10 Things We Learned at MongoDB World 2022

When you return to a normal routine after a long break, you find out how much you miss your old routine. After hosting MongoDB World remotely for two years, we were happy to get back to seeing people in person — almost 3,000 of them. Here’s a quick rundown of the top 10 things we learned at MongoDB World 2022.

1. Queryable Encryption was a hit

How many times have you been to a concert and the opening act winds up being as good as the band you actually went to see? Queryable Encryption was like that at MongoDB World 2022. While a lot of attendees came to learn about MongoDB Atlas Search or Atlas Serverless Databases, they were equally intrigued by the ability to encrypt data in use and perform rich, expressive queries on encrypted data. This groundbreaking innovation is the result of a collaborative effort between Brown University cryptographer Seny Kamara, his longtime collaborator Tarik Moataz, and MongoDB.

2. Developers are in the driver's seat

Starting with the opening keynote by MongoDB CEO Dev Ittycheria, MongoDB World reinforced the notion that developers are the key to the future success and productivity for today’s organizations. “Every product we build, every feature we develop, is all geared toward developer productivity,” Ittycheria said. In fact the entire event centered on powerful new tools that are now available in our developer data platform.

In the Partner Promenade, dozens of vendors showed how they’re helping developers become faster and more productive. As Søren Bramer Schmidt, chief architect and founder of Prisma, explained, “New generations of developers are much bigger, and we can invest in better tooling for them. It’s an exciting time to be building tools for developers.” As the world increasingly goes digital, developers will be the key to companies’ success. Services, products, and advancements are inherently tied to the ability of developers to quickly build, iterate, and release.

3. Everyone's data is in motion

The volume of data moving to the cloud is unprecedented. In a session titled “Connecting Distributed Data to MongoDB With Confluent,” Joseph Morais, cloud partner solutions architect for Confluent, cited a study that predicted 75% of all databases would be on a cloud platform by 2022. MongoDB senior vice president of product management, Andrew Davidson, said, “MongoDB has really broken through with the MongoDB Relational Migrator at the perfect time, since so many enterprises are accelerating their efforts to get off legacy relational databases and legacy on-premises estates to move to MongoDB Atlas.”

4. Public cloud security is not as easy as some people think

While scores of businesses are increasing their cloud footprints with new cloud-native services and applications, securing them is becoming increasingly complex. Steve Walsh, senior solutions architect at MongoDB, gave a session titled “Securing Your Application's Data in the Public Cloud” and cited constantly changing cloud deployments and security policies in multi-cloud environments as reasons why security can be three times more complex in a multi-cloud environment. According to an ITRC study that Walsh cited, failure to configure cloud settings properly caused 30% of data breaches in 2021. MongoDB Atlas is designed to be secure by default, which simplifies the process of restricting access to sensitive data.

5. Ray Kurzweil might be even more prescient than he realizes

On Day 3 of MongoDB World 2022, best-selling author, pioneering inventor, and futurist Ray Kurzweil delivered a wide-ranging keynote address covering everything from computational power to vaccine trials to life expectancy and literacy rates. In the address, Kurzweil said it was likely that an AI would pass a Turing test by 2029. Just days later, news reports came out about a Google engineer who’d been fired after claiming that an artificial-intelligence chatbot the company developed had become sentient, though the company dismissed the claims.

6. Attendees were eager to try MongoDB

It’s easy to assume that everyone who came to MongoDB World was already using it and wanted to know about new features and capabilities. But in the Learn Booth at the event, plenty of visitors weren't using MongoDB at all — they were there to discover and evaluate. In the Ask the Experts booth, roughly one in 10 people asked about how to prepare to migrate to MongoDB. One of the most common questions we heard was, "How do I convert relational schemas to the document model?" We have tools like Relational Migrator to help with that. We also recommend training for developer and ops teams, including our MongoDB for SQL Pros university course and our Developer-Led Training programs to ramp them up on what makes MongoDB different from SQL.

7. Developer friction comes in many forms

The opening keynote address and product announcements set the stage for many of the conversations we had over the next few days. We consistently heard from developers about the friction points that we could help eliminate for them, and how reducing developer friction results in real benefits — apps and services get launched that could not have existed otherwise because of the toll that complexity takes on development teams’ bandwidth. Atlas Serverless databases are going to be a big part of getting those new services off the ground because it’s one less thing developers have to worry about. And the MongoDB CLI allows developers to interact with our services using the method they’re familiar with — especially advanced developers who prefer control and speed over a more visual interface.

8. @MarkLovesTech draws the crowds

MongoDB CTO Mark Porter was the center of the action at the event. Wherever he went, a crowd would gather, eager to meet, exchange thoughts, and ask questions. His talks during the Builder’s Fest were standing room only.

Mark Porter delivers a short talk on scaling and managing teams at MongoDB World 2022. Photo by Eoin Brazil.

9. Every software company needs custom track jackets

Our field marketing team knocked it out of the park with the custom track jacket. After MongoDB CEO Dev Ittycheria debuted the jacket during the Day 1 keynote, it immediately became the most desired piece of swag of the show. A few lucky contestants won their own track jackets during the Builder’s Fest. Developers are either highly fashion-conscious or avid joggers.

10. There's no replacement for in-person gatherings

For almost three years, we’ve been getting by with remote events and Zoom calls, but we learned at least two more things from MongoDB World 2022: There’s no replacement for real-life, in-person experiences, and remote interactions actually require a different set of skills. “It is not impossible to talk with people on Zoom. But it requires so much more intentionality,” Mark Porter said. “My takeaway from MongoDB World is making sure that in this new hybrid world, we can talk with people! But even on Zoom, we must become much more focused on the intentionality of talking with them because it is so much different."

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Building Together: MongoDB Is Google Cloud’s Technology Partner of the Year for Data Management

Few companies can credibly claim to understand and work with data as effectively as Google, so when Google Cloud announced this week that MongoDB is the Google Cloud Technology Partner of the Year for Data Management, the award felt particularly meaningful. But if the honor was just a matter of two leaders in data management slapping each other on the back, it wouldn't be that interesting. No, what makes the award compelling is the customer success that Google Cloud and MongoDB have jointly enabled. We agree with Google Cloud's vision that "True transformation spans the entire business and enables every person to transform," and together have helped customers to achieve this. Some customers elect to run MongoDB Atlas, our fully managed database service, on Google Cloud because of its broad footprint (Atlas is available in 29 Google Cloud regions), cost/performance benefits, seamless security and scalability, and the recently launched pay-as-you-go option that simplifies subscriptions and can reduce costs. Others have gone further, choosing to take advantage of combining the best services from both companies, like using Google Kubernetes Engine (GKE) or Google Cloud Run for their application tier and MongoDB for their data tier. And some choose Google Cloud so they can tie into Google services that are tightly integrated with MongoDB, including BigQuery, Datastream, and Dataproc. A few examples that show the ambition and innovation MongoDB and Google Cloud customers are bringing to their products: Precognitive combines device intelligence, advanced behavioral analytics, and a real-time decision engine to accurately detect and prevent fraud for online businesses. Using Google Cloud Bigtable as their data store for behavioral and device data, and MongoDB as the data store for everything else, Precognitive is able to capture and analyze vast amounts of data from around the world, in near real time, to combat fraud. Read our full story on how Precognitive uses MongoDB Atlas . Forbes , the world's largest business media brand, reaches more than 140 million people worldwide every month across a number of online and offline channels. The company needed to innovate its way through the global COVID-19 pandemic and turned to MongoDB Atlas running on Google Cloud to enable dramatically better agility. Among other initiatives, the publishing giant married MongoDB's flexible data schema with Google Cloud’s machine learning services to deliver a trending story recommendation engine for journalists. Read our full story on Forbes’ migration to Atlas — and the successes of that move . One of the things we love about working with Google Cloud is the company's pragmatic approach to solving customer problems. Customers tend to choose a predominant cloud vendor upon which to build the majority of their applications, and Google Cloud often serves this role. At Plaid , which helps retailers decipher the complexities of consumer behavior, the company chose to migrate its legacy databases to MongoDB Atlas running on Google Cloud, allowing it to tap into Google Kubernetes Engine, Google Cloud Engine (GCE), Cloud BigTable, and BigQuery. But Plaid also needed to ensure it could run across multiple clouds, which Google Cloud enables with its Anthos service . MongoDB and Google Cloud, working together, deliver that multi-cloud experience for customers. Read our case study on Plaid, MongoDB, and Google Cloud . If you’re ready to experience the fruits of the MongoDB and Google Cloud partnership, take a look at MongoDB Atlas in the Google console . You can get started for free. Read more stories about MongoDB and Google Cloud customers doing great things: How Humanitix uses MongoDB Atlas Device Sync to close the education gap, one great app experience at a time Powering France’s Yellow Pages: How Solocal Turned to MongoDB Atlas and Google Cloud to Manage Two Billion Visits a Year How Macquarie Bank Built a Real-Time Payments Platform in Weeks KODE Labs: Advanced smart building management delivers on promise of sustainable buildings

June 17, 2022
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Building AI With MongoDB: Integrating Vector Search And Cohere to Build Frontier Enterprise Apps

Cohere is the leading enterprise AI platform, building large language models (LLMs) which help businesses unlock the potential of their data. Operating at the frontier of AI, Cohere’s models provide a more intuitive way for users to retrieve, summarize, and generate complex information. Cohere offers both text generation and embedding models to its customers. Enterprises running mission-critical AI workloads select Cohere because its models offer the best performance-cost tradeoff and can be deployed in production at scale. Cohere’s platform is cloud-agnostic. Their models are accessible through their own API as well as popular cloud managed services, and can be deployed on a virtual private cloud (VPC) or even on-prem to meet companies where their data is, offering the highest levels of flexibility and control. Cohere’s leading Embed 3 and Rerank 3 models can be used with MongoDB Atlas Vector Search to convert MongoDB data to vectors and build a state-of-the-art semantic search system. Search results also can be passed to Cohere’s Command R family of models for retrieval augmented generation (RAG) with citations. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. A new approach to vector embeddings It is in the realm of embedding where Cohere has made a host of recent advances. Described as “AI for language understanding,” Embed is Cohere’s leading text representation language model. Cohere offers both English and multilingual embedding models, and gives users the ability to specify the type of data they are computing an embedding for (e.g., search document, search query). The result is embeddings that improve the accuracy of search results for traditional enterprise search or retrieval-augmented generation. One challenge developers faced using Embed was that documents had to be passed one by one to the model endpoint, limiting throughput when dealing with larger data sets. To address that challenge and improve developer experience, Cohere has recently announced its new Embed Jobs endpoint . Now entire data sets can be passed in one operation to the model, and embedded outputs can be more easily ingested back into your storage systems. Additionally, with only a few lines of code, Rerank 3 can be added at the final stage of search systems to improve accuracy. It also works across 100+ languages and offers uniquely high accuracy on complex data such as JSON, code, and tabular structure. This is particularly useful for developers who rely on legacy dense retrieval systems. Demonstrating how developers can exploit this new endpoint, we have published the How to use Cohere embeddings and rerank modules with MongoDB Atlas tutorial . Readers will learn how to store, index, and search the embeddings from Cohere. They will also learn how to use the Cohere Rerank model to provide a powerful semantic boost to the quality of keyword and vector search results. Figure 1: Illustrating the embedding generation and search workflow shown in the tutorial Why MongoDB Atlas and Cohere? MongoDB Atlas provides a proven OLTP database handling high read and write throughput backed by transactional guarantees. Pairing these capabilities with Cohere’s batch embeddings is massively valuable to developers building sophisticated gen AI apps. Developers can be confident that Atlas Vector Search will handle high scale vector ingestion, making embeddings immediately available for accurate and reliable semantic search and RAG. Increasing the speed of experimentation, developers and data scientists can configure separate vector search indexes side by side to compare the performance of different parameters used in the creation of vector embeddings. In addition to batch embeddings, Atlas Triggers can also be used to embed new or updated source content in real time, as illustrated in the Cohere workflow shown in Figure 2. Figure 2: MongoDB Atlas Vector Search supports Cohere’s batch and real time workflows. (Image courtesy of Cohere) Supporting both batch and real-time embeddings from Cohere makes MongoDB Atlas well suited to highly dynamic gen AI-powered apps that need to be grounded in live, operational data. Developers can use MongoDB’s expressive query API to pre-filter query predicates against metadata, making it much faster to access and retrieve the more relevant vector embeddings. The unification and synchronization of source application data, metadata, and vector embeddings in a single platform, accessed by a single API, makes building gen AI apps faster, with lower cost and complexity. Those apps can be layered on top of the secure, resilient, and mature MongoDB Atlas developer data platform that is used today by over 45,000 customers spanning startups to enterprises and governments handling mission-critical workloads. What's next? To start your journey into gen AI and Atlas Vector Search, review our 10-minute Learning Byte . In the video, you’ll learn about use cases, benefits, and how to get started using Atlas Vector Search.

April 25, 2024