MongoDB Atlas to Expand in the Middle East

MongoDB
June 24, 2022 | Updated: January 10, 2023

We’re proud to announce further expansion in the Middle East coming in 2023 with the launch of MongoDB Atlas on AWS in the United Arab Emirates (UAE) region. MongoDB Atlas is now available in 22 AWS regions around the world, including eight Asia Pacific regions and three Middle East and Africa regions.

The UAE region is an AWS Recommended Region, meaning it has three Availability Zones (AZ), bringing significant infrastructure to the Middle East. After the expansion in 2023, when you deploy a cluster in the UAE, Atlas automatically distributes replicas to the different AZs for higher availability. If there’s an outage in one zone, the Atlas cluster will automatically fail over to keep running in the other two. And you also will be able to deploy multi-region clusters with the same automatic failover built-in.

We’re delighted that — as with customers in Bahrain, Cape Town, and more — United Arab Emirates organizations will be able to keep data in their own country, delivering low-latency performance and ensuring confidence in data locality. We’re confident our UAE customers in government, financial services, and utilities in particular will appreciate this upcoming capability as they build tools to improve citizens’ lives and better serve their local users.

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MACH Aligned for Retail: API-First

Retailers must constantly evolve to meet growing customer expectations and remain competitive. Both their internal- and external-facing applications must be developed using principles that promote agility and innovation, moving away from siloed architectures. As discussed in the first article of this series , the MACH Alliance promotes the development of modern applications through open tech ecosystems. MACH is an acronym that represents Microservices, API-first, Cloud-native SaaS, and Headless. MongoDB is a proud member of the Alliance, providing retailers with the tools to build highly flexible and scalable applications. This is the second in a series of blog posts focused on MACH and how retail organizations can leverage this framework to gain a competitive advantage. In this article, we’ll discuss concepts relating to the second letter of MACH: API-first. Read the first post in this series, "MACH Aligned for Retail: Microservices." What is an API-first approach and why is it important? An application programming interface (API) is a set of routines, protocols, and tools that allow applications, or services within a microservices architecture, to talk to each other. APIs can be seen as messengers that deliver requests and responses. Applications built around APIs are said to be API-first. With this approach, the design and development of APIs come before the software implementation. Typically, an interface is created that is used to host and develop the API. The development team will then leverage the interface to build the rest of the application. This methodology enables developers to have access to specific functionalities of external applications or other microservices within the same application, depending on their needs. It promotes reusability because functionalities are interoperable with mobile and other client applications. In addition, applications developed with an API layer in mind can adapt to new requirements more easily because additional services and automation can be integrated into production when new requirements arise, therefore remaining competitive for longer. An API-first approach to developing applications The role of API-first in retail APIs play a crucial role in deeply interconnected systems that need to interface with other internal applications, third-party partners, and customers — all key areas when it comes to developing powerful retail applications. Think about how an e-commerce platform connects to the different systems making up the purchase process, such as inventory management, checkout, payment processing, shipping, and loyalty programs. The use of APIs is deeply interlinked with the concept of microservices . Software and data need to be decoupled to enable retailers to meet ever-increasing requirements, including omnichannel and cross-platform integration, seamless experiences across physical and online stores, and the ability to leverage real-time capabilities that enable differentiating features, such as live inventory updates and real-time analytics. APIs can be seen as a bridge for loosely coupled microservices to communicate with each other. Besides enabling a microservices architecture, an API-first approach offers the following additional benefits: Avoid duplication of efforts and accelerate time to market . Developers can work on multiple frontends at the same time, being confident that functionalities can be integrated by embedding the same APIs once ready. Think of multiple development teams working on an e-commerce web application, mobile portal, and internal inventory management system all at the same time. An API enabling the placement of a new order can be seamlessly leveraged by the web and mobile application and fed into the inventory management system to aid warehouse workers. Bug-fixing and feature enhancements can happen simultaneously, avoiding duplication of efforts and allowing new capabilities to be released to market more quickly. Reduce risks and operating costs . An API-first approach enables system stability and interoperability from the beginning because API efficiency is placed at the center of the development lifecycle and is no longer an afterthought once the application or functionality has been developed. This approach reduces the risk for retailers and saves money and effort in troubleshooting unstable systems. Enable new opportunities and scale faster . A flexible approach revolving around APIs provides more opportunities when it comes to integrating and refactoring the way different client applications and microservices communicate with each other, allowing retailers to improve and scale their IT offering in a fraction of the time. This approach also changes the way retailers can interact with external partners and do business with them since they can be provided with the tools to easily integrate with the retailer’s offering. Achieve language flexibility . Effective retailers need to have the capability to adapt their digital offering to different regions and languages. The plug-in capabilities of API-first allow developers to offer language-agnostic solutions that different microservices can integrate with, leveraging region-specific frontends. Steps to an API-first application What is the alternative? The four MACH Alliance principles combined (Microservices, API-first, Cloud-native SaaS, Headless) act as a disrupting force compared to the way applications were built until recently. Adapting to a new technology paradigm requires effort and a different developer mindset. But what was there before? From an API-first perspective, it can be said that the opposite is code-first. With this approach, application development starts in the integrated development environment (IDE), in which code is written and the software takes shape. Development teams know that they will need to build an interface to be able to interact with each function of the code, but it is seldom a priority; developing core functionalities takes precedence over the interface where those functionalities will be hosted and accessed. When the time comes for the interface to be developed, the code has already been defined. This means the API is developed around existing code rather than vice versa, which poses limitations. For example, developers might not be able to return data the way they want because of the underlying data schema. The code-first approach Bottlenecks can also occur as other teams requiring the API will need to wait until the code is finalized to be able to embed it in their underlying applications. Any delays in the software development lifecycle will hold them up and delay progress. Although a code-first approach might have worked in the past, it is no longer suitable for dealing with highly interconnected applications. Learn more about how MongoDB and MACH are changing the game for ecommerce. How MongoDB helps achieve an API-first approach Simply lifting and shifting monolithic applications to a microservice and API-first architecture will only provide minimal benefits if they are still supported by a relational data layer. This is where most of the bottlenecks occur. Changes to application functionalities will require constant refactoring of the database schemas, object-relational mapping (ORM), and refining at the microservice level. Moving to a modern MACH architecture requires a modern data platform that removes data silos. The MongoDB developer data platform provides a flexible data model, along with automation and scalability features to adapt to even the most challenging retail use cases and to multiple platforms (e.g., on-premises, cloud, mobile, and web applications). MongoDB Atlas, MongoDB’s fully managed cloud database, also provides capabilities to manage the data layer end to end via APIs, such as the MongoDB Atlas Data API . This is a REST-like, resilient API for accessing all Atlas data that enables CRUD operations and aggregations with instantly generated endpoints. This is a perfect answer to an API-first approach, since developers can access their data using the same principles leveraged to connect to other applications and services. The MongoDB Atlas Data API workflow MongoDB’s Atlas Data API provides several other benefits, allowing developers to: Build faster with developer-friendly data access. Developers work with a familiar, REST-like query and response format, no client-side drivers are necessary. Scale confidently with a resilient, fully managed API that reduces the operational complexity needed to start reading and writing your data. Integrate your MongoDB Atlas data seamlessly into any part of your stack — from microservices to analytics workloads. This article has provided only a sample of what can be leveraged via MongoDB’s APIs. The MongoDB Query API provides a comprehensive set of features to seamlessly work with data in a native, familiar way. It supports multiple index types, geospatial data, materialized views, full-text search, and much more. In the next part in this MongoDB and MACH Alliance series, we will discuss how a cloud-native SaaS architecture can enable full application flexibility and scalability. Read the first post in this series, "MACH Aligned for Retail: Microservices."

June 24, 2022
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Retrieval Augmented Generation for Claim Processing: Combining MongoDB Atlas Vector Search and Large Language Models

Following up on our previous blog, AI, Vectors, and the Future of Claims Processing: Why Insurance Needs to Understand The Power of Vector Databases , we’ll pick up the conversation right where we left it. We discussed extensively how Atlas Vector Search can benefit the claim process in insurance and briefly covered Retrieval Augmented Generation (RAG) and Large Language Models (LLMs). MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More One of the biggest challenges for claim adjusters is pulling and aggregating information from disparate systems and diverse data formats. PDFs of policy guidelines might be stored in a content-sharing platform, customer information locked in a legacy CRM, and claim-related pictures and voice reports in yet another tool. All of this data is not just fragmented across siloed sources and hard to find but also in formats that have been historically nearly impossible to index with traditional methods. Over the years, insurance companies have accumulated terabytes of unstructured data in their data stores but have failed to capitalize on the possibility of accessing and leveraging it to uncover business insights, deliver better customer experiences, and streamline operations. Some of our customers even admit they’re not fully aware of all the data in their archives. There’s a tremendous opportunity to leverage this unstructured data to benefit the insurer and its customers. Our image search post covered part of the solution to these challenges, opening the door to working more easily with unstructured data. RAG takes it a step further, integrating Atlas Vector Search and LLMs, thus allowing insurers to go beyond the limitations of baseline foundational models, making them context-aware by feeding them proprietary data. Figure 1 shows how the interaction works in practice: through a chat prompt, we can ask questions to the system, and the LLM returns answers to the user and shows what references it used to retrieve the information contained in the response. Great! We’ve got a nice UI, but how can we build an RAG application? Let’s open the hood and see what’s in it! Figure 1: UI of the claim adjuster RAG-powered chatbot Architecture and flow Before we start building our application, we need to ensure that our data is easily accessible and in one secure place. Operational Data Layers (ODLs) are the recommended pattern for wrangling data to create single views. This post walks the reader through the process of modernizing insurance data models with Relational Migrator, helping insurers migrate off legacy systems to create ODLs. Once the data is organized in our MongoDB collections and ready to be consumed, we can start architecting our solution. Building upon the schema developed in the image search post , we augment our documents by adding a few fields that will allow adjusters to ask more complex questions about the data and solve harder business challenges, such as resolving a claim in a fraction of the time with increased accuracy. Figure 2 shows the resulting document with two highlighted fields, “claimDescription” and its vector representation, “claimDescriptionEmbedding” . We can now create a Vector Search index on this array, a key step to facilitate retrieving the information fed to the LLM. Figure 2: document schema of the claim collection, the highlighted fields are used to retrieve the data that will be passed as context to the LLM Having prepared our data, building the RAG interaction is straightforward; refer to this GitHub repository for the implementation details. Here, we’ll just discuss the high-level architecture and the data flow, as shown in Figure 3 below: The user enters the prompt, a question in natural language. The prompt is vectorized and sent to Atlas Vector Search; similar documents are retrieved. The prompt and the retrieved documents are passed to the LLM as context. The LLM produces an answer to the user (in natural language), considering the context and the prompt. Figure 3: RAG architecture and interaction flow It is important to note how the semantics of the question are preserved throughout the different steps. The reference to “adverse weather” related accidents in the prompt is captured and passed to Atlas Vector Search, which surfaces claim documents whose claim description relates to similar concepts (e.g., rain) without needing to mention them explicitly. Finally, the LLM consumes the relevant documents to produce a context-aware question referencing rain, hail, and fire, as we’d expect based on the user's initial question. So what? To sum it all up, what’s the benefit of combining Atlas Vector Search and LLMs in a Claim Processing RAG application? Speed and accuracy: Having the data centrally organized and ready to be consumed by LLMs, adjusters can find all the necessary information in a fraction of the time. Flexibility: LLMs can answer a wide spectrum of questions, meaning applications require less upfront system design. There is no need to build custom APIs for each piece of information you’re trying to retrieve; just ask the LLM to do it for you. Natural interaction: Applications can be interrogated in plain English without programming skills or system training. Data accessibility: Insurers can finally leverage and explore unstructured data that was previously hard to access. Not just claim processing The same data model and architecture can serve additional personas and use cases within the organization: Customer Service: Operators can quickly pull customer data and answer complex questions without navigating different systems. For example, “Summarize this customer's past interactions,” “What coverages does this customer have?” or “What coverages can I recommend to this customer?” Customer self-service: Simplify your members’ experience by enabling them to ask questions themselves. For example, “My apartment is flooded. Am I covered?” or “How long do windshield repairs take on average?” Underwriting: Underwriters can quickly aggregate and summarize information, providing quotes in a fraction of the time. For example, “Summarize this customer claim history.” “I Am renewing a customer policy. What are the customer's current coverages? Pull everything related to the policy entity/customer. I need to get baseline info. Find relevant underwriting guidelines.” If you would like to discover more about Converged AI and Application Data Stores with MongoDB, take a look at the following resources: RAG for claim processing GitHub repository From Relational Databases to AI: An Insurance Data Modernization Journey Modernize your insurance data models with MongoDB and Relational Migrator

April 18, 2024