MongoDB: Powering Digital Natives

Sebastian Rojas Arbulu
November 7, 2024 | Updated: November 8, 2024

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:

  1. 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.

  2. 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.

  3. 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.

  4. 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.
Image with a bunch of company logos that represent tools and process to maximize velocity. The companies/tools included are: AWS, Azure, Kubernetes, Google Cloud, Dev Ops, Kotlin, GitHub Copilot, Datadog, Agile, Adobe, Kafka, GitHub, Hashicorp Terraform, Vercel, GiLab, Node JS

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.
Framework displaying the evolving tech landscape. On the left is a flow with 4 components, it starts with Gen AI, goes to proprietary data, then to LLMs, and ends with integration of a new vector database. On the right side is the same flow set-up, starting with event-driven, which goes to real-time, then customer experience, and ends with rethinking data querying, processing, and analysis.

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
Architecture diagram displaying data sprawl and manageability issues.

It compromises every single one of the four outcomes your technology foundation should be providing, yielding the opposite results:

  1. 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.

  2. 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.

  3. 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.

  4. 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
Diagram displaying the unified developer experience. The diagram starts at the top with a box titled your applications. This then flows down to the next level titled the MongoDB developer data platform, which has boxes for OLTP, time series, full-text search, real-time analytics, stream processing, and vector search. Finally, the bottom of the diagram is titled your data, and comprises of different components for storing data, such as secure, multi-cloud, elastic, and then has the 3 major cloud providers: AWS, Azure, GCP.

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:

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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 .

November 4, 2024
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Hanabi Technologies Uses MongoDB to Power AI Assistant, Hana

For all the hype surrounding generative AI, cynics tend to view the few real-world implementations as little more than “fancy chatbots.” But for Abhinav Aggarwal, CEO of Hanabi Technologies, the idea of a generative AI-powered bot that is more than just an assistant was intriguing. “I’d been using ChatGPT since it launched,” said Aggarwal. “That got me thinking: How could we make a chatbot that was like a team member?” And with that concept, Hana was born. The problem with bots “Most generative AI chatbots do not act like people; they wait for a command and give a response,” said Aggarwal. “We wanted to create a human-like chatbot that would proactively help people based on what they wanted—automating reminders, for example, or fetching time zones from your calendar to correctly schedule meetings.” Hanabi’s flagship product, Hana, is an AI assistant designed to enhance team collaboration within Google Chat, working in concert with Google Workspace and its suite of products. “Our target customers are smaller companies of between 10 and 50 people. At this size you’re not going to build your own agent from scratch,” he said. Hana integrates with Google APIs to deliver a human-like assistant that chimes in with helpful interventions, such as automatically setting reminders and making sure meetings are booked in the right time zone for each participant. “Hana is designed to bring AI to smaller companies and help them collaborate in a space where they are already working—Google Workspace,” Aggarwal explained. The MongoDB Atlas solution For Hana to act like a member of the team, Hanabi needed to process massive amounts of data to support advanced features like retrieval-augmented generation (RAG) for better information retrieval across Google Docs and many other sources. And with a rapidly growing user base of over 600 organizations and 17,000+ installs, Hanabi also required a secure, scalable, and high-performing data storage solution. MongoDB Atlas provided a flexible document model, built-in vector database, and scalable cloud-based infrastructure, freeing Hanabi engineers to build new features for Hana rather than focusing on rote tasks like data extract, transform, and load processes or manual scaling and provisioning. Now, MongoDB Atlas handles a variety of responsibilities: Scalability and security: MongoDB Atlas’s auto-scaling and automatic backup features have enabled Hanabi to seamlessly grow its user base without the need for manual database management. RAG: MongoDB Atlas plays a critical role in Hana’s RAG functionality. The platform enables Hanabi to split Google Docs into small sections, create embeddings, and store these sections in Atlas’s vector database. Development Processes: According to Aggarwal, MongoDB’s flexibility in managing changing schemas has been essential to the company’s fast-paced development cycle. Data Visualization: Using MongoDB Atlas Charts has enabled Hanabi to create comprehensive dashboards for real-time data visualization. This has helped the team track usage, set reminders, and optimize performance without needing to build a manual dashboard. Impact and results With MongoDB Atlas, Hanabi can successfully scale Hana to meet the demands of its rapidly expanding user base. The integration is also enabling Hana to offer powerful features like automatic interactions with customers, advanced information retrieval from Google Docs, and manually added memory snippets, making it an essential tool for teams around the world. Next steps Hanabi plans to continue integrating more tools into Hana while expanding its reach to personal Gmail users. The company is also rolling out a new automatic-interaction feature, further enhancing Hana’s ability to proactively assist users without direct commands. MongoDB Atlas remains a key component of Hanabi’s stack, alongside Google Kubernetes Engine, NestJS, and LangChain, enabling Hanabi to focus on innovating to improve the customer experience. Tech Stack MongoDB Atlas Google Kubernetes Engine NestJS LangChain Are you building AI apps? Join the MongoDB AI Innovators Program today! Successful participants gain access to free MongoDB Atlas credits, technical enablement, and invaluable connections within the broader AI ecosystem. If your company is interested in being featured, we’d love to hear from you. Connect with us at ai_adopters@mongodb.com.

November 21, 2024