Application Modernization with gravity9 And MongoDB Atlas: How Digital Decoupling Supports the Customer Offering

The goal of most organizations is pretty clear: to improve customer offerings and become more operationally efficient, streamlined and profitable. But is it possible for organizations to excel in an agile fashion when they are reliant upon legacy systems? It’s the age-old dilemma between risk and innovation. How can you mitigate the former while accelerating the latter?

Nearly all organizations operate with some type of legacy system in place that, often, is central to the operation of the business or the customer offering—i.e., one that would be highly costly and disruptive to move away from. To solve this predicament, the process of digital decoupling enables organizations to detach incrementally from legacy systems while acknowledging the critical role they often play.

In this blog, we’ll further explore the value of digital decoupling as well as introduce how gravity9 with MongoDB Atlas delivers the smoothest transition possible.

Why Not Simply Upgrade?

Digital decoupling is not a “big-bang” upgrade where one system is fully replaced by another overnight; rather, it allows for the continued existence of your legacy system as part of your digital architecture while simultaneously unlocking innovation. But why not simply upgrade? Isn’t “out with the old and in with the new” the faster route to take? Not always. When a big-bang upgrade focuses on the replacement of a legacy system that is central to a customer offering or business operations, it becomes a much more complex, risky, and time-intensive undertaking. Often, many months or years can pass before any value is delivered to customers. But while your organization is focusing time and effort on a long-term, large-scale system replacement initiative, your customers’ needs will be changing and your competitors will be continuing to innovate. Once your new system is finally ready to be deployed to the marketplace, there’s a good chance it may already be rendered obsolete.

Digital decoupling offers a faster, less risky, and more flexible alternative. The legacy system is maintained as the core of your business, but strategic portions are exposed through modern microservices to allow for the rapid creation of new digital products and offerings. The organization can utilize new, modern technologies to maintain the functionality of the legacy system while building a more advanced, digital architecture around it. By maintaining the existing legacy system, the organization significantly reduces disruption and risk while unlocking the ability to innovate new products on a rapid timescale.

How it Works

Applying a digital decoupling approach makes quickly innovating new digital products and services on top of your data possible by way of microservices and an event-driven architecture.

Microservice Architecture after Digital Decoupling

By utilizing event-driven architecture, individual systems and capabilities can be built as fully scalable microservices each with their own database, allowing solutions to be built around each microservice that can be combined to provide limitless additional capabilities and services for customers in a rapid and agile fashion. Digital decoupling creates a customer experience delivered via a modern, feature-rich UI or website that is intuitive, user friendly and continuously evolving, while the legacy system still operates behind the scenes.

After years of working with large organizations, the solutions architects at gravity9 have a deep understanding of event-driven architecture as a solution to digital decoupling.

Our adherence to domain-driven design is in our DNA, it is how we build solutions and is core to the way we work….We build event-driven microservices on top of monolithic legacy architecture.

Noel Ady, gravity9 Founding Partner.

By utilizing domain-driven design, system actions are communicated or triggered by way of an event, with colloquialized messages sent between the legacy application and the new architecture via a bus. An adaptor is created to sit in front of the legacy system and speak to your “new IT” in the language of events. This adaptor looks at the data in your legacy system and raises events when changes occur, then optionally writes back changes raised by other systems, allowing your legacy system to participate in the event-driven architecture. The use of APIs ensures the traffic is two-way and non-intrusive to the legacy application so that it can continue to operate as expected.

One of the key technology concerns related to adaptors for legacy systems is the concept of a “delta store.” Events in an event-driven architecture should contain the context for the event, often including the previous value, to help receiving systems properly respond to the event. In more modern systems it’s possible to get this data from webhooks or similar alternatives, but these mechanisms won’t exist in older legacy systems so a different approach via a delta store is needed.

A delta store will contain the history of changes on a value (the ‘deltas’) to allow the adaptor to properly construct the event context and to ensure that events are only raised for true changes in values.

Why MongoDB?

MongoDB’s flexible data schema makes it an excellent implementation technology for a delta store, allowing a dynamic mechanism that can flex to new data and event types on demand. gravity9 partners with MongoDB Atlas, MongoDB’s multi-cloud, secure and flexible database service, as an integral technology enabler of digital decoupling to increase flexibility of the resulting architecture. Importantly, Atlas also enhances reliability for mission-critical production databases with continuous backups and point-in-time recovery. It’s secure for sensitive data and automates key processes like infrastructure provisioning, setup and deployment so teams can access the database resources they need, when they need them. Best of all, MongoDB’s features and benefits help free up developer time so they can focus their talent on more innovative tasks.

What Should I do Next?

The logic is just as important as the physical in digital decoupling when it comes to modelling your events. Utilizing best-practice, domain-driven design alongside a proven approach is the key to success. Together, gravity9 and MongoDB have replicated this success time and time again, enabling organizations to lay the foundations for newer more modern architecture without the disruption of removing their legacy systems.

Interested in learning more about MongoDB’s Modernization Program? Contact us today!

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Built With MongoDB: Milky Way AI

Sagar Setu received his PhD in helicopter flight dynamics and has a fascination with deep learning and integration within the field of aerospace. However, helicopter flight dynamics is not what Sagar is involved in today. Through Entrepreneur First , an international program that helps entrepreneurs launch companies, Sagar met Eunice Wong , a fellow aspiring entrepreneur, who introduced Sagar to the world of retail, which he calls a “fantastic playground” for any engineer to be involved in. The pair founded Milky Way AI, with Wong as CEO and Sagar as CTO. Milky Way AI is designed to empower the largest retailers and brands globally with real-time visibility into how their products and their competitors’ products are being merchandised across thousands of stores. In this edition of #BuiltWithMongoDB, we chat with Sagar about the ways Milky Way AI creates opportunities for retailers, his favorite MongoDB features, and how the competitive AI industry keeps him motivated. MongoDB: What does the product look like now, and how does it work? Sagar: Our flagship product is called InstaShelf. It’s a mobile app that we put in the hands of distributors and merchandisers. When there is a person stocking the shelves and we put the app in the hands of that person, they are able to snap a photo, which then goes through our proprietary computer vision engine. This generates a variety of insights that are valuable for both the distributor and the brands. We are looking into how we can share this same data with and make it equally valuable to the retailers. We developed this product over the last year and launched our beta three months ago. Since then we have gotten quite good traction in terms of users in a number of countries that are deploying the product. MongoDB: Let's talk about that traction. How far along are you? Sagar: We started in January with a 15-store pilot for Kelloggs in Singapore. We have deployed across 150 stores now, and we are set to deploy across a few hundred more in Malaysia and the Philippines. By the end of next year, we hope to be in three more countries — just with Kelloggs. The typical number of users for each of our pilots is around 15 to 20 merchandisers visiting between 50 and 60 stores. In a typical audit, the user takes 10 to 15 photographs and our AI identifies what's on the shelf from these pictures, reporting on key metrics such as a brand’s share of shelf compared with a competitor brand, products that are out of stock, product placement compliance, and so forth. MongoDB: What does your tech stack consist of? Sagar: The web and mobile components of our solution are built using MongoDB, React Native, React, Node.js, and Python Flask. The computer vision pipeline is built on both TensorFlow and Pytorch. We use MongoDB for all our database requirements — transactional and analytical. Our top criteria for choosing the tech stack were proven scalability and stability, and a wide developer pool. It was important in the early stages to keep the team lean and the product flexible, and the choice of MongoDB Atlas turned out to be a great one. The support for being schemaless was crucial in allowing us to stay nimble as we learned the nitty-gritty of the domain. With features such as triggers and BI Connector, we could orchestrate various components of the solution right from the Atlas GUI, saving us hundreds of working hours. MongoDB: What are some of your favorite features in MongoDB? Sagar: My favorite feature is the support with autoscaling, which is the primary concern if you’re building anything into production. I’ve never had to worry about that. I don’t even think about it; I have just turned the features on, and it allows so much creativity. With MongoDB Atlas, I have peace of mind. MongoDB: What is something that you are learning right now? Sagar: Learning is a constant, working in the field of AI. A wonderful plus point is you always have so much competition: there might be a paper tomorrow that basically undoes everything you’ve done — something comes out that is far superior to the method you just took two months to deploy. So I’m always reading, learning, and trying to improve our solutions. MongoDB: What’s one of your favorite books? Sagar: The Selfish Gene . It’s not exactly technical, but more on the scientific side. That’s more of my kind of read. I really like the thought process the book instills in you. It gives you an understanding of the world — the good, the bad, and learning not to take things personally. Looking to build something cool? Get started with the MongoDB for Startups program.

May 26, 2021
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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