The Learning Experience: Celebrating a Year of MongoDB Developer Days

One year ago today MongoDB held our first regional Developer Day event, a full-day experience designed to teach the fundamentals and advanced capabilities of MongoDB. Developer Day events have been held in person in over 35 cities, across 16 countries, and in seven languages. Initially created by a core team with a passion for enabling developers, a group of talented MongoDB Developer Advocates and Solution Architects have since scaled the program to directly engage and teach thousands of developers worldwide.

As developer relations programs go, engaging builders through hands-on workshops is hardly a new approach. But the difference at MongoDB is how closely we collaborate cross-functionally with other teams, and how focused we are on providing authentically hands-on, engaging experiences for developers.

From the start, the MongoDB Developer Day program was a collaborative effort to take a platform that is easy to get started with and to introduce more advanced capabilities in a compelling way. Based on the helpful and positive feedback we continue to get from participants, we know Developer Days help developers gain skills and a sense of accomplishment as they alternate between instruction and applying that learning through hands-on exercises.

To share more about what has made Developer Days so successful, I asked members of that core team—Lead Developer Advocate Joel Lord, and Sr Developer Advocates Mira Vlaeva and Diego Freniche Brito—to reflect on key areas that went into creating this enduring experience.

Photograph of the Developer Day class at MongoDB.local New York City. There are roughly 70 people in the photo standing in front of the presentation stage.
Our Developer Day class at MongoDB.local NYC in May, 2024

A sense of accomplishment

“We all agreed that a Developer Day should focus on hands-on learning, where developers can experiment with MongoDB, potentially make mistakes, and take pride in building something on their own,” Mira said. “The goal was to build a fun learning experience rather than just sitting and listening to lectures all day.”

The curriculum was designed to encourage developers to work together at certain points as they advance from data modeling and schema design concepts to implementing powerful Atlas Search and Vector Search capabilities. “One of my favorite moments of the day is when people start working together,” Joel said. “At the beginning of the day, our attendees can be a little hesitant, but they quickly begin to collaborate with each other, and it's wonderful to witness that happen.”

Building great Developer Days together

While our MongoDB Developer Relations team designed the agenda and course material, it was our partnerships with other teams and stakeholders that helped Developer Days take flight. There were many key stakeholders that shared a vision for enabling developers to realize more value with our platform.

As Joel remembers, “We had to work and collaborate with a number of other teams, which was, at the time, new to us.” Among the key teams involved, Diego added, “Working with Field and Strategic Marketing teams has been a great experience. They help us so much with all the really important tasks… there's so much they've done that Developer Days wouldn't be a reality without them.”

The program has expanded our collaboration with several other teams, including marketing, product, and sales, to ensure our courses remain up-to-date and we make the most of our time in each city by welcoming developers from key accounts.

Continued success and improvements

To ensure Developer Days were as impactful as possible, we initially ran the program as a pilot in seven cities. In addition to noting live observations and interactions, we used surveys to collect feedback and report on an NPS (net promoter score) to assess whether the event exceeded participant expectations. These initial events were spaced out enough that the team could implement improvements and try new approaches at subsequent Developer Days.

“We had the opportunity to run the same labs multiple times, make small changes each time, and observe how people react to the different configurations,” said Mira, who continues to contribute improvements such as new interactive elements.

As we continue to bring the Developer Day experience to new cities, we’re also taking Developer Days online. “There are so many reasons why people may not be able to attend our in-person events,” said Lauren Schaefer, who recently rejoined MongoDB Developer Relations to lead the program forward. “I look forward to working with my team to tackle the challenges of bringing our curriculum successfully online.”

So a year later, I want to say thank you to everyone who has made Developer Days a success—from seven staff members who supported our first event in Chicago, to the roughly 100 talented people across MongoDB who are now part of the program. Even more importantly, I’m thankful for all of the participants (across 35 great cities and 16 countries!) that joined us for this full-day experience. As I love to say at the beginning of every Developer Day, we’re here to learn from each other. I hope you’ve learned as much from us as we have from you!

To learn more about MongoDB’s global community of millions of developers—and to check out upcoming events like Developer Days—please visit our events page.

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The Dual Journey: Healthcare Interoperability and Modernization

Interoperability in healthcare isn’t just a buzzword; it’s a fundamental necessity. It refers to the ability of IT systems to enable the timely and secure access, integration, and use of electronic health data. However, integrating data across different applications is a pressing challenge, with 48% of US hospitals reporting a one-sided sharing relationship in which they share patient data with other providers who do not, in turn, share patient data with the hospital. The ability to share electronic health data seamlessly across various healthcare systems can revolutionize patient care, enhance operational efficiency, and drive innovation. In this post, we’ll explore the challenges of healthcare data sharing, the role of interoperability , and how MongoDB can be a game-changer in this landscape. The challenge of data sharing in healthcare Today's consumers have high expectations for accessing information, and many now anticipate quick and continuous access to their health and care records. One of the biggest IT challenges faced by healthcare organizations is sharing data effectively and creating seamless data integrations to build patient-centric healthcare solutions. Healthcare data has to be shared in multiple ways: Between internal applications to ensure seamless data flow across various internal systems. Between primary and secondary care , to coordinate care across healthcare providers. To patient portals and telemedicine to enhance patient engagement and remote care. To payers, institutions, and patients themselves , to streamline interactions with insurance companies and regulatory bodies. To R&D units to accelerate medical research and pharmaceutical developments. The complexity of healthcare data is staggering, and hospitals regularly need to integrate dozens of different applications—all of which means that there are significant barriers to healthcare data sharing and integration. A vision for patient-centric healthcare Imagine a world where patient data is shared in real-time with all relevant parties—doctors, hospitals, labs, pharmacies, and insurance companies. This level of interoperability would streamline the flow of information, reduce errors, and improve patient outcomes. Achieving this, however, is no easy feat as healthcare data is immensely complex, involving various types of data such as unstructured clinical notes, lab tests, medical images, medical devices, and even genomic data. Furthermore, types of data mean different things depending on where and where it was collected. Achieving seamless data sharing also involves overcoming barriers to data sharing between different healthcare providers and systems, all while adapting to evolving regulations and standards. Watch the " MongoDB and FHIR: Navigating Healthcare Data " session from MongoDB.local NYC on YouTube. The intersection of modernization and interoperability Modernization of healthcare IT systems and achieving interoperability are two sides of the same coin. Both require significant investments and a focus on transitioning from application-driven to data-driven architecture. By focusing first on data and then connecting applications with a developer data platform like MongoDB Atlas , healthcare organizations can avoid data silos and achieve vendor-neutral data ownership. As healthcare interoperability standards define a common language, organizations might question whether, instead of reinventing the wheel with their own data domains, they can use the interoperability journey (and its high investments) to modernize their applications. MongoDB’s document data model supports the JSON format, just like FHIR (Fast Healthcare Interoperability Resources) and other interoperability standards, making it a more efficient and flexible data platform for developing healthcare applications beyond the limitations of external APIs. FHIR for storing healthcare data? The most implemented standard worldwide, HL7 FHIR , treats each piece of healthcare data as a self-contained resource, which can be accessed through unique URLs on FHIR servers, similar to how web pages are accessed on the internet. FHIR adopted a pragmatic approach for which there was no need to define a complete and unique set of resources for all the clinical data, but trying to get what most of the electronic health records (EHR) share. That is the 80% / 20% approach. For the 20% of non-standardized data, they created the famous FHIR Extensions , to extend every resource to the specific needs. However, FHIR is not yet fully developed, with only 15 of the 158 resources it defines having reached the highest level of maturity. The constant changes can be as simple as a name change or can be so complex that data has to be rearranged. FHIR is designed for exchange of data, however, as questioned above, it might be thought to be used also for persistence. In this post from Alastair Allen, he discusses the problems that could arise when using FHIR for persistence depending on the use case, and this chart aids in the decision-making. Figure 1: Using FHIR for persistence depending on the complexity of the use case For specific applications with no complex data, such as an Enterprise Master Patient Index (EMPI) to match and unify patient demographics across applications or to integrate data from wearables, leveraging FHIR for persistence is a good approach. However, building a primary operational repository for broader applications like a Patient Summary or even a Healthcare Information System (HIS) presents a significant challenge. This is because the data model these applications require for a specific organization goes beyond the capabilities of FHIR, and solving the bias through FHIR extensions makes this an inefficient approach. OpenEHR as an alternative approach In Catalonia, Spain—for about 8 million people and roughly 60 public hospitals—there are 29 different hospital EHR systems. This fact converts Catalonia to a long-experienced actor in healthcare interoperability. Each hospital maintains a team of developers exclusively focused on building interoperability interfaces. This is not a one-time cost, but it is a fact that there will be an increasing demand for data sharing. This will never end, and the costs are increasingly higher. In Catalonia, as well as in other European regions, some thought about changing the paradigm. Rather than implementing interoperability interfaces, why not create new applications that are implicitly interoperable? This is what openEHR proposes, defining the clinical information model from the maximal perspective and developing applications that consume a subset of the clinical information system. This is what the openEHR fans relate to as “Interoperability.” It’s not a protocol but an open architecture. However, while FHIR is very versatile—offering data models for administrative and operational data efficiently—openEHR focuses exclusively on clinical data. So while a combination of FHIR and openEHR can solve part of the problem, future healthcare applications need to integrate a wide variety of data, including medical images, genomics, proteomics, and data from complex medical devices—which could be complicated by the lack of a single standard. Overcoming this challenge with the document model and MongoDB Now, let’s discover the power of the document model to advance interoperability while modernizing systems. MongoDB Atlas features a flexible document data model, which provides a flexible way of storing and organizing healthcare data using JSON-like documents. With a flexible data schema, healthcare organizations can accommodate any data structure, format, or source into one platform, providing seamless third-party integration capabilities necessary for interoperability. While different use cases will have different solutions, the flexibility of the document model means MongoDB is able to adapt to changes. Figure 2 below shows a database modeled in MongoDB, where each collection stores a FHIR resource type (e.g., patients, encounters, conditions). These documents mirror the FHIR objects, conserving its complex hierarchy. Let's imagine our application requires specific fields not supported by FHIR, and there is no need for an FHIR extension because it won’t be shared externally. We can add a metadata field containing all this information that is as flexible as needed. It can be used to track the standard evolution of the resource, the version of the document itself, tenant ID for multi-tenant applications, and more. Figure 2: Data modeled in MongoDB Another possibility is to add the searchable fields of the resource as key-value pairs so that you can retrieve data with a single index. We can maintain the indexes by automating this with the FHIR search parameters. In a single repository, we can combine FHIR data with custom application data. Additionally, data in other protocols, such as DICOM metadata and OMOP, can be integrated, providing unparalleled flexibility in accessing your data. This setup permits access through different endpoints within one repository. Using MQL (the MongoDB Query Language), you can build FHIR APIs or use the MongoDB SQL interface to provide SQL querying capabilities to connect your preferred business intelligence tools. Figure 3: Unified and flexible data store and flexible data retrieval MongoDB’s developer data platform At the center of MongoDB’s developer data platform is MongoDB Atlas, the most advanced cloud database service on the market. It provides integrated full-text search capabilities, allowing applications to perform when making complex queries without the need to maintain a separate system. With all the possibilities of generative AI, multi-dimensional vectors to represent the meaning of the data are becoming a necessity for all vendors and organizations. MongoDB’s capabilities of storing vectors alongside the operational data while providing the vector search aggregation stage functionality, enables fast data retrieval with the possibility of pre-filtering your data with any of the data existing in your operational database. This is executed in a single query, deriving in a best-of-class performance operation. Figure 4: Vector embeddings These capabilities transform a single database into a unique, powerful, and easy-to-use interface capable of handling diverse use cases without the need for any single-purpose databases. Solving the dual challenge with MongoDB Achieving interoperability and modernization in healthcare IT are challenging but essential. MongoDB provides a powerful platform that meets organizations’ modern data management needs. By embracing MongoDB, healthcare organizations can unlock the full potential of their data, leading to improved patient outcomes and operational efficiency. Figure 5: Closing the gap between interoperability and modernization journey with MongoDB Refactoring applications to incorporate interoperability resources as part of documents—and extending them with all the requirements for your modern needs—will ensure organizations’ data layers remain robust and adaptable. By doing so, organizations can create a flexible architecture that can seamlessly integrate diverse data types and accommodate future advancements. This approach not only enhances data accessibility and simplifies data management but also supports compliance with evolving standards and regulations. Furthermore, it enables real-time data analytics and insights, fostering innovation and driving better decision-making. Ultimately, this strategy positions healthcare organizations to effectively manage and leverage their data, leading to improved patient outcomes and operational efficiencies. For more detailed information and resources on how MongoDB can transform organizations’ healthcare IT systems, we encourage you to apply for an exclusive innovation workshop with MongoDB's industry experts to explore bespoke modern app development and tailored solutions for your organization. Additionally, check out these resources: MongoDB and FHIR: Navigating Healthcare Data with MongoDB How Leading Industries are Transforming with AI and MongoDB Atlas The MongoDB Solutions Library is curated with tailored solutions to help developers kick-start their projects For developers: From FHIR Synthea data to MongoDB

August 28, 2024
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MongoDB’s 2024 Year in Review

It’s hard to believe that another year is almost over! 2024 was a transformative year for MongoDB, and it was marked by both innovation and releases that further our commitment to empowering customers, developers, and partners worldwide. So without further ado, let’s dive into MongoDB’s 2024 highlights. We’ll also share our executive team’s predictions of what 2025 might have in store. A look back at 2024 MongoDB 8.0: The most performant version of MongoDB ever In October we released MongoDB 8.0 , the fastest, most resilient, secure, and reliable version of MongoDB yet. Architectural optimizations in MongoDB 8.0 have significantly improved the database’s performance, with 36% faster reads and 59% higher throughput for updates. Our new architecture also makes horizontal scaling cheaper and faster. Finally, working with encrypted data is easier than ever, thanks to the addition of range queries in Queryable Encryption (which allows customers to encrypt, store, and perform queries directly on data). Whether you’re a startup building your first app, or you’re a global enterprise managing mission-critical workloads, MongoDB 8.0 offers unmatched power and flexibility, solidifying MongoDB’s place as the world’s most popular document database. Learn more about what makes 8.0 the best version of MongoDB ever on the MongoDB 8.0 page . Delivering customer value with the MongoDB AI Applications Program AI applications have become a cornerstone of modern software, and MongoDB is committed to equipping customers with the technology, tools, and support they need to succeed on their AI journey. That’s why we launched the MongoDB AI Applications Program (MAAP) in 2024, a comprehensive program designed to accelerate the development of AI applications. By offering customers resources like access to AI specialists, an ecosystem of leading AI and tech companies, and AI architectural best practices supported by integrated services, MAAP helps solve customers’ most pressing business challenges, unlocks competitive advantages, and accelerates time to value for AI investments. Overall, MAAP’s aim is to set customers on the path to AI success. Visit the MongoDB AI Applications Program page or watch our session from AWS re:Invent to learn more! Advancing AI with MongoDB Atlas Vector Search In 2024, MongoDB further cemented its role in the AI space with enhancements to MongoDB Atlas Vector Search . Recognized in 2024 (for the second consecutive year!) as one of the most loved vector databases , MongoDB continues to provide a scalable, unified, and secure platform for building cutting-edge AI use cases. Recent advancements like vector quantization in Atlas Vector Search help deliver even more value to our customers, enabling them to scale applications to billions of vectors at a lower cost. Head over to our Atlas Vector Search quick start guide to get started with Atlas Vector Search today, or visit our AI resources hub to learn more about how MongoDB can power AI applications. Search Nodes: Performance at scale Search functionality is indispensable in modern applications, and with Atlas Search Nodes, organizations can now optimize their search workloads like never before. By providing dedicated infrastructure for Atlas Search and Vector Search workloads, Search Nodes ensure high performance (e.g., a 40–60% decrease in query times), scalability, and reliability, even for the most demanding use cases. As of this year , Search Nodes are generally available across AWS, Google Cloud, and Microsoft Azure. This milestone underscores MongoDB’s commitment to delivering powerful solutions that scale alongside our customers’ needs. To learn more about Search Nodes, check out our documentation or watch our tutorial . Looking ahead: MongoDB’s 2025 predictions After the excitement of the past few years, 2025 will be defined by ensuring that technology investments deliver tangible value. Organizations remain excited about the potential AI and emerging technologies hold to solve real business challenges, but are increasingly focused on maintaining a return on investment. “Enterprises need to innovate faster than ever, but speed is no longer the only measure of success. Increasingly, organizations are laser-focused on ensuring that their technology investments directly address critical business challenges and provide clear ROI and competitive advantage—whether it’s optimizing supply chains, delivering hyper-personalized customer experiences, or scaling operations efficiently,” said Sahir Azam, Chief Product Officer at MongoDB. “In 2025, I expect to see organizations make significant strides in driving this innovation and efficiency by applying AI to more production use cases and by maturing the way they leverage their data to build compelling and differentiated customer experiences.” Indeed, we expect to see organizations make more strategic investments in emerging technologies like gen AI—innovating with a sharp focus on solving business challenges. “In 2025, we can expect the focus to shift from ‘what AI can do’ to ‘what AI should do,’ moving beyond the hype to a clearer understanding of where AI can provide real value and where human judgment is still irreplaceable,” said Tara Hernandez, VP of Developer Productivity at MongoDB. “As we advance, I think we’ll see organizations begin to adopt more selective, careful applications of AI, particularly in areas where stakes are high, such as healthcare, finance, and public safety. A refined approach to AI development will be essential—not only for producing quality results but also to build trust, ensuring these tools genuinely support human goals rather than undermining them.” With more capable, accessible application development tools and customer-focused programs like MAAP at developers’ fingertips, 2025 is an opportunity to make a data-driven impact faster than ever before. "Right now, organizations have an opportunity to leverage their data to reimagine how they do business, to more effectively adapt to a changing world, and to revolutionize our quality of life,” said Andrew Davidson, SVP of Products at MongoDB. “By harnessing our latest technologies, developers can build a foundation for a transformative future." Head over to our updates page to learn more about the new releases and updates from MongoDB in 2024. Keep an eye on our events page to learn what's to come from MongoDB in 2025!

December 19, 2024