MongoDB highlights from AWS re:Invent 2022

William To
December 13, 2022 | Updated: December 7, 2023
#AWS re:Invent

Since its inception a decade ago, AWS re:Invent has become one of the preeminent conferences for the global cloud community — and a venue for inspiration, exploration, and innovation.

This year, MongoDB attended and hosted talks, workshops, and sessions; met with customers and partners; and connected with developers and potential and current customers in the expo hall.

MongoDB is the AWS Marketplace Partner of the Year - EMEA

MongoDB was awarded the AWS Marketplace Partner of the Year for the Europe, Middle East, and Africa (EMEA) region. Since 2021, MongoDB Atlas on AWS has grown by 173 percent among EMEA users, a result of a deeper collaboration with AWS and increased attention to the customer experience. For instance, we’ve simplified the purchase and pricing of MongoDB on AWS; partnered with AWS Marketplace Vendor Insights for increased security, compliance, and confidence; helped customers accelerate their migrations to MongoDB on AWS; and more.

For more details, read “MongoDB and AWS: How a decade-old collaboration got even better in 2022.”

MongoDB customers were everywhere at AWS re:Invent

Many MongoDB customers trust AWS for their cloud computing needs, making AWS re:Invent an ideal opportunity to better understand customer needs and use cases, strengthen relationships, and plan for the new year. We were happy to see how many MongoDB customers were mentioned in AWS CEO Adam Selipsky’s keynote address. In between debuting new AWS features and capabilities, Selipsky also mentioned many AWS and MongoDB customers, including Intuit, Okta, Palo Alto Networks, Expedia, and Epic Games. In fact, two-thirds of the brands mentioned across all four keynotes use and trust MongoDB.

Additionally, joint MongoDB and AWS partners Vercel and BigID, as well as customers TEG/Ticketek and Midland Credit Management, were featured on the Voice of the Customer series. The videos should be posted on the Amazon Partner Network channel soon.

MongoDB customer and partner live streams from re:Invent

From fashion startups to telecommunications providers, MongoDB customers span a wide range of industries, sizes, and business models. To help them share their diverse experiences, we also live-streamed conversations with leaders from four innovative MongoDB customers and partners: Okta/Auth0, VEERUM, Alloy Automation, and Vercel.

For our first livestream, MongoDB developer relations lead Shane McAllister sat down with Okta VP of engineering Andrew Yu for a conversation on how Okta became the preferred identity provider of tech teams across a range of industries and sectors.


MongoDB developer relations lead Shane McAllister discusses the meteoric rise of Okta with Okta VP of engineering Andrew Yu.

Next, MongoDB senior developer advocate Jesse Hall spoke with VEERUM CTO Rob Southon about their unique “digital twin” technology that allows remote site visits, enabling VEERUM customers to reduce time, money, and environmental impact.


MongoDB senior developer advocate Jesse Hall and VEERUM CTO Rob Southon discuss VEERUM's innovative asset management model and how it reduces costs and carbon footprint.

Afterward, Gregg Mojica, co-founder of Alloy Automation, spoke to MongoDB principal developer advocate Mike Lynn about his tech journey, including Alloy’s participation in the MongoDB for Startups program, overcoming the challenges of the COVID-19 pandemic, and the successful completion of a Series A funding round.


MongoDB principal developer advocate Michael Lynn talks to Alloy Automation co-founder Gregg Mojica about Alloy’s journey.

Shane McAllister also sat down with Vercel CEO Guillermo Rauch for a discussion about the MongoDB integration in the Vercel Marketplace, new announcements for Next.js 13, and the latest World Cup win for Argentina ⚽️.


MongoDB developer relations lead Shane McAllister speaks with Vercel CEO Guillermo Rauch on all the new Vercel announcements at AWS re:Invent 2022.

MongoDB executives on prime time

MongoDB CEO Dev Ittycheria and CISO Lena Smart were featured on theCube by SiliconANGLE, a leading tech news site. Ittycheria discussed some of current data trends, including the link between productivity and innovation, how consolidating tech stacks accelerates the release cycle, and how the MongoDB developer data platform empowers teams to make decisions faster and shorten time to market.


MongoDB CEO Dev Ittycheria sits down with interviewers from SiliconANGLE’s theCube talk show to talk about today’s rapidly evolving data landscape.

In her interview on theCube, Smart covered a variety of subjects, including new security features like Queryable Encryption, how to turn security from an obstacle to an opportunity, and the evolution of MongoDB. She also compared and contrasted emerging security challenges (from AI to quantum computing) with past crises like Y2K.


In this episode of SiliconANGLE’s theCube, MongoDB CISO Lena Smart reflects on past crises, the development of MongoDB, and how to turn security from obstacle to opportunity.

Lastly, SVP of product management Andrew Davidson spoke with Patrick Moorhead and David Newman from Futurum Research on The Six Five on the Road at AWS re:Invent 2022 show. Davidson began with an explanation of the data landscape, defining and discussing transactional data before explaining how MongoDB’s innovative document data model empowers developers with its flexibility and ease of use.

Security and "The Rise of the Developer Data Platform"

In CISO Lena Smart’s re:Invent fireside chat with MongoDB’s Karen Huaulme, Principal Developer Advocate; Andrew Davidson, SVP, Products; and Krista Braun, Executive Keynote Advisor; Smart discussed how security innovations played an important role in the growth of the MongoDB Atlas developer data platform, an integrated set of data and application services that share a unified developer experience.


MongoDB CISO Lena Smart presents “The Rise of the Developer Data Platform,” before joining MongoDB employees Andrew Davidson, Krista Braun, and Karen Huaulme for a panel discussion on the evolution of MongoDB.

Making the most out of your data

On each day of AWS re:Invent, MongoDB hosted lightning talks at our in-booth theater, covering a variety of practical topics such as frontend development, real-time analytics, and more.

In his two daily sessions, executive solutions architect Sigfrido “Sig” Narváez discussed different ways to maximize the value of your data. In the first workshop, Narváez and Ralph Capasso, director of engineering for MongoDB Data Lake, used real (and fictional) open source data from a Blue Origin rocket launch to demonstrate how the Atlas developer data platform can streamline tech stacks and provide real-time analytics and visualizations to boost customer engagement. Check out the rocket-analytics GitHub repo for more information.

In his next workshop, Narváez discussed how to tap into data locked away in relational databases by migrating to the Atlas developer data platform. In his demo, Narváez covered several key competencies, including transforming data with the MongoDB Relational Migrator, invoking a GraphQL endpoint with Postman, and using the Realm SDK to build a mobile app enabled with cloud sync. Visit the liberate-data GitHub repo for more information, including a complete Postman collection to import into your environment.

MongoDB executive solutions architect Sigfrido Narváez gives a demonstration to a small group in the green-and-lime MongoDB booth.
MongoDB executive solutions architect Sigfrido “Sig” Narváez presents a workshop at the in-booth theater at AWS re:Invent 2022.

Demystifying the edge

Following up on a MongoDB World presentation, “Building Your First Edge Computing App with MongoDB Atlas Device Sync, Realm, & Verizon 5G Edge,” MongoDB solutions architect and Realm specialist Mark Brown put together a practical, step-by-step tutorial on how developers can use LTE and 5G networks to bypass the physical fiber optic infrastructure of the internet, and deliver speedy, seamless service. Although Brown’s re:Invent sessions were not recorded, you can access the workshop modules for a self-guided walkthrough. Read the Mobile Edge Computing: Realizing the Benefits of 5G with MongoDB and Verizon 5G Edge white paper and blog series for more information.

MongoDB solutions architect (and Realm specialist) Mark Brown presents a guided walkthrough on edge computing and LTE/5G networks at AWS re:Invent 2022.
MongoDB solutions architect (and Realm specialist) Mark Brown hosts his workshop on bypassing physical internet infrastructure using LTE and 5G networks and edge applications.

The evolution of a data-driven application

In her workshop “10 Things You Didn’t Know Your Data Could Do for You,” MongoDB principal developer advocate Karen Huaulme shared her experience in creating a data-driven application. Read “Streamline, Simplify, Accelerate: New MongoDB Features Reduce Complexity” to learn more.


MongoDB principal developer advocate Karen Huaulme shares the struggles, rewards, and lessons learned from building a data-driven application.

Transitioning from relational to NoSQL

MongoDB developer relations director Rick Houlihan shared cultural and operational aspects of switching from relational to NoSQL. Watch the recording of his talk, "From RDBMS to NoSQL,” or read "Relational to NoSQL at Enterprise Scale.”


MongoDB developer relations director Rick Houlihan talks about the cultural shift from relational to NoSQL at AWS re:Invent 2022.

If you attended AWS re:Invent 2022, we hope you had a good time, and that we’ll see you there next year. In the meantime, you can run MongoDB Atlas on AWS — just head to the AWS Marketplace to get started. Sign up for a free trial to test out all the features and abilities that you’ve heard so much about.

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Forrester Study: How IT Decision Makers Are Using Next-Generation Data Platforms

Data is critical to every financial institution; it is recognized as a core asset to drive customer growth and innovation. As the need to leverage data efficiently increases, however, the legacy technology that still underpins many organizations is not built to support today’s requirements. Not only is this infrastructure costly and complex, it doesn’t support the diversity of workloads and functions that modern applications require. To overcome these challenges, organizations are increasingly adopting an integrated data platform that offers a seamless developer experience, runs anywhere, and scales to meet growing business needs. To better understand how such data platforms are being used, MongoDB commissioned Forrester Consulting to survey global IT decision makers at financial services and fintech organizations. In this article, we’ll share findings from the survey to help answer questions such as: What impact are legacy technologies having on financial services? What are the requirements for a data platform? And, for those already adopting next-generation data platforms, what benefits are they experiencing? According to the survey, the majority of decision makers are aware of issues related to legacy technologies: 57% of respondents said that their legacy technology was too expensive and doesn’t fulfill the requirements of modern applications. 50% said legacy technology cannot support the volume, variety, and velocity of transactional data. 47% noted that their systems landscape struggled to handle the rate of change required to stay up to date with customer expectations. Download the full study: What’s Driving Next-Generation Data Platform Adoption in Financial Services What is a next-generation data platform? Within the context of this study, a next-generation data platform is defined as supporting flexible and versatile data models, offering multiple access patterns (e.g., document, relational, graph), and catering to the speed, scale, performance, integration, and security needs of small or large organizations for new development or modernization efforts. All of these features are included in a single platform that delivers real-time, consistent, and trusted data to support a business. Adoption of next-generation data platforms in the financial services and fintech space is already high, with nearly 90% of respondents saying they are already adopting. The benefits are already understood, with 74% of respondents acknowledging not only that there are technology benefits but also that a next-generation data platform frees up teams to focus on innovation and enables faster software builds and iterating at scale (76%). The key to innovation - What's driving the adoption of next-gen data platforms? Security and risk management are key use cases Given the huge amount of confidential client and customer data that the financial services industry deals with on a daily basis — and the strict regulations — security must be of highest priority. The perceived value of this data also makes financial services organizations a primary target for data breaches. Many organizations are still working to realize the full potential of adopting next-generation data platforms; however, it’s understood that such platforms are the only way to manage cost, maximize security, and continue to innovate. Fraud protection (51%), risk management (46%) and anti-money laundering (46%) are high priorities for any new data platform, according to respondents. And, these findings directly correlate with 40% of respondents saying that their current database is unable to meet security requirements. Multi-cloud is driving investment Regardless of their size and business mix, most financial institutions have come to understand the benefits of cloud and multi-cloud services. Multi-cloud — the practice of leveraging cloud services from more than one provider — is no longer just a nice-to-have option. Regulators, however, are increasingly focused on cloud concentration risk as so much of the technology underpinning global financial services relies on so few large cloud services providers. Regulators have so far offered financial institutions warnings and guidance rather than enacting new regulations, although they are increasingly focused on ensuring that the industry is considering plans. An outage or cyberattack at a large public cloud provider, they worry, could derail the global financial system. Decision makers are finding that multiple clouds provide them with lower costs, higher performance, and greater flexibility. This is why, according to the survey, the top driver for investment for decision makers when adopting next-generation data platforms is multi/hybrid cloud capabilities (49%), followed by scalability (44%). Improving real-time analytics capabilities The ability to perform real-time analytics is key for financial institutions, as they need to provide more personalized customer experiences, react more quickly to market trends, and detect and prevent potential threats. With legacy systems, few of these organizations can respond to changes in data minute by minute or second by second. Among survey respondents, real-time analytics was the top feature (54%) that organizations are interested in with regard to next-generation data platforms. With improved analytics capabilities, businesses can analyze any data in place and deliver insights in real time. Legacy infrastructure is holding organizations back To remain competitive and build experiences that retain customers, financial institutions need to master their data estate. Specifically, they need to free themselves from the rigid data architectures associated with legacy mainframes and monolithic enterprise banking applications. Only then can developers build high-quality customer-facing applications rather than maintain legacy systems. High costs and data complexity are the top challenges driving organizations to modernize legacy workloads and unlock business agility. According to 57% of IT decision-makers questioned, legacy technology is too expensive and does not fulfill the requirements of modern applications. This correlates with 79% of respondents seeking a data platform that will address multiple workloads — ranging from transactional to analytical — as data continues to expand. What is the impact? Financial organizations use next-generation data platforms to replace legacy technologies that fragment and duplicate data and cause internal silos. This change also addresses key needs like reducing costs, lowering complexity, better onboarding for customers, and meeting security requirements. Once in place, a next-generation data platform provides several advantages, including minimizing data inconsistencies (43%), expanding geographical coverage (42%), freeing up resources (40%), and reducing time-to-market for new ideas (37%). Other advantages include eliminating the impact of database downtime for upgrades, migrations, and schema changes. And, additional benefits can be seen within the customer and employee experience, as they engage with and access information. Based on these benefits, financial services organizations are looking to increase investment in next-generation data platforms by an average of one million dollars or more in the next one to three years. The volume and variety of data that financial services companies must deal with will only increase in the coming years. As such, figuring out how to leverage, protect, and innovate around that data will put organizations in good stead moving forward. A next-generation data platform can be the key to making this happen. About the study MongoDB commissioned Forrester Consulting to conduct a study questioning global IT decision makers at financial services and fintech organizations to evaluate the impact they are experiencing when adopting next-generation data platforms. The study evaluates the benefits, challenges, and barriers of adoption that decision makers are experiencing, as well as the outcomes after adoption. To create this study, Forrester Consulting supplemented this research with custom survey questions asked of database/data platform strategy decision-makers in finserv (73%) or fintech (27%) from North America (22%), Europe (39%), and APAC (39%). The organizations questioned had 1,000+ employees. The custom survey began and was completed in September 2022. Download the full study — What’s Driving Next-Generation Data Platform Adoption in Financial Services — to learn about organizations’ needs and plans for using next-generation data platforms.

December 13, 2022
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Retrieval Augmented Generation for Claim Processing: Combining MongoDB Atlas Vector Search and Large Language Models

Following up on our previous blog, AI, Vectors, and the Future of Claims Processing: Why Insurance Needs to Understand The Power of Vector Databases , we’ll pick up the conversation right where we left it. We discussed extensively how Atlas Vector Search can benefit the claim process in insurance and briefly covered Retrieval Augmented Generation (RAG) and Large Language Models (LLMs). MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More One of the biggest challenges for claim adjusters is pulling and aggregating information from disparate systems and diverse data formats. PDFs of policy guidelines might be stored in a content-sharing platform, customer information locked in a legacy CRM, and claim-related pictures and voice reports in yet another tool. All of this data is not just fragmented across siloed sources and hard to find but also in formats that have been historically nearly impossible to index with traditional methods. Over the years, insurance companies have accumulated terabytes of unstructured data in their data stores but have failed to capitalize on the possibility of accessing and leveraging it to uncover business insights, deliver better customer experiences, and streamline operations. Some of our customers even admit they’re not fully aware of all the data in their archives. There’s a tremendous opportunity to leverage this unstructured data to benefit the insurer and its customers. Our image search post covered part of the solution to these challenges, opening the door to working more easily with unstructured data. RAG takes it a step further, integrating Atlas Vector Search and LLMs, thus allowing insurers to go beyond the limitations of baseline foundational models, making them context-aware by feeding them proprietary data. Figure 1 shows how the interaction works in practice: through a chat prompt, we can ask questions to the system, and the LLM returns answers to the user and shows what references it used to retrieve the information contained in the response. Great! We’ve got a nice UI, but how can we build an RAG application? Let’s open the hood and see what’s in it! Figure 1: UI of the claim adjuster RAG-powered chatbot Architecture and flow Before we start building our application, we need to ensure that our data is easily accessible and in one secure place. Operational Data Layers (ODLs) are the recommended pattern for wrangling data to create single views. This post walks the reader through the process of modernizing insurance data models with Relational Migrator, helping insurers migrate off legacy systems to create ODLs. Once the data is organized in our MongoDB collections and ready to be consumed, we can start architecting our solution. Building upon the schema developed in the image search post , we augment our documents by adding a few fields that will allow adjusters to ask more complex questions about the data and solve harder business challenges, such as resolving a claim in a fraction of the time with increased accuracy. Figure 2 shows the resulting document with two highlighted fields, “claimDescription” and its vector representation, “claimDescriptionEmbedding” . We can now create a Vector Search index on this array, a key step to facilitate retrieving the information fed to the LLM. Figure 2: document schema of the claim collection, the highlighted fields are used to retrieve the data that will be passed as context to the LLM Having prepared our data, building the RAG interaction is straightforward; refer to this GitHub repository for the implementation details. Here, we’ll just discuss the high-level architecture and the data flow, as shown in Figure 3 below: The user enters the prompt, a question in natural language. The prompt is vectorized and sent to Atlas Vector Search; similar documents are retrieved. The prompt and the retrieved documents are passed to the LLM as context. The LLM produces an answer to the user (in natural language), considering the context and the prompt. Figure 3: RAG architecture and interaction flow It is important to note how the semantics of the question are preserved throughout the different steps. The reference to “adverse weather” related accidents in the prompt is captured and passed to Atlas Vector Search, which surfaces claim documents whose claim description relates to similar concepts (e.g., rain) without needing to mention them explicitly. Finally, the LLM consumes the relevant documents to produce a context-aware question referencing rain, hail, and fire, as we’d expect based on the user's initial question. So what? To sum it all up, what’s the benefit of combining Atlas Vector Search and LLMs in a Claim Processing RAG application? Speed and accuracy: Having the data centrally organized and ready to be consumed by LLMs, adjusters can find all the necessary information in a fraction of the time. Flexibility: LLMs can answer a wide spectrum of questions, meaning applications require less upfront system design. There is no need to build custom APIs for each piece of information you’re trying to retrieve; just ask the LLM to do it for you. Natural interaction: Applications can be interrogated in plain English without programming skills or system training. Data accessibility: Insurers can finally leverage and explore unstructured data that was previously hard to access. Not just claim processing The same data model and architecture can serve additional personas and use cases within the organization: Customer Service: Operators can quickly pull customer data and answer complex questions without navigating different systems. For example, “Summarize this customer's past interactions,” “What coverages does this customer have?” or “What coverages can I recommend to this customer?” Customer self-service: Simplify your members’ experience by enabling them to ask questions themselves. For example, “My apartment is flooded. Am I covered?” or “How long do windshield repairs take on average?” Underwriting: Underwriters can quickly aggregate and summarize information, providing quotes in a fraction of the time. For example, “Summarize this customer claim history.” “I Am renewing a customer policy. What are the customer's current coverages? Pull everything related to the policy entity/customer. I need to get baseline info. Find relevant underwriting guidelines.” If you would like to discover more about Converged AI and Application Data Stores with MongoDB, take a look at the following resources: RAG for claim processing GitHub repository From Relational Databases to AI: An Insurance Data Modernization Journey Modernize your insurance data models with MongoDB and Relational Migrator

April 18, 2024