MongoDB Supports Cutting-Edge Startups with NVIDIA Inception

Steve Jurczak
October 21, 2022
#Startups

We are excited to announce that MongoDB for Startups is collaborating with NVIDIA Inception to power cutting-edge startups in AI, autonomous driving, gaming, robotics, healthcare, and more. Here, we answer frequently asked questions to provide a brief overview of the initiative.

Why should a startup build its MVP with MongoDB for Startups and NVIDIA Inception?

If you are a founder looking to build innovative technologies in AI, data science, gaming, and other breakthrough industries, then you’ll want to leverage MongoDB for Startups (a program that helps startups build faster and scale further with free MongoDB Atlas credits, one-on-one technical advice, co-marketing opportunities, and access to our partner network) and NVIDIA Inception (a free global program designed to nurture cutting-edge startups) for your MVP.

Together with MongoDB and NVIDIA Inception, founders can leverage the most intuitive and flexible way to work with data so they can adapt quickly to the changing needs of a growing business. Utilizing MongoDB Atlas, a fully managed multi-cloud developer data platform, startups can scale rapidly while enjoying the freedom to run anywhere with the best cloud environments. NVIDIA Inception members get a custom set of ongoing benefits, such as NVIDIA Deep Learning Institute credits, marketing support, and technology assistance, which provides startups with the fundamental tools to help them grow.

What is the MongoDB for Startups program?

MongoDB for Startups is focused on creating a valuable technical startup program that enables the success of high-growth startups from ideation to IPO. We designed this program to give startups access to the best technical database for their rapidly scaling ventures.

Apply to our program and you will receive:

$500 in credits for all MongoDB cloud products (valid for 12 months)
A dedicated technical advisor for a two-hour, one-on-one consultation to help you with your data migration and optimization
Co-marketing opportunities
Access to the MongoDB developer ecosystem and access to our VC partners

What is the NVIDIA Inception Program?

NVIDIA Inception helps startups in all industries accelerate growth and build their products faster. With more than 10,000 startup members, the program is free and available for tech startups in all stages.

Inception members can receive:

Up to $100K in AWS cloud credits
Technical training and engineering guidance
Product discounts
Co-marketing and co-selling
Customer introductions and VC exposure

Apply to NVIDIA Inception and the MongoDB for Startups Program today.

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How to Model Data in Document Databases for Read and Write Performance

MongoDB is often seen as a good choice for storing unstructured data. The ability to persist data in MongoDB without defining what type of data it is or designing schema for it is one of the reasons many of our customers choose us. But the idea that MongoDB is a "schemaless" database is not accurate. Although a document database does allow you to store data without defining what it is, the shape of that data matters if you plan to do more than simply retrieve whole documents by keys. The Need to Model for NoSQL At this year's MongoDB World event, Daniel Coupal , staff developer advocate at MongoDB, explained that the need to model data in a document database is due to the presence of constraints that must be taken into account when you're persisting data in MongoDB. Constraints include things like network and hard disc speed, maximum file size of documents, and features that you don't have now but might add later. "If you look at the stack of an application, you have the application that talks to MongoDB that talks to whatever layer you add — it could be the cloud or a physical machine — those constraints are going to map to some of those layers," Coupal said. "It's imperative to know the features of the products you use." MongoDB offers features like transactions , field-level encryption , data federation , and archives , which require that you model data differently. In relational databases , data modeling is fairly straightforward due to the nature of third normal form (3NF) — the database schema design approach for relational databases. So, essentially, there’s only one solution for modeling the database. With the document model and MongoDB, however, you have several options for data modeling. You can nest everything under a single collection , and possibly wind up with duplicate sets of data (and, therefore, data concurrency issues), or you can use separate collections for different datasets and avoid duplicate data altogether. Ultimately, according to Coupal, "the optimal grouping of objects into collections is determined by the workload." During the session, Coupal provided a breakdown of data modeling methodology that involves a three-phase process starting with the workload, proceeding to relationships, and moving to patterns for optimization purposes. "In a lot of the solutions we're trying to build with NoSQL, performance is a top requirement," he said. He also cited better performance as one of the big reasons why people switch from SQL to MongoDB. What are the Data Access Patterns? In essence, with MongoDB, the way you plan to access the data determines the way you store it in the database. Data that is accessed together should be stored together. In the session, Coupal also presented an insightful analogy between the nature of data modeling in relational databases versus the document model in MongoDB. Essentially, the difference is that with the relational model, if you have a car and you want to model it, you'll take each part of the car individually and place it in its own table. Then, when you want to use the car, you have to reassemble it part by part (and table by table) before you can drive it. With MongoDB, you take the car and put it in the garage (the equivalent of a collection). When you want to use it, you take it out. That's it. "We do only one read on the disc to get everything we need together," Coupal said. Techniques for Data Modeling in MongoDB Coupal also provided an explanation of two different data modeling techniques: referencing and embedding . Embedding is a way to combine what would normally be two tables in a tabular database into one using an array. "The array is the expression of the one-to-many relationship," Coupal said. Referencing is useful for when the "many" side of the relationship is a huge number. Although MongoDB does support transactions, in almost all cases, it's better to use a document for more efficient read-write performance. As we know, developers are the ones who are most likely to understand the data access patterns for their applications. Properly designed schemas can increase performance for a given set of hardware by reducing computation, I/O, and contention. What really differentiates MongoDB from relational databases is the ability to co-locate related data in the atomic unit of storage so multiple values for an attribute can exist within a single record rather than being broken up into rows and stored independently. A document database with a properly designed schema lets you filter and retrieve data with minimal computational overhead and in a single I/O operation. This approach can make finding and retrieving data far faster and less expensive. To see the complete session from MongoDB World 2022, which includes a list of 12 data modeling patterns and techniques for evolving schema in MongoDB, watch The Principles of Data Modeling for MongoDB .

October 20, 2022

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Hanabi Technologies Uses MongoDB to Power AI Assistant, Hana

For all the hype surrounding generative AI, cynics tend to view the few real-world implementations as little more than “fancy chatbots.” But for Abhinav Aggarwal, CEO of Hanabi Technologies, the idea of a generative AI-powered bot that is more than just an assistant was intriguing. “I’d been using ChatGPT since it launched,” said Aggarwal. “That got me thinking: How could we make a chatbot that was like a team member?” And with that concept, Hana was born. The problem with bots “Most generative AI chatbots do not act like people; they wait for a command and give a response,” said Aggarwal. “We wanted to create a human-like chatbot that would proactively help people based on what they wanted—automating reminders, for example, or fetching time zones from your calendar to correctly schedule meetings.” Hanabi’s flagship product, Hana, is an AI assistant designed to enhance team collaboration within Google Chat, working in concert with Google Workspace and its suite of products. “Our target customers are smaller companies of between 10 and 50 people. At this size you’re not going to build your own agent from scratch,” he said. Hana integrates with Google APIs to deliver a human-like assistant that chimes in with helpful interventions, such as automatically setting reminders and making sure meetings are booked in the right time zone for each participant. “Hana is designed to bring AI to smaller companies and help them collaborate in a space where they are already working—Google Workspace,” Aggarwal explained. The MongoDB Atlas solution For Hana to act like a member of the team, Hanabi needed to process massive amounts of data to support advanced features like retrieval-augmented generation (RAG) for better information retrieval across Google Docs and many other sources. And with a rapidly growing user base of over 600 organizations and 17,000+ installs, Hanabi also required a secure, scalable, and high-performing data storage solution. MongoDB Atlas provided a flexible document model, built-in vector database, and scalable cloud-based infrastructure, freeing Hanabi engineers to build new features for Hana rather than focusing on rote tasks like data extract, transform, and load processes or manual scaling and provisioning. Now, MongoDB Atlas handles a variety of responsibilities: Scalability and security: MongoDB Atlas’s auto-scaling and automatic backup features have enabled Hanabi to seamlessly grow its user base without the need for manual database management. RAG: MongoDB Atlas plays a critical role in Hana’s RAG functionality. The platform enables Hanabi to split Google Docs into small sections, create embeddings, and store these sections in Atlas’s vector database. Development Processes: According to Aggarwal, MongoDB’s flexibility in managing changing schemas has been essential to the company’s fast-paced development cycle. Data Visualization: Using MongoDB Atlas Charts has enabled Hanabi to create comprehensive dashboards for real-time data visualization. This has helped the team track usage, set reminders, and optimize performance without needing to build a manual dashboard. Impact and results With MongoDB Atlas, Hanabi can successfully scale Hana to meet the demands of its rapidly expanding user base. The integration is also enabling Hana to offer powerful features like automatic interactions with customers, advanced information retrieval from Google Docs, and manually added memory snippets, making it an essential tool for teams around the world. Next steps Hanabi plans to continue integrating more tools into Hana while expanding its reach to personal Gmail users. The company is also rolling out a new automatic-interaction feature, further enhancing Hana’s ability to proactively assist users without direct commands. MongoDB Atlas remains a key component of Hanabi’s stack, alongside Google Kubernetes Engine, NestJS, and LangChain, enabling Hanabi to focus on innovating to improve the customer experience. Tech Stack MongoDB Atlas Google Kubernetes Engine NestJS LangChain Are you building AI apps? Join the MongoDB AI Innovators Program today! Successful participants gain access to free MongoDB Atlas credits, technical enablement, and invaluable connections within the broader AI ecosystem. If your company is interested in being featured, we’d love to hear from you. Connect with us at ai_adopters@mongodb.com.

November 21, 2024