Ventana Research's Latest Report Highlights MongoDB's Role as a Cloud Data Platform Provider

MongoDB
January 12, 2022 | Updated: July 6, 2022

Ventana Research, a market advisory and research firm, recently published an Analyst Perspective on MongoDB, noting that the MongoDB data platform provides businesses the ability to accelerate development and data-driven decision-making.

As Ventana Research explains the evolution from traditional databases to modern, cloud-based platforms, the study covered multiple trends related to both the present and future of data platform software. We have identified six key trends from MongoDB that were represented in the Ventana Research Analyst Perspective.

Non-relational, or NoSQL, databases are on the rise. We see this as evidence of an unprecedented, widespread change in how businesses perceive and use their databases.
Cloud-based services and products are rapidly gaining popularity. Given the rise of real-time, data-driven applications, organizations are relying more and more on the flexibility, availability, and functionality of cloud-native data platforms. Such products are ideal for quickly building competitive products, delivering highly personalized experiences, and improving business agility.
As a result, operational database requirements will only become more demanding. As applications become more advanced, databases will become a pivotal part of an organization’s success — or failure. We believe that in order to keep up with their applications (and their competition), companies require a comprehensive, powerful platform like MongoDB Atlas.
Convergence is the name of the game. As companies seek out new and better operational data platforms, both relational and non-relational database providers will venture into areas that were traditionally dominated by their competitors. Examples include non-relational databases (like MongoDB) adding relational features like ACID transactions, or relational databases offering compatibility for non-relational data models like graphs or documents.
Companies are increasingly opting for hybrid and multi-cloud models. MongoDB Atlas’ multi-cloud clusters enable users to leverage exclusive provider features (like Google Cloud’s AI tools), improve availability in geographic regions, or migrate data across clouds with no downtime.
Non-relational, cloud-native databases are becoming more powerful — and more attractive to customers. Thanks to convergence and competition, non-relational databases are becoming ever more capable. Their advancements include real-time analytics, rich visualizations, and mobile data sync and storage.

Read Ventana Research Analyst Perspectives to gain insight into the current data landscape and the possibilities of tomorrow.

Updated January 17, 2022.

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Safe Software Deployments: Through the Looking Glass

We’ve covered a lot of ground in this Safe Software Deployment series, from the 180 Rule to Z Deployments to the Goldilocks Gauge. But there is an elephant in the room. Or should I say, a jabberwock. In Lewis Carroll’s novel Through the Looking Glass , Alice discovers that the mirror above her mantle is not a mirror at all, but a doorway to another world in which things work very differently. When developers push software from staging to production, they often have a similar experience. Though they want to believe that staging and production are the same, they discover that staging is not a mirror at all, and production is another world, in which things work very differently. And out of that distortion come bugs and outages. The bottom line is this: Staging ≠ Production. And it never will. There are simply too many variables between these two environments to ever achieve exact alignment. Those environments can be different in hardware: CPU cores, threads per core, cache size, microcode; bus architectures; memory size; firmware. Or different in software or configuration: OS versions; compilers; libraries; network traffic profiles. Or different in network topology, edge caching; DNS and directory services. And of course we know that no matter how diligent you test in staging, customer workloads exercise the software in different ways. But the major reason they are different is because both environments have had a different set of software deployed to them over time, with a different set of configuration parameters - and a different combination of patches, hacks, and rollbacks. That staging is a mirror of production is one of the great delusions of software development. Developers often tell this lie to themselves, to overcome their fear of pushing to prod. Worse, developers often know the truth, but don’t really know how to explain this ambiguity to their management chain, leading to inevitable trust issues when deployments fail. So what can we do about it? First, accept reality. Modern, distributed software systems are nonlinear, and all test environments are simulacrums. It’s particularly important to help managers understand that even if it were possible to create exact duplicates of production – which it’s not – it would be practically and financially unjustifiable. Second, approach testing like an actuary. The leap from staging to production is essentially an exercise in probability. You should know your architecture, operational characteristics, and costs well enough to prioritize tests and reduce risk. You may even want to create two or more test environments that are deliberately different to reduce the odds of failure. And you should continue to run tests after the release, so you can surface bugs before your customers do. Third, if you can, treat both production and staging like cattle, not pets. If you have an enlightened software organization, one that believes in best practices, set up your systems so that you can blow away and recreate both your production and staging environments at regular intervals. This will reset many of the deviations and environment drift that build up over time. Finally, you have to be able to do automated rollbacks (the 180 Rule ) which work reliably ( Z Deployments ), and that are the right size for optimum efficiency and safety (the Goldilocks Gauge . ;-) I saved this column for last because completely eliminating the differences between staging and production is not a solvable problem. And frankly, you shouldn’t even try. But you don’t want to get caught flat-footed either, so you need a system of best practices that greatly reduces risk and fosters confidence. In other words, a system of Safe Software Deployment that helps you overcome the fear of pushing to prod. And most importantly, everyone in your organization needs to have a common understanding of the problem, from the top down. So feel free to print this post out, slide it under the door of your manager, and slink away like that Cheshire Cat. Have another technique for managing the divergence between staging and production? Share it with me at @MarkLovesTech .

January 11, 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