Introducing Database Digest: Building Foundations for Success

Boris Bialek
April 15, 2025

Today at MongoDB .local Toronto, I’m excited to share the first issue of Database Digest—MongoDB’s new digital magazine that explores the critical role of data in powering modern applications. This inaugural issue explores modern data architecture, and shows how—when the right data foundation meets emerging technologies—pioneering companies are fundamentally reimagining what's possible.

The dawn of data ubiquity

Currently, we stand in what McKinsey calls the "data ubiquity era"— with data flowing through organizations as essentially as electricity powers the modern world. The transformation to this era has brought both unprecedented opportunity and formidable challenges. Organizations must simultaneously manage huge volumes of data while delivering the real-time, personalized experiences that define competitive advantage today.

Successfully doing so doesn’t mean merely adopting new technologies. Instead, it requires fundamentally rethinking how data is stored, processed, and leveraged to drive business value. Traditional relational database systems simply cannot meet these demands. The future belongs to organizations with data architectures designed for the agility, scalability, and versatility needed to handle diverse data types while seamlessly integrating with emerging technologies like AI.

The cornerstone of AI success

The rise of AI, and the speed at which the market has been changing have fundamentally shifted the importance of adaptability. However, software can only adapt as fast as its foundation allows.

At MongoDB, we believe modern databases are the cornerstone of the age of AI, providing the essential capabilities needed for success in this new era. To do so, they must be able to:

Handle all forms of data and provide intelligent search: Modern databases consolidate structured and unstructured data into a single system, eliminating silos that restrict AI innovation. They ground AI output in accurate, contextual data that drives better outcomes.
Scale without constraints and react instantly: Databases should be able to adapt to unpredictable workloads and massive data volumes without performance degradation. They should enable real-time decisions and actions when opportunities or threats emerge.
Embed domain-specific AI and secure data throughout: Modern databases enhance accuracy with specialized models that reduce hallucinations, and they protect information at every stage without sacrificing speed or functionality.

Figure 1. Modern database demands.

Graphic covering the demands for modern databases. On the left, there is a question which is Modern Database Demands: What is needed? On the right, three demands are listed out. The first states handle all forms of data and provide intelligent search. The second is scale without constraints and react instantly. Finally, the third is embed domain-specific AI and secure data throughout.

The impact of a modern database on AI innovation isn’t theoretical—we're seeing organizations like Swisscom leverage this approach to apply generative AI to their extensive expert content library, transforming how they serve the banking industry by delivering bespoke, contextual information within seconds.

The AI revolution

Perhaps nowhere is the impact of a modern data foundation more profound than in the rapid evolution of AI applications. In just a short time, we've progressed from simple LLM-powered chatbots to more advanced agentic systems capable of understanding complex goals, breaking them into manageable steps, and executing them autonomously—all while maintaining context and learning from interactions.

This represents more than incremental progress—it's a fundamental shift in how AI serves as a strategic partner in solving business challenges. MongoDB sits at the heart of this transformation, providing the critical bridge between AI models and data while enabling vector storage, real-time processing, and seamless integration with LLM orchestrators.

Companies like Questflow demonstrate the power of this approach, revolutionizing the future of work through a decentralized, autonomous AI agent network that orchestrates multiple AI agents collaborating with humans. By leveraging MongoDB's flexible document model and vector search capabilities, they're enabling startups to create dynamic AI solutions that handle everything from data analysis to content creation, while maintaining context and learning from interactions.

Modernizing legacy systems: the strategic imperative

For established enterprises, the journey to a modern data foundation often begins with addressing the legacy systems that consume up to 80% of IT budgets yet constrain innovation. Modernization isn't just a technical upgrade—it's a strategic move toward growth, efficiency, and competitive advantage.

The evidence is compelling: Bendigo and Adelaide Bank achieved a staggering 90% reduction in both time and cost modernizing core banking applications using MongoDB's repeatable modernization framework and AI-powered migration tools. This transformation isn't just about cost savings—it's about creating the foundation for entirely new capabilities that drive business value.

Modern data architecture must embody flexibility across multiple dimensions—from supporting diverse data models to providing deployment options spanning cloud-native, cloud-agnostic, and on-premise environments. This approach enables organizations to break free from silos, integrate AI capabilities, and create a unified data foundation supporting both current operations and future innovations.

What’s next for data

The organizations featured throughout Database Digest share a common vision: they recognize that tomorrow's success depends on today's data foundation. The convergence of flexible document models, advanced AI integration, and cloud-native capabilities isn't just enabling incremental improvements—it's powering applications and experiences that were never before possible.

So I invite you to explore the full, inaugural issue of Database Digest to discover how MongoDB is helping organizations across industries build the foundation for tomorrow's success. This isn't just about technology—it's about creating the foundation for transformation that delivers real business value in our increasingly data-driven world.

Visit mongodb.com/library/database-digest to download your copy and join us on this journey into the future of data.

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Now Generally Available: 7 New Resource Policies to Strengthen Atlas Security

Organizations demand for a scalable means to enforce security and governance controls across their database deployments without slowing down developer productivity. To address this, MongoDB introduced resource policies in public preview on February 10th, 2025. Resource policies enable organization administrators to set up automated, organization-wide ‘guardrails’ for their MongoDB Atlas deployments. At public preview, three policies were released to this end. Today, MongoDB is announcing the general availability of resource policies in MongoDB Atlas. This release introduces seven additional policies and a new graphical user interface (GUI) for creating and managing policies. These enhancements give organizations greater control over MongoDB Atlas configurations, simplifying security and compliance automation. How resource policies enable secure innovation Innovation is essential for organizations to maintain competitiveness in a rapidly evolving global landscape. Companies with higher levels of innovation outperformed their peers financially, according to a Cornell University study analyzing S&P 500 companies between 1998 and 2023 1 . One of the most effective ways to drive innovation is by equipping developers with the right tools and giving them the autonomy to put them into action 2 . However, without standardized controls governing those tools, developers can inadvertently configure Atlas clusters to deviate from corporate or regulatory best practices. Manual approval processes for every new project create delays. Concurrently, platform teams struggle to enforce consistent security policies across the organization, leading to increased complexity and costs. As cybersecurity threats evolve daily and regulations tighten, granting developers autonomy and quickly provisioning access to essential tools can introduce risks. Organizations must implement strong security measures to maintain compliance and enable secure innovation. Resource policies empower organizations to enforce security and compliance standards across their entire Atlas environment. Instead of targeting specific user groups, these policies establish organization-wide guardrails to govern how Atlas can be configured. This reduces the risk of misconfigurations and security gaps. With resource policies, security and compliance standards are applied automatically across all Atlas projects and clusters. This eliminates the need for manual approvals. Developers gain self-service access to the resources they need while remaining within approved organizational boundaries. Simultaneously, platform teams can centrally manage resource policies to ensure consistency and free up time for strategic initiatives. Resource policies strengthen security, streamline operations, and help accelerate innovation by automating guardrails and simplifying governance. Organizations can scale securely while empowering developers to move faster without compromising compliance. What resource policies are available? table, th, td { border: 1px solid black; } Policy Type Description Available Since Restrict cloud provider Ensure clusters are only deployed on approved cloud providers (AWS, Azure, or Google Cloud). This prevents accidental or unauthorized deployments in unapproved environments. This supports organizations in meeting regulatory or business requirements. Public preview

April 14, 2025

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Reimagining Legacy Systems with AI: Why We're Building the Future

This article was adapted from an interview with Galileo’s Chain of Thought podcast. Watch the full episode on YouTube. At MongoDB, we talk a lot about what it means to be at an inflection point—a moment where you can either maintain the status quo or redefine what's possible. For my team and for software engineers around the world, that inflection point is here. Now. Large language models (LLMs), agents, and now model context protocol (MCP) are fundamentally changing not just how we work, but what is possible with technology. So we must adapt; we have to build differently, and be faster. At MongoDB, we're creating something unique. Something smarter. We’re embracing and exploring everything that AI can do for developers. And we’re inviting the next generation of engineers to join us in shaping it. The hidden cost of legacy systems If you've spent any time in enterprise engineering, you know the challenge many organizations face: decades-old systems that are critical to a company’s operations but are held together with duct tape and wishful thinking. The developers who built them have long since moved on. The documentation is missing, outdated, or was never written. The last update was six years ago; some of the dependencies are abandoned. Whether you call it “tech debt” or “care and feeding” or “maintenance”, it consumes a huge fraction of our time and engineering budget—without a clear path forward. This is the reality many companies face—whether they’re in finance , healthcare, or the public sector—they have no choice but to pour millions of dollars into just keeping these systems afloat. Or do they? At MongoDB, we're building a new kind of engineering capability—one that combines the latest advancements in generative AI with the principles of forward-deployed engineering to help modernize legacy systems at a speed and scale that feels impossible. What is forward-deployed AI engineering? Because it’s new, you may not have heard of this role before. It's a bridge between engineering, consulting, and product development. As an AI Forward Deployed Engineer, you won’t just sit behind the scenes writing code. You'll be embedded with our customers, working side by side with their teams to solve real-world modernization challenges. No theory, just hands-on engineering at the sharpest edge of AI innovation. Your goal? Write software with AI, write software for AI, at speeds you’ve never experienced. In some tasks, we have benchmarked AI as being more than eight hundred times faster than a human being… working in that environment is, I assure you, radically different. You’ll deliver immediate, meaningful impact—whether that’s untangling a million-line code base, modernizing outdated Java frameworks, or helping teams migrate from niche, unsupported languages to modern tech stacks. MongoDB + AI: Changing the game One of the most exciting parts of our work is how AI is fundamentally changing what's possible. In the past, a modernization project like this might take five years and dozens of engineers. Today, with the power of MongoDB and AI, that same work is done in a fraction of the time—sometimes in months, sometimes in weeks—with a much smaller, highly focused team. We use LLMs and other AI tools to do things like: Add missing documentation to legacy code Write unit tests where none existed Remove outdated frameworks, replacing them with others Analyze and map massive, messy code bases Move between programming languages, frameworks, platforms, ORMs, databases, and front-end technologies. Let me be clear—none of this is easy, or on autopilot. We augment great engineers with powerful tools so they can focus on the work that matters most, so they can think big, and go far . Combining human expertise with AI capability leads to outcomes no one thought possible. Why it matters Modernizing these systems isn’t just about efficiency or cost savings—it’s mission-critical. The legacy platforms power trading systems, healthcare infrastructure, and governmental services that support real people every day. In multiple countries around the world. Being able to show them a fully functional prototype in weeks, instead of years, is game-changing. It proves what's possible. It builds momentum. It allows them to rethink how they, too, can structure teams and processes in a world where technology moves faster than ever. Why join MongoDB? If you're an engineer who thrives on autonomy, problem-solving, and building real solutions with immediate impact—and you want to work with AI every day in novel and complex ways— this is your chance. We’re looking for folks from a variety of backgrounds—software development, consulting, product engineering, or technical architecture—who are passionate about learning and applying new technologies, skilled communicators who are excited to partner with customers, and who are comfortable operating in ambiguity. As part of our Application Modernization function, you will: Work directly with customers and stakeholders Rapidly build solutions that solve meaningful business challenges Operate at the intersection of engineering, product, and consulting Learn and apply cutting-edge AI, inventing both new methodologies and technologies Be part of a small, fast-moving, high-impact team You might even write a white paper or two. You’ll also be part of a culture that values leadership at every level. At MongoDB, we believe in making it matter —and this role is designed to make change, not just for our customers, but for the software industry as a whole. If you’ve ever wanted to be part of a startup within an enterprise, this is your opportunity. We're building a new way of working — one where experimentation, agility, and ownership are at the core. Help us build what’s next At MongoDB, we’re not just modernizing applications—we’re modernizing how software is made . If that excites you, if you want to shape the future of software development alongside a team of builders, thinkers, and innovators—we’d love to hear from you. Check out our open roles and join us in redefining what's possible.

April 23, 2025