoDB continues to expand its translytical market share by delivering new capabilities that enhance automation, intelligent memory tiering, and multimodel support, including vector, streaming, analytics, and integrated transactions.” “Developers have been telling us for years that they need easy ways to work with all their data in one place,” said Jim Scharf, Chief Technology Officer at MongoDB. “That’s what continues to drive our strategy of making MongoDB Atlas the developer data platform. We’re excited to be recognized as a Leader in the new The Forrester Wave™: Translytical Data Platforms, and we will continue to support our customers’ growing needs for their data.” What are MongoDB customers doing with translytical capabilities? The Forrester report notes that organizations “use MongoDB to support real-time analytics, customer intelligence, the Internet of Things (IoT), and AI applications.” So, let’s look at a few examples in action. Companies like Ignition started using MongoDB just for operational data—but, over time, expanded into using Atlas Vector Search for AI use cases. Meanwhile, Bosch Digital makes their IoT data easier to work with by bringing multiple data sources together in a single platform. And, Keller Williams uses MongoDB Charts to bring their analytics to where their transactional data is, making it faster to gather insights for their product teams. Overall, customers are attracted to MongoDB because of how developer-friendly the platform is, and because it simplifies their lives by bringing their data together. Access your complimentary copy of The Forrester Wave™: Translytical Data Platforms, Q4 2024 here . Interested in starting your own translytical journey? Sign up for a free MongoDB Atlas account today! 1 Multimodel is defined as support for storing and using various data types.
MongoDB Helps Asian Retailers Scale and Innovate at Speed
More retailers across ASEAN are looking to the document database model to support the expansion of their businesses and respond quickly to ever-more-rapidly changing customer demands. Here are two stories shared during our MongoDB.local events in Indonesia and Malaysia in September 2024. Simplicity and offline availability: EasyEat empowers merchants to optimize dining experiences with MongoDB Atlas EasyEat delivers a software-as-a-service (SaaS) point-of-sale (POS) system tailored for restaurants. It simplifies daily operations, optimizes costs, and enhances customer satisfaction for merchants that provide food delivery and pickup services. The platform launched in 2020, and in less than 4 years it has grown to serve over 1,300 merchants and over four million consumers across Malaysia and Indonesia. Speaking at MongoDB.local Kuala Lumpur in September 2024 , Deepanshu Rawat, Engineering Manager at EasyEat, explained how MongoDB Atlas empowered EasyEat to rapidly scale its operations across both the merchant POS and consumer applications. EasyEat’s move from a SQL database to MongoDB Atlas also delivered greater flexibility, enabling faster product development and ease of use for the engineering team. For EasyEat, MongoDB Atlas is more than just a database. The retailer is making full use of the developer data platform’s unique features, including: Analytics node: EasyEat must regularly provide reports to its merchants. These queries tend to be complex, taking significant time to process and putting an excessive load on the system. “With MongoDB Atlas’s analytics node , we are able to process those heavy queries without it impacting our daily operations,” said Rawat. Atlas Triggers: EasyEat uses this feature to perform a range of asynchronous operations. “Using Atlas Triggers helps us optimize the performance of our applications,” said Rawat. MongoDB Atlas Search: EasyEat has started using MongoDB Atlas Search to execute faster and more efficient searches as its platform’s user base grows. “Atlas Search enables us to make searches in our user application very smooth, and on our end, we don’t face any delay or latency issues,” said Rawat. In addition, EasyEat is exploring a few other capabilities on MongoDB, including online archiving . The company is also considering how it can use generative AI via MongoDB Atlas Vector Search to build a personalized recommendations engine. From 10 seconds to 1: Alfamart drives 1,000% efficiency using MongoDB Atlas Alfamart is a leading retailer with over 19,000 stores across Indonesia and the Philippines. It serves 18.1 million customers and handles approximately 4.6 million retail transactions daily. Speaking at MongoDB.local Jakarta in September 2024 , Alfamart’s Chief Technology Officer, Bambang Setyawan Djojo, shared insights into how the company has used MongoDB Atlas to sustain massive scale and to power its digital transformation. The 2015-2020 period was critical for Alfamart. It was in the midst of rapid expansion and had an ambitious digital transformation agenda. In early 2020, as the COVID-19 pandemic began, Alfamart’s offline transactions plummeted while its online transactions soared. “The growth of online transactions was not linear but exponential,” said Setyawan Djojo. “This was the moment: We knew we needed the tools to adapt quickly and go to market fast. This is when we decided to look for a new database.” With its previous SQL database, Alfamart struggled to handle the growing data load, particularly during peak hours. MongoDB Atlas’s flexible document database model delivered greater efficiency for Alfamart’s team of 350 developers. It also smoothly accommodated Alfamart’s need for sudden and significant upscaling. “Fast processing times are critical to keep our customers happy,” said Setyawan Djojo. “It used to take us 10 seconds to scan members during peak hours, but with MongoDB, it is now below one second.” Setyawan Djojo added, “MongoDB helped us eliminate a lot of downtime compared to our previous SQL database.” MongoDB Atlas’s auto-scaling capabilities were a game changer for Alfamart. “MongoDB can automatically scale up and down depending on the usage of resources and performance. So during peak times, the database can scale up, and once the transaction peak is passed, it can scale back down,” said Setyawan Djojo. Looking ahead, Alfamart plans to continue exploring the potential of the MongoDB Atlas platform to further increase productivity, efficiency, and flexibility. Visit our solutions page to learn more about how MongoDB is helping retailers innovate worldwide. Check out our quick-start guide to get started with MongoDB Atlas Vector Search today. Visit our product page to learn more about MongoDB Atlas Search .
Building Gen AI with MongoDB & AI Partners | October 2024
It’s no surprise that AI is a topic of seemingly every professional conversation and meeting nowadays—my friends joke that 11 out of 10 words that come out of my mouth are “gen AI.” But an important question remains: do organizations truly know how to harness AI, or do they simply feel pressured to join the crowd? Are they driven by FOMO more than anything else? One thing is for sure: adopting generative AI still presents a huge learning curve. Which is why we’ve been working to provide the right tools for companies to build innovative gen AI apps with, and why we offer organizations a variety of AI knowledge and guidance, regardless of where they are with gen AI. We’re fortunate to work with our industry-leading partners to help educate and shape this nascent market. Working so closely with them on product launches, integrations, and solving real-world challenges allows us to bring diverse perspectives and a better understanding of AI to our customers, giving them the technology and confidence to move forward even before engaging with tough use cases and specific technical problems (something that the MongoDB AI Applications Program can definitely help with). One of our main educational initiatives has been our webinar series with our top-tier MAAP partners. We’ve constantly launched video content to deepen understanding of topics essential to gen AI for enterprises answering broader questions such as “ how can my company generate AI-driven outcomes ” and “ how can I modernize my workload ,” to specific, tangible topics such as “ how to build a chatbot that knows my business .” Each session is designed to move beyond the basics, sharing insights from experts in AI, and addressing our customers’ burning questions and challenges that matter most to them. Welcoming new AI and tech partners In October, we also welcomed four new AI and tech partners that offer product integrations with MongoDB. Read on to learn more about each great new partner! Astronomer Astronomer empowers data teams to bring mission-critical software, analytics, and AI to life and is the company behind Astro, the industry-leading data orchestration and observability platform powered by Apache Airflow. " Astronomer's partnership with MongoDB is redefining RAG workflows for GenAI workloads. By integrating Astronomer's managed Apache Airflow platform with MongoDB Atlas' powerful vector database capabilities, we enable organizations to orchestrate complex data pipelines that fuel advanced AI and machine learning applications”, said Julian LaNeve, CTO at Astronomer. “This collaboration empowers data teams to manage real-time, high-dimensional data with ease, accelerating the journey from raw data to actionable insights and transforming how businesses harness the power of generative AI." CloudZero CloudZero is a cloud cost optimization platform that automates the collection, allocation, and analysis of cloud costs to identify savings opportunities and improve cloud efficiency rates. "Database spending is one of the shared costs that can make it tricky for organizations to reach 100% cost allocation. CloudZero eliminates that problem," said Anand Sundaram, Senior Vice President of Product at CloudZero. “ Our industry-leading allocation engine can organize MongoDB spend in a matter of hours , tracing it precisely to the products, features, customers, and/or teams responsible for it. This way, companies get a clear view of what’s driving their costs, who’s accountable, and how to optimize to maximize their cloud efficiency.” ObjectBox ObjectBox is an on-device vector database for mobile, IoT, and embedded devices that enables storing, syncing, and querying data locally online and offline. " We’re thrilled to partner with MongoDB to give developers an edge,” celebrated Vivien Dollinger, CEO and co-founder of ObjectBox. “By combining MongoDB’s cloud and scalability with ObjectBox’s high-performance on-device database and data sync, we empower developers to build fast, data-rich applications that feel right at home across devices and environments. Offline, online, edge, cloud, whenever, wherever... We’re here to enable your data with speed and reliability." Rasa Rasa is a flexible framework for building conversational AI platforms that lets companies develop scalable generative AI assistants that hit the market faster. “ Rasa is excited to partner with MongoDB to empower companies in building conversational AI experiences. Together, we’re helping create generative AI assistants that save costs, speed up development, and maintain full brand control and security,” said Melissa Gordon, CEO of Rasa. “With MongoDB, deploying production-ready generative AI assistants is seamless, and we’re eager to continue accelerating our customers’ journey toward trusted conversational AI solutions.” But wait, there's more! Whether you’re starting out or scaling up, MongoDB and our partners are here with the resources, expertise, and trusted guidance to help you succeed in your genAI strategy! And if you have any suggestions for a good webinar topic, don’t hesitate to reach out. 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.
MongoDB: Powering Digital Natives
Today's rapidly evolving digital landscape is dominated by digital native companies, driving innovation . These are companies born in the digital age and who operate through digital channels with a business model enabled by technology and data. They are not only adept at using technology but are also reshaping the way software is developed and deployed. This article delves into the challenges and opportunities facing digital natives in modern application development, with a particular focus on the complexities of managing data. We’ll explore how the right data platform can empower your digital native organization to build high-quality software faster, adapt to changing market demands, and unlock the full potential of your business. Strong foundations: The four pillars of tech-fueled growth for digital natives Achieving explosive growth requires a strong foundation built on specific principles, which empower rapid scaling and success. Here, we explore the four key pillars that fuel tech-driven growth for digital natives: Product-market fit, fast: As a digital native, you must continuously ship and iterate products to achieve a quick product-market fit. This builds customer trust and captures opportunities before competitors can in an evolving market. Data and AI-driven decisions: You must leverage data to personalize experiences, automate processes, and guide product decisions. A robust data architecture feeds real-time data into AI models, enabling data-driven decisions organization-wide. Balance of freedom and control: Your developers must have the freedom to choose technologies, even as your organization maintains control over the infrastructure to manage risks and costs at scale. Selected technologies must integrate within your overall technology estate. Extensible and open technologies: You must explore disruptive technologies while maintaining existing systems. Freedom from platform and vendor lock-in enables quick adoption of innovations, from current generative AI capabilities to future technological advances. Data: The unsolved challenge in modern application development From cloud platforms and managed services to gen AI code assistants, advancements have transformed how engineering teams build, ship, and run applications: Agile methods and programmatic APIs streamline development, while CI/CD and infrastructure as code automate processes. Containerization, microservices, and serverless architectures enable modularity, while new languages and frameworks boost capabilities. Enhanced logging and monitoring tools provide deep application health insights. Figure 1: Tools and processes to maximize velocity. But none of these advancements address where developers spend most of their time— data . In fact, 73% of developers share time and again that working with data is the hardest part of building an application or feature. So why is data the problem? Traditionally, selecting a database, often an open-source relational one, is the first step in development. However, these databases can struggle with the characteristics of modern data: it’s high volume, unstructured, and constantly evolving. As applications mature and their data demands grow, development teams may encounter challenges with achieving scalability and maintaining service resilience. Some teams turn to NoSQL databases, but even then they find there are limited capabilities, pushing them back to relational databases. As the application gains traction, the business’s appetite for innovation grows, compelling development teams to incorporate an expanding array of database technologies. This results in an architectural sprawl, imposing on teams the challenges of mastering, sustaining, and harmonizing new technologies. Concurrently, the dynamic technology landscape undergoes constant evolution, demanding teams to swiftly adjust. As a result, self-contained, autonomous teams encounter these hurdles recurrently, highlighting the pressing need for streamlined solutions to mitigate complexity and enhance agility. Figure 2: The evolving tech landscape. Data sprawl: A major threat to developer productivity and business agility Data sprawl is slowing everyone down. The more systems we add, the harder it is for developers to keep up. Each new database brings its own unique language, format, and way of working. This creates a huge headache for managing everything—from buying new systems to making sure they all work together securely. It’s a constant battle to keep data accessible, consistent, and backed up across all these different platforms. Figure 3: Teams building on separate stacks leads to data sprawl and manageability issues across the organization It compromises every single one of the four outcomes your technology foundation should be providing, yielding the opposite results: Missed opportunities, lost customers: Fragmented development experiences consume time as engineers struggle with multiple technologies, frameworks, and extract, transform, and load mechanisms for duplicating data between systems. This slows down releases, degrades digital product quality, and impedes engineers from achieving product-market fit and effective competition. Flying blind: With your operational data siloed across multiple systems, you lack the data foundations necessary to use live data in shaping customer experiences or reacting to market changes. This is because you are unable to feed reliable, consistent, real-time data into your AI models to take action within the flow of the application or to provide the business with up-to-the-second visibility into operations. High attrition, high costs: Complex data architecture impacts development team culture, leading to siloed knowledge, inefficient collaboration, and decreased developer satisfaction. This complexity also consumes substantial resources in maintaining existing systems by diverting resources from new projects that are vital for business competition in new markets. Disruption from new technologies: Dependence on any one cloud provider can stifle innovation for development teams by restricting access to the latest technologies. Developers are confined to the tools and services offered by a single provider, hindering their ability to explore and integrate new, potentially more efficient, or advanced technologies. Speed: A unified developer experience for building high-quality software faster In today’s digital world, speed is king. Your customers expect seamless experiences, but clunky applications leave them frustrated. But traditional databases can be a bottleneck, struggling to keep pace with your ever-evolving data and slowing down development. The future of data is here, and it’s flexible: a data platform built for digital natives . It leverages a flexible document model, letting you store and work with your data exactly how you need it. This eliminates rigid structures and complex migrations, freeing your developers to focus on what matters—building amazing applications faster. Flexible document data models empower developers to handle today’s rapidly evolving application data ( 80%+ unstructured) that relational databases struggle with. MongoDB documents are richly typed, boosting developer productivity by eliminating the need for lengthy schema migrations when implementing new features. Developers get to use their preferred tools and languages. Through its drivers and integrations, MongoDB supports all of the most popular programming languages, frameworks, integrated development environments, and AI-code assistance tools. MongoDB scales! It starts small and scales globally. Built for elasticity and horizontal scaling, it handles massive workloads without app changes. Figure 4: A unified developer experience, integrating all necessary data services for building sophisticated modern applications Introducing MongoDB Atlas : a fully-managed cloud database built for the modern developer. It enables the integration of real-time data from devices with AI capabilities (through vector embeddings and large language models ) to personalize user experiences. Stream processing empowers constant data analysis, while in-app analytics provides real-time insights without needing separate data warehouses, all while automatically managing data movement and storage for cost-effectiveness. MongoDB Atlas simplifies database management with the following: Easy deployment via UI, API, CLI, Kubernetes, and infrastructure as code tools. Automated operations for cost-effective performance and real-time monitoring. MongoDB Atlas customer success stories: Development with speed, scale, and efficiency Delivery Hero Delivery Hero, a global leader in online food delivery, leverages MongoDB Atlas to power its rapid service. Founded in 2011, Delivery Hero now serves millions of customers in over 70 countries through brands like PedidosYa, foodpanda, and Glovo. Having replaced its legacy SQL database, Delivery Hero optimized operations and bolstered performance by using MongoDB Atlas. By leveraging MongoDB Atlas Search, Delivery Hero revolutionized its search functionality, ensuring a seamless user experience for its extensive customer base through simplified indexing and real-time data accuracy. MongoDB’s scalability has empowered Delivery Hero to manage over 100 million products in its catalog without encountering latency issues, enabling the company to expand its services while maintaining peak performance. This agility, coupled with MongoDB’s cost-effectiveness, has enabled Delivery Hero to swiftly adapt to evolving customer demands, solidifying its position in the fiercely competitive delivery market. MongoDB Atlas Search was a game changer. We ran a proof of concept and discovered how easy it is to use. We can index in one click, and because it’s a feature of MongoDB, we know data is always up-to-date and accurate. Andrii Hrachov, Principal Software Engineer, Delivery Hero Read the full customer story to learn more. Coinbase Coinbase, a prominent cryptocurrency exchange boasting 245,000 ecosystem partners and managing assets worth $273 billion , trusts MongoDB to handle its extensive data workload. As the company grew, MongoDB scaled seamlessly to accommodate the increased demand. To further improve performance in the fast-paced crypto world, Coinbase partnered with MongoDB to develop a system that significantly accelerated data transfer to reporting tools, reducing processing time from days to a mere 5-6 hours. This near real-time data access enables Coinbase to rapidly analyze trends and make informed decisions, maintaining a competitive edge in the ever-evolving crypto landscape. Watch Coinbase's full session at MongoDB.local Austin, 2024 to learn more. MongoDB: Your flexible platform for digital growth With MongoDB, you can freely explore, experiment, develop, and deploy according to your digital-native business needs. If you would like to learn more about how MongoDB can empower your digital-native business to conquer market trends, visit: Innovate With AI: The Future Enterprise Application-Driven Intelligence: Defining the Next Wave of Modern Apps AI-Driven Real-Time Pricing with MongoDB and Vertex AI
MongoDB and Partners: Building the AI Future, Together
If you’re like me, over the past year you’ve closely watched AI’s developments—and the world’s reactions to them. From infectious excitement about AI’s capabilities, to impatience with its cost and return on investment, every day has been filled with AI twists and turns. It’s been quite the roller coaster. During the ride, from time to time I’ve wondered where AI falls on the Gartner hype cycle, which gives "a view of how a technology or application will evolve over time." Have we hit the "peak of inflated expectations" only to fall into the "trough of disillusionment?" Or is the hype cycle an imperfect guide, as The Economist argues? The reality is that it takes time for any new technology—even transformative ones like AI—to take hold. And every advance, no matter how big, has had its detractors. A famous example is that of Picasso (!), who in 1968 said, “Computers are useless. They can only give you answers.” (!!) For our part, MongoDB is convinced that AI is a once-in-a-generation technology that will enhance every future application—a belief that has been reinforced by the incredible work our partners have shared at MongoDB’s 2024 events. Speeding AI development MongoDB is committed to helping organizations of all sizes succeed with AI, and one way we’re doing that is by collaborating with the MongoDB partner ecosystem to create powerful, user-friendly AI development tools and solutions. For example, Fireworks.ai —which is a member of the MongoDB AI Applications Program ecosystem —created an inference solution that hosts gen AI models and supports containerized deployments. This tool makes it easier for developers to build and deploy powerful applications with a range of easy-to-use tools and customization options. They can choose to use state-of-the-art, open-source language, image, and multimodal foundation models off the shelf, or they can customize and fine-tune models to their needs. Jointly, Fireworks.ai and MongoDB provide a solution for developers who want to leverage highly curated and optimized open-source models and combine these with their organization’s own proprietary data—and to do so with unparalleled speed and security. “MongoDB is one of the most sophisticated database providers, and it’s very easy to use,” said Benny Chen , cofounder of Fireworks.ai. "We want developers to be able to use these tools, and we want to work with providers who enable and empower developers." Nomic , another MAAP ecosystem member, also enables developers with best-in-class solutions across the entire unstructured data workflow. Their Embed offering, available through the Nomic API , allows users to vectorize large-scale datasets for use in text, image, and multimodal retrieval applications, including retrieval-augmented generation (RAG), using only their web browser. The Nomic-MongoDB solution is a highly efficient, open-weight model that developers can use to visualize the unstructured datasets they store in MongoDB Atlas . These insights help users quickly discover trends and articulate data-driven value propositions. Nomic also supported the recently announced vector quantization in MongoDB Atlas Vector Search , which reduces vector sizes while preserving performance. Last—but hardly least!—there’s our new reference architecture with MAAP partners AWS and Anthropic. Announced at MongoDB.local London , the reference architecture supports building memory-enhanced AI agents, and is designed to streamline complex processes and develop smarter, more responsive applications. For more—including a link to the code on Github— check out the MongoDB Developer Center . Making AI work for anyone and everyone The companies MongoDB partners with aren’t just making gen AI easier for developers—they’re building tools for everyone. For example, Capgemini has invested $2 billion in gen AI and is training 100,000 of its employees in the technology. GenYoda, a solution that helps insurance professionals with their daily work, is a product of this investment. GenYoda leverages MongoDB Atlas Vector Search to analyze large amounts of customer data, like policy statements, premiums, claims history, and health information. Using GenYoda, insurance professionals can quickly analyze underwriters’ reports to make informed decisions, create longitudinal health summaries, and streamline customer interactions to improve contact center efficiency. GenYoda can ingest 100,000 documents in just a few hours and respond to users’ queries in two to three seconds—a metric on par with the most widely used gen AI models. And it produces results: in one example, by using Capgemini’s solution an insurer was able to increase productivity by 15%, add new reports 25% faster (thus speeding decision-making), and reduce the manual effort of searching PDFs, increasing efficiency by 10%. Building the future of AI together So, what’s next? Honestly, I’m as curious as you are. But I’m also incredibly excited. At MongoDB, we’re active participants in the AI revolution, working to embrace the possibilities that lie ahead. The future of gen AI is bright, and I can’t wait to see what we’ll build together. To learn more about how MongoDB can accelerate your AI journey, explore the MongoDB AI Applications Program .
MongoDB Atlas Introduces Enhanced Cost Optimization Tools
MongoDB Atlas was designed with elasticity at its core and has always allowed customers to scale capacity vertically and horizontally, as required and automatically. Today, these inherent capabilities are even better and more cost-effective. At the recent MongoDB.local London, MongoDB announced several new MongoDB Atlas features that improve elasticity and help optimize costs while maintaining the performance and availability that business-critical applications demand. These include scaling each shard independently, extending storage beyond 4 TB or more , and 5X more responsive auto-scaling . Organizations and their customers are inherently dynamic, with operations, web traffic, and application usage growing unpredictably and non-linearly. For example, website traffic can spike due to a single video going viral on social media, and holidays are a frequent cause of application usage slowdowns. Traditionally, organizations have tackled this volatility by over-provisioning infrastructure, often at significant cost. Cloud adoption has improved the speed at which infrastructure can be provisioned in response to growing and volatile demand. Simultaneously, companies are focused on striking the perfect balance between performance and cost efficiency. This balance is acute in the current economic climate, where cost optimization is a top priority for Infrastructure & IT Operations (I&O) leaders. The goal is not balance between supply and demand. The goal is to meet the most profitable and mission-critical demand with the resources available. Nathan Hill, Distinguished VP Analyst, Gartner - Dec 2023 However, scaling infrastructure to meet demand without overprovisioning can be complex and costly. Organizations have often relied on manual processes (like scheduled scripts) or dedicated teams (like IT ops) to manage this challenge. MongoDB Atlas enables a more effective approach. With MongoDB Atlas, customers can manage flexible provisioning, zero-downtime scaling, and easy auto-scaling of their clusters. From October 2024, all Atlas customers with dedicated tier clusters can employ these recently announced enhancements for improved cost optimization. Granular resource provisioning MongoDB’s tens of thousands of customers have complex and diverse workloads with constantly changing requirements. Over time, workloads can grow unpredictably, requiring scaling up storage, compute, and IOPS independently and at differing granularities. Imagine a global retailer preparing for Cyber Monday, when traffic could be 512% higher than average — additional resources to serve customers are vital. Independent shard scaling enables customers running MongoDB Atlas to do this in a cost-optimal manner. Customers can independently scale the tier of individual shards in a cluster when one or more shards experience disproportionately higher traffic. For customers running workloads on sharded clusters, scaling each shard independently of all other shards is now an option (for example, only the shards serving US traffic during Thanksgiving). Customers can scale operational and analytical nodes independently in a single shard. This improves scalability and cost-optimization by providing fine-grained control to add resources to hot shards while maintaining the resources provisioned to other shards. All Atlas customers running dedicated clusters can use this feature through Terraform and the Admin API . Support for independent shard auto-scaling and configuration management via the Admin API and Terraform will be available in late 2024. Extended Storage and IOPS in Azure : MongoDB is introducing the ability to provision additional storage and IOPS on Atlas clusters running on Azure. This enables support for optimal performance without over-provisioning. Customers can create new clusters on Azure to provision additional IOPS and extended storage with 4TB or more on larger clusters (M40+). This feature is being rolled out and will be available to all Atlas clusters by late 2024. Head over to our docs page to learn more. With these updates, customers have greater flexibility and granularity in provisioning and scaling resources across their Atlas clusters on all three major cloud providers. Therefore, customers can optimize for performance and costs more effectively. More responsive auto-scaling Granular provisioning is excellent for optimizing costs while ensuring availability for an expected increase in traffic. However, what happens if a website gets 13X higher traffic or a surge in app interactions due to an unexpected social media post? Several enhancements to the algorithms and infrastructure powering MongoDB’s auto-scaling capabilities were announced in October 2024 at .local London . Cumulatively, these improve the time taken to scale and the responsiveness of MongoDB’s auto-scaling engine. Customers running dynamic workloads, particularly those with sharper peaks, will see up to 5X improvement in responsiveness. Smarter scaling decisions by Atlas will ensure that resource provisioning is optimized while maintaining high performance. This capability is available on all Atlas clusters with auto-scaling turned on, and customers should experience the benefits immediately. Industry-leading MongoDB Atlas customers like Conrad and Current use auto-scaling to automatically scale their compute capacity, storage capacity, or both without needing custom scripts, manual intervention, or third-party consulting services. Customers can set upper and lower tier limits, and Atlas will automatically scale their storage and tiers depending on their workload demands. This ensures clusters always have the optimal resources to maintain performance while optimizing costs. Take a look at how Coinbase is optimizing for both availability and cost in the volatile world of cryptocurrency with MongoDB Atlas’ help, or read our auto-scaling docs page to learn more. Optimize price and performance with MongoDB Atlas As businesses focus more on optimizing cloud infrastructure costs, the latest MongoDB Atlas enhancements— independent shard scaling, more responsive auto-scaling, and extended storage with IOPS—empower organizations to manage resources efficiently while maintaining top performance. These tools provide the flexibility and control needed to achieve cost-effective scalability. Ready to take control of your cloud costs? Sign up for a free trial today or spin up a cluster to get the performance, availability, and cost efficiency you need.
Health-Tech Startup Aktivo Labs Scales Up With MongoDB Atlas
Aktivo Labs , a pioneering health-tech startup based in Singapore, has made significant strides in the fight against chronic diseases. Aktivo Labs develops innovative preventative healthcare technology solutions that encourage healthier lifestyles. The Aktivo Score ® —the flagship product of Aktivo Labs built on MongoDB Atlas —is a simple yet powerful tool designed to guide users toward healthier living. “By collecting and analyzing data from smartphones and wearables—including physical activity, sleep patterns, and sedentary behavior—the Aktivo Score provides personalized recommendations to help users improve their health,” said Aktivo Labs CTO Jonnie Avinash at MongoDB.local Singapore in August 2024 . Aktivo Labs also works closely with insurance companies. Acting as a data processor, it helps insurers integrate some of the Aktivo Score features into their own apps to improve customer engagement. Empowering insurers with out-of-the-box apps and user journeys From the start, the Aktivo Labs engineering team chose to work on MongoDB Atlas because the platform’s document model and cloud nature provided the flexibility and scalability required to support the company’s business model. The first goal of the engineering team was to enable insurance providers to integrate Aktivo Score smoothly within their own infrastructures. The team built software development kits (SDKs) that insurers can embed in various iOS and Android apps. The SDKs enable progressive web app journeys for user experience, which insurers can then rebrand and customize as their own. Next, the Aktivo Labs team created a web portal to help companies manage their apps and monitor their performance. This required discreet direct integrations with a myriad of wearables. “When we started to deploy things with companies, we were able to replicate this architecture so we could support all kinds of configurations,” Avinash said. “We could give you dedicated clusters if the number of users that you’re expecting is big enough. If you’re not expecting too many customers, we could give you colocated or shared environments.” Finding more efficiencies, flexibility, and scalability with MongoDB Atlas “When we started off, one of our challenges was that we had a very small engineering team. A lot of the focus had to be on functionality, and the cost of tech had to be kept low,” said Avinash. Working on MongoDB Atlas allowed the Aktivo Labs team to focus on product development rather than on database management and overhead costs. As the company grew and expanded to markets across Asia, Africa, and the Middle East, another challenge arose: Aktivo Labs needed to ensure its platform could scale and handle large volumes of disparate data efficiently. MongoDB Atlas was the optimal solution because its fully managed multi-cloud platform could easily scale as the company grew. MongoDB Atlas also provided Aktivo Labs the flexibility it needed to handle the wide variety, volume, and complexity of data generated by users’ health metrics. Based on insights from the MongoDB Atlas oplog, the engineering team made proactive updates to the database in real-time in anticipation of dynamic changes to leaderboards and challenges in the app. This approach enables Aktivo Labs to manage complex data flows efficiently, ensuring that users always have access to the latest metrics about their health. MongoDB Atlas’s secondary nodes and analytics nodes provide isolated environments for intensive data processing tasks, such as calculating risk scores for diabetes and hypertension. This separation ensures that the primary user-facing applications remain responsive, even during periods of heavy data processing. These isolated environments have also been an important factor in achieving compliance with the data-anonymization requirements from health insurers. “The moment you start showing that it’s a managed service and you’re able to show a lot of these things, the amount of faith that both auditors and clients have in us is a lot more,” said Avinash. Powered by MongoDB Atlas, Aktivo Labs is now looking to expand into U.S. and European markets, pursuing its mission of preventing chronic diseases on a global scale. Visit our product page to learn more about MongoDB Atlas.
Away From the Keyboard: Rafa Liou, Senior Partner Marketing Manager
Welcome to the latest article in our “Away From the Keyboard” series, which features interviews with people at MongoDB, discussing what they do, how they prioritize time away from their work, and their advice for others looking to create a more holistic approach to coding. Rafa Liou, Senior Partner Marketing Manager at MongoDB, was gracious enough to tell us why he's not ashamed to advocate strongly for a healthy work-life balance and how his past career in the wild world of advertising helped him first recognize the need to do so. Q: What do you do at MongoDB? RAFA: I’m a Marketing Manager focused on MongoDB’s AI partner ecosystem . I help promote our partnerships with companies such as Anthropic, Cohere, LangChain, Together AI, and many others. I work to drive mutual awareness, credibility, and product adoption in the gen AI space via marketing programs. Basically telling the world why we’re better together. It’s a cool job where I’m able to wear many hats and interact with lots of different teams internally and externally. Q: What does work-life balance look like for you? RAFA: Work-life balance is really important to me. It’s actually one of the things I value the most in a job. I know some people advise against this but anytime I’m interviewing with a company I ask about it because it definitely impacts my mental health, how I spend my time outside of work, and my ability to do the things I love. I’m very fortunate to work for a company that understands that, and trusts me to do my job and, at the same time, be able to step out for a walk, a workout, not miss a dinner reservation with my husband, or whatever it is. It makes a lot of difference in both my productivity and happiness. After I log off, you can find me taking a HIIT class, exploring the restaurant scene in LA, or biking at the beach. It’s so good to be able to do all of that stress-free! Q: How do you ensure you set boundaries between work and personal life? RAFA: I usually joke that if you do everything you’re tasked with at the pace you’d like things to get done, you will never stop working. It is really important to prioritize them based on value, urgency, and feasibility. By assessing your pipeline more critically, you will be able to distill what needs to be done right now and also be at peace with the things that will be handled down the road, making it easier to disconnect when you’re done for the day. It’s also important to set expectations and boundaries with your manager and teams so you can fully enjoy life after work without worrying about that Slack message when you’re at the movies. Q: Has work/life balance always been a priority for you, or did you develop it later in your career? RAFA: Before tech, I worked in advertising, which is a very fast-paced industry with the craziest deadlines. For some time in my career, working relentlessly was not only required, but it was also rewarded by agency culture. When you’re young, nights in the office brainstorming over pizza with friends may sound fun. But it starts to wear you out pretty quickly, especially when you don’t have the time, energy, or even the mental state to enjoy your personal life after long hours. As I matured and climbed a few steps in my career, I felt the urge and empowerment to set some boundaries to protect myself. Now, it’s a non-negotiable factor for me. Q: What benefits has this balance given you in your career? RAFA: By constantly exercising prioritization, I’ve become a more efficient professional. When you focus on what really matters, you are also able to execute at higher quality, without distractions or the feeling of getting overwhelmed. Of course, with prioritization comes a lot of trade-offs and discussions with stakeholders on what should be prioritized today versus tomorrow. So, I think I’ve also gotten better at negotiation and conflict resolution (things I’ve always struggled with). Last but not least: having consistent downtime to unwind makes me more creative and energized to come up with new ideas and take on new projects. Q: What advice would you give to someone seeking to find a better balance? RAFA: First and foremost: don’t be ashamed of wanting a better work-life balance. I often find people living and breathing work just because they don’t want to be seen as lazy or uncommitted. Once you understand that a better work-life balance will actually make you a better professional—more intentional, efficient, and even strategic (as you will spend energy to solve what creates more value in a timely manner)—it will be easier to have this mindset, communicate it to others, and live by it. Something more practical would be to start a list of all the things you have to do, acknowledge you can’t finish them all by the end of the day (or week, or month), and ask yourself: Do they all carry the same importance? How can I prioritize them? What would happen if I work on X now instead of Y? I would experiment with this approach and check how you feel and how it impacts your day-to-day life. You might be surprised by the result. Making time for personal life events, hobbies, and meet-ups with family and friends will also help you have something to look forward to after closing your laptop. This is all easier said than done but I guarantee that once this becomes part of your core values and you find the balance that works for you, it is totally worth it! Thank you to Rafa Liou for sharing his insights! And thanks to all of you for reading. For past articles in this series, check out our interviews with: Senior AI Developer Advocate, Apoorva Joshi Developer Advocate Anaiya Raisinghani Interested in learning more about or connecting more with MongoDB? Join our MongoDB Community to meet other community members, hear about inspiring topics, and receive the latest MongoDB news and events. And let us know if you have any questions for our future guests when it comes to building a better work-life balance as developers. Tag us on social media: @/mongodb
Driving Neurodiversity Awareness and Education at MongoDB
Roughly 20% of the US population is neurodiverse, which means that you likely work with a colleague who learns and navigates the workplace (and the world) differently than you do. Which is a good thing! Studies have shown that hiring neurodiverse individuals benefits workplaces , with Deloitte noting that organizations “can gain a competitive edge from increased diversity in skills, ways of thinking, and approaches to problem-solving.” Config at MongoDB —which Cian and I are the global leaders of—recognizes the prevalence, importance, and power of neurodiversity in the workplace. Config’s mission is to educate both our members and the wider employee population at MongoDB about neurodiversity in the workplace, and through education to empower them to embrace—and champion—neurodiversity. Since it was founded in April 2023, Config’s membership has grown by over 150%, and it now has members in New York, Dublin, Paris, Gurugram, and Sydney. In fact, more than 200 people who span a range of MongoDB teams—from Engineering and Product, to the People team, to Marketing—take part in Config. We like to say that no one succeeds until all of us succeed. And that no one belongs until all of us belong. As managers, culture leaders, and as people, it's our responsibility to do whatever we can to make that true. Invisible differences like neurodiversity are hard to spot, but they enrich our work and our lives. Config.MDB plays an important role in helping us achieve this ambition. Making an impact on the MongoDB community Over the last year and a half, Config has held over fifteen events globally—with almost 1,000 employees in attendance. Config has held educational events for both the group’s members and the wider MongoDB audience on neurodiversity-related topics like autism awareness and ADHD awareness, along with events tailored to allies and members who identify as neurodivergent or who are part of a neurodivergent family. Config has also held training sessions for MongoDB people managers that provide them knowledge and tools to better manage neurodiverse team members. Ger Hartnett, an Engineering Lead at MongoDB said the training “gave me a much better understanding and appreciation for neurodiversity. This course was truly eye-opening for me. I learned practical ways to be more inclusive and supportive, both at work and in everyday life.” The group also holds quarterly virtual meetings to share the latest updates, personal experiences, and practical tips for members, focusing on career development, benefit entitlements, and events happening within MongoDB. Outside of events and training sessions, Config has had a broader business impact on the company, with some Config leads partnering with the employee inclusion and recruiting teams to put together an interview accommodation program. This program supports candidates who are neurodiverse or have a disability by allowing them to apply for special requests to make their interview experience more inclusive and enjoyable. Making a difference for individual members Config’s focus on educational and training events has had a dramatic and direct impact on members. The group is a safe space for neurodiverse or disabled people to share their experiences and seek advice on various issues. Cian is one of Config’s founding members, and had this to say about his personal experience: I was diagnosed with dyslexia in college and wanted to start a group like Config after speaking with other employees who were neurodiverse. We agreed that there was a need for a group like this at MongoDB. After the group was formed, I attended several events that focused on ADHD and saw a lot of similarities between traits and experiences of those with ADHD and myself. After attending these events, struggles that I had and that I thought were personality traits could be a sign of ADHD, I turned to some of our members for guidance on how to seek a diagnosis. Earlier this year, I was diagnosed with ADHD by a medical professional. I have noticed an improvement in my quality of life, and thanks to Config, I have a lot of valuable tips and resources to help me in my day-to-day. Had it not been for Config and these events I would still be none the wiser. Config has also made an impact on employees who are parents of neurodivergent children, like Sarah Lin , a senior information/content architect and Config member: I joined Config to be part of the change I want to see in the world—to help make the inclusive and supportive workplace I'd want my autistic daughter to experience. I certainly hope I'm contributing because membership has benefitted me personally. I've learned more about different types of neurodivergence and ways to support my colleagues. From our employee resource group events, I've learned more about autism and the lives of autistic adults so that I can be a better support for my daughter as we look toward her adulthood. The best part has been conversations with other parents and seeing myself reflected in their struggles, persistence, and achievements. Looking ahead As Config continues to expand its footprint within MongoDB, the group plans to introduce advanced educational programming to raise awareness for neurodiversity in the workplace. It also plans to hold workshops to foster professional development and executive functioning. Config also hopes to grow its global membership to provide community outreach at scale for nonprofit organizations that specifically service neurodiverse individuals. Ultimately, Config’s aim is to create the best environment for teams at MongoDB. Our view of success is not only the “what” but also the “how.” Being sustainable, encouraging growth through learning, and accomplishing goals as a team are all meaningful to us. And we believe strongly in the power of allyship; we want MongoDB to be a place where amazing people feel supported and are given the opportunity to do their best. After all, many of us are already close to neurodivergent individuals. One of Config’s Executive Sponsors, Mick Graham, has a daughter who is neurodivergent—which he says gives him extra inspiration to support Config now and in the future. Overall, being part of Config has raised our understanding of how neurodivergent people navigate the world. And the group—and the inspirations and experiences members have shared—contribute to making MongoDB a place that great people want to be. Interested in learning more about employee resource groups at MongoDB? Join our talent community to receive the latest MongoDB culture highlights.
Reflections On Our Recent AI "Think-A-Thon"
Interesting ideas are bound to emerge when great minds come together, so there was no shortage of interesting ideas on October 2nd, when MongoDB’s Developer Relations team hosted our second-ever AI Build Together event at MongoDB.local London. In some ways, the event is similar to a hackathon: a group of developers come together to solve a problem. But in other ways, the event is quite different. While hackathons normally take an entire day and involve intensive coding, the AI Build Together events are organized to take place over just a few hours and don't involve any coding at all. Instead, it’s all based around discussion and ideation. For these reasons, MongoDB’s Developer Relations team likes to dub them “think-a-thons.” Our first AI Build Together event was held earlier this year at .local NYC. After seeing the energy in the room and the excitement from attendees, our Developer Relations team knew it wanted to host another one. The .local London event’s fifty attendees—which included developers from numerous industries and leading AI innovators who served as mentors—came together to brainstorm and discuss AI-based solutions to common industry problems. .local London AI Build Together attendees brainstorming AI solutions for the healthcare industry The AI mentors included: Loghman Zadeh (gravity9), Ben Gutkovich (Superlinked), Jesse Martin (Hasura), Marlene Mhangami (Microsoft), Igor Alekseev (AWS), and John Willis and Patrick Debois (co-founders of DevOps). Upon arrival, participants joined a workflow group best aligned with their industry and/or area of interest—AI for Education, AI for DevOps, AI for Healthcare, AI for Optimizing Travel, AI for Supply Chain, and AI for Productivity. The AI for Productivity group collaborating on their workflow The discussions were lively, and it was amazing to see how much energy these attendees brought to their discussions. For example, the AI for Education workflow group vigorously discussed developing a personalized AI education coach to help students develop their educational plans and support them with career advice. Meanwhile, the AI for Healthcare workflow group focused on the idea of creating an AI drive tool to provide personalized healthcare to patients and real-time insights to their providers. The AI for Productivity team came up with a clever product that helps you read, digest, and identify the key aspects of long legal documents. The AI for Optimizing Travel group seeking advice from AI mentor Marlene A talented artist was also brought in to visualize each workflow group’s problem statements and potential solutions—literally and figuratively illustrating their innovative ideas. Graphic recorder Maria Foulquié putting the final touches on the illustration Final illustration documenting the 2024 MongoDB.local London AI Build Together event All in all, our second time hosting this event was deemed a success by everyone involved. “It was impressive to see how attendees, regardless of their technical background, found ways to contribute to complex AI solutions,” says Loghman Zadeh, AI Director at gravity9, who served as one of the event’s advisors. “Engaging with so many creative and forward-thinking individuals, all eager to push the boundaries of AI innovation was refreshing. The collaborative atmosphere fostered dynamic discussions and allowed participants to explore new ideas in a supportive environment.” If you’re interested in taking part in events like these—which offer a range of networking opportunities—there are three more MongoDB.local events slated for 2024—Sao Paulo, Paris, and Stockholm. Additionally, you can join your local MongoDB user group to learn from and connect with other MongoDB developers in your area.