2019 MongoDB Innovation Awards Winners

We were happy again to see a number of amazing nominations for the 6th annual MongoDB Innovation Awards. MongoDB’s annual awards recognize companies who are using MongoDB to dream big and deliver incredibly bold, innovative solutions.

We are thrilled to announce our 12 winners who were honored at MongoDB World, New York, June 17-19.

Global Go to Market Partner of the Year: IBM

IBM

Each day, we create 4.5 quintillion bytes of data from a variety of sources from climate information, to posts on social media sites, purchase transaction records and medical images. IBM has the world’s most comprehensive portfolio of data and analytics capabilities in the market. IBM helps enterprises extract new insights from the explosion of available data to drive competitive advantage. IBM delivers a full spectrum of analytics descriptive, predictive and prescriptive to cognitive to turn big data into big insights. We provide talent management and social collaboration solutions powered by workforce science and cognitive analytics capabilities. For more information, go to: www.ibm.com/analytics

The Enterprise: Marriott

Marriott

With over 7,000 properties and a new hotel opening every 14 hours, Marriott is the leading hospitality provider in the world today. Marriott turned to MongoDB to assist with sprawling reservation shopping demand. By using MongoDB in conjunction with Linux on Z, Marriott architected a reservation shopping platform that will allow for future scale and quicker time to market for new capabilities. Marriott also plans to use MongoDB for placing data geographically close to users for better performance and to meet the needs of GDPR.

Launch Fast: ANZ

ANZ

ANZ is a major Australian-based bank operating in 34 markets globally with representation in Australia, New Zealand, Asia, Pacific, Europe, America and the Middle East. To ensure oversight in how the bank operates, a risk and compliance management framework includes a significant number of checks and balances to be enforced and monitored. Many are in the form of documents or policies, which need significant manual effort to ensure they are complied with. ANZ set out to digitize these documents and policies, making them scalable and customizable through the development of an application framework using MongoDB Atlas. Taken from ideation to operationalization in less than six months, the application framework uses MongoDB to record these checks and balances and how they are applied. In addition, the new framework allows checks and balances to be audited and has digitized critical data sets including links back to specific decision points. This gives ANZ the confidence to dynamically manage its risk and compliance framework and respond to regulatory and market changes in days rather than months.

Scale: Square Enix

Square Enix

Square Enix, producers of Tomb Raider and other blockbuster video games, uses MongoDB for their online suite of asynchronous multiplayer features within a wide range of their games. They recently chose MongoDB Atlas for all future game production because they needed scalability for their growing global player base. Atlas also allows them to scale elastically, thus cost effectively, to meet the demands of the game regardless of the number of concurrent players.

Data-Driven Business: Continental

Continental

Continental AG, which is shaping the mobility ecosystem, chose MongoDB to collect, store and process massive volumes of data for its newest safety initiative, Vision Zero, which strives for zero fatalities or accidents on roads. It relies on vast amounts of data to guide the development of next generation sensors and software. Now with MongoDB, their developers can build new models more rapidly and quickly build and test new prototypes.

Customer Experience: Royal Caribbean

Royal Caribbean International

One of the world's biggest cruise lines, Royal Caribbean, is utilizing MongoDB to help digitally transform and significantly enhance the customer experience. The Royal Digital Marketing team, facing complex and rigid legacy infrastructure, chose MongoDB Atlas to modernize their cruise search product in order to present product structures to their guests in a simple, flexible and customizable manner. The business impact has been dramatic, having exceeded forecasts and expectations in web bookings by over 20%. In addition, other business units are now coming to the Digital Marketing team looking to build off their success.

Internet of Things: Airobotics

Airobotics

Airobotics has developed a pilotless drone solution, the first of its kind in the global market. It provides an end-to-end, fully automatic solution for collecting aerial data and gaining invaluable insights. The industrial grade platform is available on-site and on-demand, enabling sites and cities to access premium aerial data in a faster, safer, more efficient way. They leverage MongoDB in the cloud to help support the vast amount of aerial data collected and processed to deliver a more connected world.

Savvy Startup: Corva

Corva

Corva is a real-time analytics platform that powers mission critical Oil & Gas operations around the world. The company enables real-time engineering and machine learning predictions that power faster and safer drilling. The platform utilizes an extensible, data-driven API with endpoints handling 100,000+ requests per minute of streaming sensor data. Rapidly scaling from 5 rigs & 500GB of data last April to 150+ rigs & 9TB of data today, they have chosen MongoDB, Kafka, & AWS Lambda to support growth.

Atlas: Compass

Compass

Compass is an incredibly fast-growing business that is disrupting the real estate industry with technology, aiming to build the first end-to-end real estate platform. They use MongoDB Atlas to administer their primary data storage without diverting focus from the platform and products their engineering team is building.

The William Zola Award for Community Excellence: Danielle Monteiro

Honoring of our strongest Community Support advocates , William Zola, who passed away unexpectedly in 2014, recognizes those whose support contributions make a significant difference to users around the globe. Danielle Monteiro shares her knowledge about MongoDB through blogging, public speaking, and social media. She focuses on working with beginning developers to teach them how to model and search their data. Her being awarded The William Zola Award for Community Excellence is a result of her passion for the MongoDB community.

Certified Professional of the Year

MongoDB each year recognizes a current MongoDB Certified Professional who demonstrates ingenuity, hard work, and expertise as the MongoDB Certified Professional of the Year. The 2019's MongoDB Certified Professional of the Year is Rolando Martinez, a Staff Data Engineer at Vrbo, working out of Austin, Texas.

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MongoDB Enterprise Operator for Kubernetes Now Generally Available

We are pleased to announce that the MongoDB Enterprise Operator for Kubernetes is now generally available and ready for production deployments. Operators enable the Kubernetes container orchestration system to control specific applications. The Enterprise Operator is a new component which enables both private, hybrid and public cloud devops teams to seamlessly integrate MongoDB deployments with applications, microservices and other components of the modern development stack and delivery platforms.

June 18, 2019
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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