Building Gen AI with MongoDB & AI Partners | October 2024

Gregory Maxson
November 11, 2024 | Updated: November 20, 2024
#genAI

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.

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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

November 7, 2024

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Staff Engineering at MongoDB: Your Path to Making Broad Impact

Andrew Whitaker is a Senior Staff Engineer at MongoDB. His previous experience spans tiny startups to enormous organizations like AWS, where he held several different roles focusing on databases. Before joining MongoDB, he worked at a startup building optimized machine learning models in the cloud. Read on to learn more about why Andrew decided to join MongoDB in a senior-level engineering role and how his work is driving improvement within our engineering organization. Why MongoDB I have long been a fan of MongoDB’s products and services. MongoDB the database has always been a pleasure to work with – the system “brings joy” to quote a phrase. As a Python developer, I appreciate how the Python driver feels “Pythonic” in a completely natural way. The programmer interacts with the database using Python constructs: dictionaries, lists, and primitive types. By contrast, SQL databases force me to change my mental model, and the query language feels like an add-on that does not blend with the core language. As an engineer, I am always looking to expand my knowledge and grow my skills. The scope of challenges engineers face at MongoDB is what triggered my interest in the company. We obviously have people working on core databases and distributed systems. But, we also have teams dedicated to machine learning, streaming data, analytics, networking, developer tooling, drivers, and many more areas. It is very hard to get bored working at MongoDB. Finally, I would be remiss if I did not mention the people. Overall, MongoDB’s engineering culture prioritizes intelligence, low ego, and an ability to get stuff done. CL/CI (Continuous Learning, Continuous Improvement) Working at MongoDB has provided me with opportunities for continued learning and growth. Though I do not program as much as I did earlier in my career, I have recently been exploring the Rust language. I’m excited by Rust because it avoids the tradeoffs between predictable performance and safety. My work in the search space has given me exposure to the fast moving world of AI: vector embeddings, RAG, etc. For various reasons, I think MongoDB is uniquely positioned to do well in this area. On top of this, I’m working on some initiatives that are not fully public. I can say that one focus area is improving the sharding experience for our customers. We believe MongoDB sharding is best-in-breed. Still, the process requires more manual configuration than we think is ideal: customers select the shard key, cluster type, shard count, etc. We give guidance here, but I think we can raise the bar in terms of offering a seamless experience with less “futz”. I’m also working with the search team. We believe there is a natural affinity between MongoDB’s document model and AI/ML workloads. We have some features in the works that extend this integration in new and interesting ways. I also spend a fair bit of time driving quality improvements across our suite of products. Our CTO Jim Scharf frequently refers to our “ big 4 ” goals: security, durability, availability, and performance. These goals are more important than any feature we build. I’ve been working across the company to help teams define their availability SLO/SLAs. It turns out that measuring availability is a subtle topic. For example, a naive approach of counting the percentage of failed requests can underestimate downtime because customers make fewer requests when a service is unavailable. So, the first step is to clarify the definition of availability. Finally, as a lapsed academic (in a distant life, I was a graduate student at the University of Washington Department of Computer Science and Engineering), I’m always interested in finding ways to bridge theory and practice. I’ve been collaborating with some folks in our research team to drive improvements to our replication protocols. There are theoretical results that suggest it is impossible to simultaneously achieve low latency and strong consistency (“linearizability” in the technical jargon). However, we believe there are intermediate points in the consistency/latency spectrum that have not been fully explored. This work hasn't been made into a product yet, but stay tuned. Flexible working MongoDB is a hybrid company. Like many of our engineers, I work outside the company headquarters in New York City (I live in Seattle). I appreciate MongoDB’s approach to hybrid working and that company leadership, starting with Dev , cares about the well-being of their employees. It seems there are companies that don’t seem to trust their employees to make decisions, such as which days to come into the office, so I’m thankful for the autonomy I receive at MongoDB to work in a way that’s best for me. Remote work has its challenges, but I would say that the benefit for my work/life balance has been transformative. Final thoughts I have found MongoDB engineers demonstrate a strong mix of technical depth, pragmatism, and empathy. I have yet to find the “smart jerk” prototype that seems to exist throughout the tech industry. Overall, I have found MongoDB is open to change and growth at both the team level and the individual level. There is a willingness to evolve and improve that aligns with the company’s values and leadership principles and enables the success of our technology and people. Find out more about MongoDB culture and career opportunities by joining our talent community .

November 20, 2024