What’s New In Cloud Manager?

MongoDB
February 23, 2016

We update MongoDB Cloud Manager regularly so that we can make it better for our customers. The latest version of Cloud Manager that was released on 2/22/16 introduced some significant changes and improvements. Continue reading below to learn more about the changes:

  • Release of optimized navigation structure UI, including simplified Servers & Processes view. Take a look at the new Deployment tab to see the re-styled Servers and Processes tabs.
  • Completely retooled user and roles management, giving users more flexibility over what is managed and what is not managed. You can find the users and roles under the “More” menu on the Deployment tab.
  • Ability to convert a replica set to a sharded cluster

If you have any feedback, feel free to send us an e-mail at beta-team@mongodb.com. Thanks for using Cloud Manager!

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Derivitec’s Risk Analysis Platform Uses MongoDB to Give Any Trader Access to Big-Bank Computing Power

Understanding future risk is vital for every successful organisation. In the financial markets, gaining that understanding means processing a mountain of complex data. In previous generations that meant you needed big computers and proprietary software that only big banks could afford. Thanks to better open source software and commodity hardware, that is no longer the case. Derivitec uses powerful modern infrastructure like MongoDB and AWS to make it possible for traders to get streamlined, industry standard financial analytics in minutes, not months. This gives them an understanding of what a range of different future events could mean for their investments. Answering the vital questions: What could go wrong? What’s the worst-case scenario? How can I hedge that risk? To address these questions Derivitec needed to build a different, more modern, platform. In 2011, when the company started, the team knew it would be processing a large volume and wide variety of data. So the database layer was going to be a defining factor for success. For the core functionality of the platform something fast, reliable and, above all, flexible was needed. New data sources, constantly changing regulations, and variable customer requirements meant functionality would be continually evolving. For Derivitec the obvious answer was a non-relational database with a document-oriented data model . Then, as now, MongoDB was a good fit. It’s worth pointing out that other no-SQL solutions were evaluated for this functionality but, quite simply, the performance levels were nowhere near what was needed. There are dozens of non-relational databases, but the choice was still a simple one. The three reasons for deciding on MongoDB were: Tried and tested technology – it has all the modern functionality and performance but it’s also got a massive community, thousands of customers and a mature support network. Operational tooling – Cloud Manager provides monitoring, backup and automation of deployments from the beginning - reducing costs and eliminating risk Ease of use – If you’re building a platform that will be easy to use, it makes sense to use one that’s easy to use too. MongoDB was also proven to run efficiently in the cloud, which is a huge part of what makes Derivitec special. In the early days Derivitec used Microsoft’s Azure cloud, but as its usage of MongoDB increased, the decision was made to switch from Azure to Amazon Web Services . Azure’s cloud was still too focused on its own software and it wasn’t as simple as it should have been to use non-Microsoft software, whilst AWS proved to be more accommodating. With that underlying infrastructure in place the platform could now be scaled out. MongoDB’s flexibility enabled Derivitec to deliver a system that could have users calculating risk on their portfolios in minutes, compared to the months it would have normally taken. For example, you can simply drop your trades in from Excel, visualise them, rearrange your portfolios, set up new portfolios, and manage the whole booking cycle natively within the app. The playing field is far from level. A big financial institution can still call a Napoleon's army of resources - both personnel and technology. But with the right data, enough context and access to intuitive tools you might just be able to see what your multiple futures hold. It’s exciting to be working in a time that modern open source software architectures and the power of cloud computing is eliminating obstacles and liberating giant ideas from giant budgets. About Derivitec Derivitec Ltd is a privately owned, UK based independent software vendor specialising in high performance, cost effective analytics for the derivatives industry. Founded in Dec 2011, the company have been working intensively towards cloud based solutions for risk and portfolio management. The Derivitec Risk Portal has been designed to allow users to start analysing risk on their derivatives portfolios in a matter of minutes. With industrial standard models and sanitised market data as standard, customers can focus on the business of business, while Derivitec concentrates on the business of risk. For an overview of MongoDB and its implementation on the AWS cloud platform read our guide. MongoDB on AWS: Guidelines and Best Practices About the Author - George Kaye George is the CEO and founder of Derivitec.

February 19, 2016
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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