MongoDB Doubles Down on Aotearoa as Part of Continued APAC Expansion
MongoDB is expanding its business in New Zealand to help Kiwi organisations build modern applications and take advantage of the AI opportunity that exists today. With hundreds of customers already in Aotearoa, including Pathfinder, Rapido, and Tourism Holdings, we're continuing to hire and invest to continue to grow our community in the country. Powering the next generation of modern applications Interest and excitement in AI, and particularly generative AI, has exploded. With a proud history of Innovation, it's not a surprise that many New Zealand companies are early adopters of this incredible technology. In fact, an AI Forum report has revealed that AI has the potential to increase New Zealand's GDP by as much as $54 billion by 2035. No matter what you think of the veracity of those bold predictions, one thing is sure: Almost every company is trying to figure out how to take advantage of data and software, to help them build better products, more efficiently and more quickly. Jake McInteer speaking at MongoDB.local Auckland As organisations transform into digital-first businesses, they’re faced with a growing list of application and data requirements. Modern applications are complex – they need to handle transactional workloads, app-driven analytics, full-text search, AI-enhanced experiences, stream data processing, and more. Companies are being asked to do this all while reducing data infrastructure sprawl, complexity and often also cut costs. What we are seeing globally is our developer data platform solves this challenge and complexity since it integrates all of the data services organisations need to build modern applications in a unified developer experience. Additionally, we also allow our customers to easily run anywhere in the world with over 110+ locations making us uniquely placed to enable Kiwi companies to adapt to a multicloud future. We also have strong local partnerships with all three cloud hyperscalers, all of which plan to open new cloud regions in New Zealand in the coming years. With the support of our cloud partners, in New Zealand we've already seen great adoption of MongoDB Atlas, including the largest established enterprises, through to cutting-edge startups. Here are a couple of examples. Pathfinder: Protecting vulnerable children Pathfinder , headquartered in Auckland, is a global leader in software development specialising in protecting vulnerable children. The company's mission centres on empowering law enforcement agencies with state-of-the-art technology, meticulously designed to combat the reprehensible crime of child exploitation. "We are committed to delivering investigators the most advanced tools. We cannot accept delays in removing a child from harm due to investigations being overwhelmed by large amounts of disparate data. In situations where every minute impacts a child's well-being, these tools must enable investigators to swiftly navigate data challenges, and rapidly apprehend perpetrators" said Bree Atkinson, CEO of Pathfinder Labs. Pathfinder’s Paradigm service is being built on MongoDB Atlas, running on AWS, and takes advantage of the wider developer data platform features in order to enable the next generation of data-driven investigative capabilities. By using MongoDB Atlas Vector Search , a native part of the MongoDB Atlas platform, the Pathfinder team are also able to match images and details within images (such as people and objects), classify documents and text, and build better search experiences for their users via semantic search. This enables Paradigm to efficiently aid law enforcement in identifying victims and apprehending offenders. Bree Atkinson, CEO of Pathfinder Labs, and Peter Pilly, DevOps Architect at Pathfinder Labs, with the MongoDB team in Auckland at the recent .local event "MongoDB Atlas allows our team to focus on our strengths: developing outstanding technology. It works with us not against us, enhancing integration which enables us to build better user experiences," said Peter Pilley, DevOps Architect at Pathfinder Labs. "Take MongoDB Atlas Vector Search, for example. Before MongoDB, we would have needed to incorporate multiple tools to achieve that functionality. Now we can handle it all from a single platform removing complexity and architecture that wasn't needed. With MongoDB Atlas, we're able to make data-driven decisions swiftly, boosting our productivity and decision-making speed." Peter's team at Pathfinder also uses MongoDB's performance advisor. They say it's like having an extra team member who suggests the best indexes for accessing their data, which is critical in an industry where getting to a specific piece of data could make all the difference. Rapido: Optimising B2B revenue and distribution Rapido has been utilising MongoDB Atlas for over five years. The team was originally part of MongoDB for Startups , a programme that offers startups free credits and technical advice to help them build faster and scale further. Their eagerness to adopt new technologies has enabled them to effectively harness MongoDB Atlas's evolving features. Working with the Accredo ERP system, Rapido has harnessed MongoDB Atlas to innovate in business-to-business (B2B) transactions. Using features like MongoDB Atlas Vector Search, the ' moreLikeThis ' operator, and MongoDB App Services, they've transformed business interactions, offering precise product recommendations and improved real-time visibility via change streams. Rapido's platform, which has processed orders collectively worth more than $100m to date, is essential for many wholesale businesses in New Zealand. Adam Holt, CEO of Rapido, summarises their experience: "Our journey with MongoDB Atlas has been transformative. By building on a cohesive developer data platform, we don't need to bolt-on and learn special technologies for every requirement. Continuously integrating new features keeps our platform advanced in the fast-paced B2B market. It's about leveraging technology to innovate and deliver better solutions to our clients." MongoDB expands in Aotearoa The increased demand from Kiwi organisations who are looking to innovate faster and take advantage of cutting-edge technologies, like AI, means MongoDB is now doubling down on its New Zealand footprint. Earlier this month, MongoDB established its local operations in Aotearoa, New Zealand. Jake McInteer , a native Kiwi, has officially transferred from MongoDB’s Australia business to lead the organisation in New Zealand. MongoDB already has a large, engaged community, more than 200 customers, and an extensive partner network. CEO of Lumin Max Ferguson presents at the Christchurch MongoDB user group We are incredibly excited about the opportunity to invest in and contribute to the Kiwi tech ecosystem, both to support local companies and help kiwi startups like Lumin and Marsello as well as established companies like Tourism Holdings , Figured , and Foster Moore . To support our growth, we have roles open on our Sales and Solutions Architecture team. If you are based in NZ and interested in joining our incredible team, working in our hybrid environment, please check out and apply for the roles here: Enterprise Account Executive, Acquisition Senior Solutions Architect Additionally, read here about the massive opportunity at MongoDB in APAC from our SVP Simon Eid.
How Atlas Edge Server Bridges the Gap Between Connected Retail Store and the Cloud
Efficient operations and personalized customer experiences are essential for the success of retail businesses. In today's competitive retail industry, retailers need to streamline their operations, optimize inventory management, and personalize the customer experience to stay ahead. In a recent announcement at MongoDB .local London, we unveiled the private preview of MongoDB Atlas Edge Server , offering a powerful platform that empowers retailers to achieve their goals, even when low or intermittent connectivity issues may arise. What is edge computing, and why is it so relevant for retail? The retail industry's growing investment in edge computing, projected to reach $208 billion by 2023, confirms the strategic shift retailers are willing to take to reach new markets and enhance their offers. And for good reason — in scenarios where connectivity is unreliable, edge computing allows operations to continue uninterrupted. Edge computing is a strategic technology approach that brings computational power closer to where data is generated and processed, such as in physical retail stores or warehouses. Instead of relying solely on centralized data centers, edge computing deploys distributed computing resources at the edge of the network. The evolution of investments in edge computing reflects a journey from initial hesitation to accelerated growth. As edge computing continues to mature and demonstrate its value, retailers are likely to further embrace and expand their focus in bringing applications where the computing and data is as close as possible to the location where it's being used. Let’s dig into how MongoDB addresses the current challenges any retailer would experience when deploying or enhancing in-store servers using edge computing. Connected store: How MongoDB's versatile deployment from edge to cloud powers critical retail applicationsCurrently, many retail stores operate with an on-site server in place acting as the backbone for several critical applications within the store ecosystem. Having an on-site server means that the data doesn't have to travel over long distances to be processed, which can significantly reduce latency. This setup can often also be more reliable, as it doesn't depend on internet connectivity. If the internet goes down, the store can continue to operate since the essential services are running on the local network. This is crucial for applications that require real-time access to data, such as point-of-sale (POS) systems, inventory management, and workforce-enablement apps for customer service. The need for sync: Seamless edge-to-cloud integration The main driver for retailers taking a hybrid approach is that they want to experience the low latency and reliability of an on-site server coupled with the scalability and power of cloud computing for their overall IT stack. The on-site server ensures that the devices and systems that are critical to sales floor operations — RFID tags and readers for stock management, mobile scanners for associates, and POS systems for efficient checkout — remain functional even with intermittent network connectivity. This data must be synced to the retailer’s cloud-based application stack so that they have a view of what’s happening across the stores. Traditionally this was done with an end-of-day batch job or nightly upload. The aim for the next generation of these architectures is to give real-time access to the same data set, seamlessly reflecting changes made server-side or in the cloud. This needs to be achieved without a lag from the store being pushed to the cloud and without creating complex data sync or conflict resolution that needs to be built and maintained. These complexities may cause discrepancies between the online and offline capabilities of the store's operations. It makes sense that for any retailer wanting to benefit from both edge and cloud computing, it must simplify its architecture and focus on delivering value-added features to delight the customer and differentiate from their competitors. Low-latency edge computing with Atlas Edge Server and its different components to achieve data consistency and accuracy across layers This is when Atlas Edge Server steps in to bridge the gap. Edge Server runs on-premises and handles sync between local devices and bi-directional sync between the edge server and Atlas. It not only provides a rapid and reliable in-store connection but also introduces a tiered synchronization mechanism, ensuring that data is efficiently synced with the cloud. These devices are interconnected through synchronized data layers from on-premises systems to the cloud, simplifying the creation of mobile apps thanks to Atlas Device SDK , which supports multiple programming languages, development frameworks, and cloud providers. Additionally, Atlas Device Sync automatically handles conflicts, eliminating the need to write complex conflict-resolution code. In the below diagram, you can see how the current architecture for a connected store with devices using Atlas Device SDK and Atlas Device Sync would work. This is an ideal solution for devices to sync to the Atlas backend. A high-level overview of the Architecture for connected devices in a retail space with MongoDB Device Sync and MongoDB Atlas when connectivity is unreliable. In a store with Atlas Edge Server, the devices sync to Atlas on-premises. All changes made on the edge or on the main application database are synced bidirectionally. If the store server goes offline or loses connectivity, the devices can still access the database and update it locally. The store can still run its operations normally. Then, when it comes back online, the changes on both sides (edge and cloud) are resolved, with conflict resolution built into the sync server. A high-level overview of the architecture for connected devices in a retail space with MongoDB Device Sync and MongoDB Atlas solving connectivity issues by implementing an on-premises Atlas Edge Server. Deploying Atlas Edge Server in-store turns connected stores into dynamic, customer-centric hubs of innovation. This transformation produces advantageous business outcomes including: Enhanced inventory management — The hybrid model facilitates real-time monitoring of logistics, enabling retailers to meticulously track stock in store as shipments come in and sales or orders are processed. By processing data locally and syncing with the cloud, retailers gain immediate insights, allowing for more precise inventory control and timely restocking. Seamless operational workflows — The reliability of edge computing ensures essential sales tools — like RFID systems, handheld scanners, workforce apps, and POS terminals — remain operational even during connectivity hiccups. Meanwhile, the cloud component helps ensure that all data is backed up and accessible for analysis, leading to more streamlined store operations. Customized shopping experiences — With the ability to analyze data on-the-spot (at the edge) and harness historical data from the cloud, retailers can create highly personalized shopping experiences. This approach enables real-time, tailored product recommendations and promotions, enhancing customer engagement and satisfaction. Conclusion With Atlas Edge Server, MongoDB is committed to meeting the precise needs of modern retail stores and their diverse use cases. Lacking the seamless synchronization of data between edge devices and the cloud, delivering offline functionality that enables modern, next-generation workforce applications, as well as in-store technologies like POS systems, is daunting. Retailers need ready-made solutions so they don't have to deal with the complexities of in-house, custom development. This approach allows them to channel their development efforts towards value-added, differentiating features that directly benefit their customers by improving their in-store operations. With this approach, we aim to empower retailers to deliver exceptional customer experiences and thrive in the ever-evolving retail landscape. Ready to revolutionize your retail operations with cutting-edge technology? Discover how MongoDB's Atlas Edge Server can transform your store into a dynamic, customer-centric hub. Don't let connectivity issues hold you back. Embrace the future of retail with Atlas Edge Server!
Kathreftis Launches World-Class Identity Access Management with Cymmetri
Security breaches and cyberattacks are more prevalent than ever. These attacks are often targeted at an organization's identity access management systems, with over 60% of cyber threats stemming from identity-based vulnerabilities. To address this critical issue, Kathreftis, an Indian startup, emerged in 2022 with a mission to create a world-class identity access management platform. At the heart of the venture lies the company's flagship product, Cymmetri, a comprehensive solution for identity access management and governance. The critical role of identity access management Cybersecurity threats are increasingly centered around exploiting weaknesses in identity access management, particularly attacking multi-factor authentication (MFA) systems. These attacks often involve compromised usernames and passwords, and they are on the rise. In response to this growing concern, Kathreftis' Founder & CEO, Vikas Jha, set out to address four key challenges when developing Cymmetri: Centralized identity management: The first challenge was to create a unified solution capable of managing all identities with access to various systems, including partners, outsourced services, and all privileged users, through a centralized administrative console. 360-degree visibility: The second challenge involved providing a 360-degree view of all access permissions. For any user with access privileges, Cymmetri shows which applications they can access, their assigned roles, and the level of permissions granted. Scalability and data management: The third challenge was handling the increasing data volume as an organization expands. As more data is generated and access privileges are granted, system performance may slow down. Cymmetri aimed to address these issues while ensuring optimal performance. High availability and scalability: The fourth challenge was to ensure that the identity access management platform remained highly available and horizontally scalable to meet the demands of a growing user base. Choosing the right database solution Selecting the appropriate database was critical to meet these challenges. Jha and his team decided to opt for a document database due to its ability to simplify data storage. Unlike relational databases, which involve complex tables, rows, and columns, document databases offered a more flexible and streamlined solution. MongoDB was the choice because of its versatility, supporting both on-premises and cloud deployment. Additionally, many of Kathreftis' developers were already familiar with MongoDB, facilitating rapid development and a quicker go-to-market strategy. This agility provided by MongoDB was a significant advantage for the company. Global compliance and accessibility To expand its reach, Kathreftis needed a database that would adhere to Indian data privacy laws while remaining adaptable to international markets. Jha emphasized that Cymmetri needed to accommodate varying regulatory environments. "We are located in India and we needed a database that would support Indian laws. But we also knew that, as we started to grow into markets like the Middle East, the U.K., and the U.S., we wanted something that wouldn't require major code changes," Jha explained. "Today, if you want to use Cymmetri in Australia, you just need to use the Australia cloud on AWS and Azure, and the system is ready to launch." Unlocking success with MongoDB for startups Cymmetri's journey to success was further aided by the MongoDB for Startups program, which offers valuable resources such as free MongoDB Atlas credits, technical guidance, co-marketing opportunities, and access to a network of partners with exclusive perks. The company used the free credits for proof of concept (POC) during its early stages, and MongoDB experts reviewed their architecture to ensure it met their requirements. Today, Cymmetri is predominantly used by large enterprises throughout India and the Middle East, including prominent financial services firms, public sector banks, manufacturing companies, cybersecurity organizations, and data resilience managed services providers. With Cymmetri, Kathreftis aims to simplify identity access management implementation, emphasizing ease of use and automation. The company strives to reduce the total cost of ownership for identity access management solutions, making them accessible to businesses of all sizes. In a digital world where security is paramount, Kathreftis and Cymmetri are at the forefront, reshaping how organizations manage and secure their identities. With their innovative solutions and global ambitions, they are poised to make a lasting impact on the world of identity access management.
Unleashing the Power of MongoDB Atlas and Amazon Web Services (AWS) for Innovative Applications
When you use MongoDB Atlas on AWS, you can focus on driving innovation and business value, instead of managing infrastructure. The combination of MongoDB Atlas, the premier developer data platform, and AWS, the largest global public cloud provider empowers organizations to create scalable and intelligent applications while streamlining their data infrastructure management. With MongoDB Atlas and AWS, building GenAI-powered applications is far simpler. MongoDB Vector Search enables developers to build intelligent applications powered by semantic search and generative AI over any type of data. Organizations can use their proprietary application data and vector embeddings to enhance foundation models like large language models (LLMs) via retrieval-augmented generation (RAG). This approach reduces hallucinations and delivers personalized user experiences while scaling applications seamlessly to meet evolving demands and maintaining top-tier security standards. MongoDB real-world use cases MongoDB helped Forbes accelerate provisioning, maintenance, and disaster-recovery times. Plus, the flexible data structures of MongoDB's document data model allows for faster development and innovation. In another example , a popular convenience store chain reported 99.995% uptime, freeing up its engineers and allowing them to focus on building innovative solutions thanks to Atlas Device Sync . Working with MongoDB helped functional food company MuscleChef transition from a food and beverage business with a website to a data-driven company that leverages customer insights to continuously improve and scale user experience, new product development, operations and logistics, marketing, and communications. Since working with MongoDB, repeat customer orders have surged 49%, purchase frequency saw a double-digit increase, and average order value is 50% higher than its largest competitors. Thousands of customers have been successful running MongoDB Atlas on the robust infrastructure offered by AWS. No-code enterprise application development platform Unqork helps businesses build apps rapidly without writing a line of code. Using MongoDB Atlas, the platform ingests data from multiple sources at scale and pushes it to applications and third-party services. Volvo Connect enables drivers and fleet managers to track trucks, activities, and even insights using a single administrative portal. The versatility and performance of Atlas combined with the AWS global cloud infrastructure helps the business connect critical aspects of their business in completely new ways. Verizon also opted to run Atlas on AWS to unlock the full power of its 5G mobile technology by moving compute elements to the network edge, making the user experience faster. A unified approach to data handling The Atlas developer data platform integrates all of the data services you need to build modern applications that are highly available, performant at global scale, and compliant with the most demanding security and privacy standards within a unified developer experience. With MongoDB Atlas running on AWS Global Cloud Infrastructure, organizations can leverage a single platform to store, manage, and process data at scale, allowing them to concentrate on building intelligent applications and driving business value. Atlas handles transactional data, app-driven analytics, full-text search, generative AI and vector search workloads, stream data processing, and more, all while reducing data infrastructure sprawl and complexity. MongoDB Atlas is available in 27 AWS regions. This allows organizations to deliver fast and consistent user experiences in any region and replicate data across multiple regions to reach end-users globally with high performance and low latency. Additionally, the ability to store data in specific zones ensures compliance with data sovereignty requirements. Security is paramount for both MongoDB Atlas and AWS. MongoDB Atlas is secure by default. It leverages built-in security features across your entire deployment. Atlas helps organizations comply with FedRAMP certification and regulations such as HIPAA, GDPR, PCI DSS, and more. It offers robust security measures , like our groundbreaking queryable encryption, which enables developers to run expressive queries on the encrypted data. MongoDB Atlas also enhances developer productivity with its fully managed developer data platform on AWS. It offers a unified interface/API for all data and application services, seamlessly integrating into development and deployment workflows. MongoDB Atlas also integrates with Amazon CodeWhisperer . This powerful combination accelerates developer innovation for a seamless coding experience, improved efficiency, and exceptional business growth. Conclusion MongoDB Atlas and AWS have worked together for almost a decade to offer a powerful solution for organizations looking to innovate and build intelligent applications. By simplifying data management, enhancing security, and providing a unified developer experience, they ensure that organizations can focus on what truly matters: driving innovation and delivering exceptional user experiences. If you're ready to get started, MongoDB Atlas is available in the AWS Marketplace, and you have the option to start with a free tier. Get started with MongoDB Atlas on AWS today .
Transforming Payments with Volante and MongoDB: A Modern Cloud Solution
In the ever-evolving world of banking and financial services, innovation and adaptability are key to success. Volante Technologies , a trusted cloud payments modernization partner to financial businesses worldwide, has been at the forefront of this transformation, empowering cloud-native payments solutions for over 125+ banks, financial institutions, and corporations across 35 countries to harness the power of digital payments and have the freedom to evolve and innovate at record speed. Volante's Payments as a Service and underlying low-code platform process millions of mission-critical transactions and trillions in value daily, so customers can focus on growing their business, not managing their technology. Real-time ready, API enabled, and ISO 20022 fluent, Volante’s solutions power four of the top five global corporate banks, two of the world’s largest card networks, and 66% of U.S. commercial deposits. In this customer story, we'll delve into how Volante, in partnership with MongoDB, has helped banks of all sizes modernize their payment systems, opening doors to new possibilities. The challenge Banks have long grappled with the constraints of monolithic infrastructure and legacy technologies that are a decade or more old and unable to handle the 24x7x365 digital demands of today's banking. In the fast-paced world of real-time payments – payments that clear and settle almost instantaneously using an underlying platform or network called payment rails – the need for speed and innovation is paramount. Corporate B2B banking, a lucrative revenue source, is also highly competitive. To win and retain customers, banks must offer improved and new payment services. Moreover, regulatory requirements demand that banks update their B2B payments systems to enable new payment rail standards such as FedNow and RTP as well as comply with new payment messaging standards like ISO20022 . The solution In response to these challenges, Volante Technologies, in partnership with MongoDB, introduced VolPay, a groundbreaking solution that redefined the way banks approach payments technology. This modern, cloud-native solution leverages MongoDB's cutting-edge technology to provide a modular, microservices API-based application. The benefits of this collaboration include: Modularity: Banks can now choose and integrate the services they need, making it a highly customizable solution. Innovative Tech Stack: By embracing modern technology, Volante's solution is resilient and able to meet the demands of today’s payments services and is future-proof as the landscape continues to evolve. Cloud Native: The solution is designed to operate in the cloud, enabling rapid deployment and scalability. Real-time: With real-time capabilities, banks can deliver 24x7x365 customer experiences that are critical in today's fast-paced digital world. Easy Integration and Extension: Volante's solution is easy to integrate with existing systems and extend as needed. Lower Total Cost of Ownership (TCO): The solution eliminates the need for costly "oil tanker" license upgrades, reducing both costs and implementation time. Global Connectivity: Banks can expand into new markets by connecting to over 100 global clearing and settlement schemes. MongoDB plays a crucial role in Volante's solution, providing a robust foundation for reading data, and ensuring high performance, scalability, and availability. The result MongoDB underpins the VolPay solution , a pioneering approach to payments technology. Unlike the monolithic systems of the past or generic middleware solutions, VolPay is an interoperable ecosystem of business services designed for payments innovation and transformation across the entire payments lifecycle including: Real-Time / Instant Payments, Global and Domestic Payments, ISO 20022 standardization, and more. Over 125+ global financial institutions take advantage of the cloud-native, API-ready solution. Built on a microservices architecture, VolPay is inherently real-time and ready to meet the demands of today's fast-paced payments environment. VolPay is available for deployment in various configurations across an organization’s modernization journey, from on-premise data centers to public cloud instances on major platforms like Microsoft Azure and AWS. Additionally, it is offered as a SaaS-managed service called " Payments-as-a-Service ." Customers looking to support their critical workloads in a self-managed environment can utilize MongoDB Enterprise Advanced , a comprehensive suite of products and services that put engineering teams in control of their self-managed MongoDB database, helping them drive security, performance, and operational efficiency. Those leveraging VolPay in the cloud can leverage the most advanced multi-cloud database service on the market – MongoDB Atlas – with unmatched data distribution and mobility, built-in automation for resource and workload optimization, and so much more. From on premises, to hybrid cloud and multi-cloud, MongoDB Enterprise Advanced and MongoDB Atlas deliver the scalability, high availability, and deployment flexibility today’s applications demand. In this transformative landscape, MongoDB plays a critical role as the archival (read) and transactional (write) database, ensuring performance, scalability, and high availability to meet the demanding transaction-per-second (tps) requirements of banks. In conclusion, the collaboration between Volante Technologies and MongoDB has ushered in a new era of payments technology, enabling banks to stay ahead of the curve and provide their customers with innovative, real-time payment experiences. This partnership has demonstrated that modern, cloud-native solutions can be implemented in a matter of months, offering a cost-effective and efficient alternative to the traditional, cumbersome systems that have held banks back for far too long. The future of payments is here, and it's being shaped by innovators like Volante and MongoDB. If you would like to learn more about why leading banks and payment providers choose Volante and MongoDB, take a look at the below resources: Volante Payments MongoDB for Payments Payments modernization – architectures shaping the future Volante Payments as a Service
MongoDB Atlas for Industries: Driving Retail Innovation from Supply Chain to Checkout
As retailers around the globe adopt a golden opportunity to modernize customer experiences with AI, personalization, and edge-based inventory management, MongoDB is launching a new initiative to boost the retail industry’s pace of innovation with data-driven applications. Today at MongoDB.local Paris , we announced the launch of MongoDB Atlas for Retail , a new innovation accelerator that provides expert-led innovation workshops, tailored technology partnerships, and an industry-specific knowledge incubator that builds customized training paths for customers. MongoDB Atlas for Retail spans advanced retail-industry use cases, including personalized e-commerce, omnichannel inventory management, workforce and in-store devices, AI-optimized supply chain, and sustainable data operations for future growth. In the modern and hyper-competitive global retail industry, enterprises are challenged with surging consumer demand, with global e-commerce sales expected to grow 56% by 2026. To keep up with modern industry standards, retailers are moving their data to the cloud and unifying siloed architectures by adopting MongoDB Atlas. “In today’s retail industry landscape, consumers expect smart search solutions, personalization, and real-time inventory management that creates seamless shopping experiences across mobile, web, and brick-and-mortar stores,” said Boris Bialek, Field CTO of Industry Solutions at MongoDB. “With MongoDB, enterprises are improving shopping experiences and preparing for the surge of growth ahead with pioneering e-commerce solutions while slashing costs with real-time analytics in the supply chain. MongoDB Atlas for Retail enables retailers to use their data for e-commerce growth while delighting customers with application-driven insights through a unified, fully managed, and cost-effective platform.” Global growth: From holiday surges to growing e-commerce consumer demand MongoDB is partnering with retailers globally to drive sustainable growth, profitability, and technology innovation. In one example, by moving its order management system from on-premises to a fully managed, cloud-based data platform, Radial boosted its ability to process $150 million in sales in a single day. Radial works with the world’s biggest brands and has grown its reputation as a trusted fulfillment provider for nearly 35 years, and also won a 2023 MongoDB Innovation Award . After selecting the MongoDB Atlas developer data platform, Radial improved performance, reduced latency, and developed real-time solutions for customers. “As a retail platform, we look for every opportunity to expand into the cloud. It offers the scalability we need to accommodate those seasonal peaks without creating complexity or escalating costs,” explains Eric Lutts, Senior Director of Database Engineering at Radial. “The support we get from MongoDB is second to none; amazing customer service is its DNA. I have experience with a lot of other database vendors, and what sets MongoDB apart is that they don’t just help, they educate so we can get the most from the platform in the future.” In partnership with public cloud providers like Google Cloud and the MACH Alliance pioneers at commercetools, MongoDB is also working behind the scenes with the largest beauty retailer in the U.S., Ulta Beauty , to seamlessly manage seasonal demand surges during holiday shopping. “We recently had an unplanned traffic surge that impacted our domain services. It took less than an hour for MongoDB Atlas to scale up to the next level of the cluster and manage that traffic,” says Sethu Madhav Vure, IT Architect, Ulta Beauty. “The on-demand, dynamic scaling, plus GKE, has saved the day more than once.” The keys to MongoDB retail solutions are speed and scalability. Take the German food delivery service Delivery Hero for example. With up to 12,000 requests per second, MongoDB Atlas enables a seamless web and mobile ordering experience for 2.2 billion multinational customers across 70 countries. Services like Delivery Hero need to be able to help customers quickly and easily find the foods they are craving. To handle random access requests at scale, the team considered using Elasticsearch, which would have added another database system for the group to maintain. But with MongoDB Atlas Search , Delivery Hero implemented the new features it needed in less than two weeks, versus three months with an outside vendor. “MongoDB Atlas Search was a game changer. We ran a proof of concept and discovered how easy it is to use. We can index in one click, and because it’s a feature of MongoDB, we know data is always up-to-date and accurate,” said Andrii Hrachov, principal software engineer at Delivery Hero. Build together: How MongoDB leverages tech partnerships to accelerate retail innovation From supply chain to checkout, MongoDB builds modern retail customer experiences with a strong support system. Today, it’s easier than ever for retailers and tech-forward e-commerce firms to find the right fit for online and mobile shopping platforms. Whether MongoDB customers are looking for a system integrator, a public cloud offering, or a built-in e-commerce platform, MongoDB has a vast network of partners at the ready. We're excited to announce that the MongoDB Partner Ecosystem Catalog has just launched alongside MongoDB Atlas for Retail. With this new tool, customers can easily discover what the MongoDB Partner Ecosystem has to offer. MongoDB works with more than 1,000 partners around the globe. Customers are now able to explore these partners and those who are part of the new AI innovators program. Using this discovery tool, customers can explore and filter the MongoDB Partner Ecosystem based on use case and industry, and discover how competitors and existing MongoDB customers are working together with the ecosystem. Start your journey with MongoDB Atlas for Retail MongoDB Atlas for Retail includes solutions and capabilities that can help organizations reimagine how they interact with end users by deploying data-driven applications with the flexibility, security, and resilience the retail industry requires: Run data-driven applications anywhere: MongoDB Atlas for the Edge enables organizations to deploy applications closer to where data is generated, processed, and stored, across mobile and IoT devices, on-premises servers, and multiple major cloud providers. For example, retail organizations can seamlessly deploy applications across mobile devices and on-premises services for interacting with customers when internet connectivity is unavailable while also ensuring data is synchronized with the cloud once connectivity is available. MongoDB Atlas for the Edge enables organizations to build, deploy, and manage applications that are accessible virtually anywhere without the complexity typically associated with operating distributed applications at the edge. Reimagine possibilities with innovation workshops: MongoDB Atlas for Retail includes dedicated executive engagement with industry experts from MongoDB and the MongoDB Partner Ecosystem to ideate client-specific solutions using best practices developed through proven industry experience. Retail innovation workshops are tailored to address the unique challenges and opportunities that organizations face so they can modernize their operations with security and compliance in mind. Jumpstart application development: With MongoDB Atlas for Retail, organizations can engage with the MongoDB Professional Services team to take advantage of retail expertise and accelerate projects from concept to prototype and to production in less time. Experts from the MongoDB Professional Services team can work backward from an organization’s specific challenges to conduct architectural reviews and help quickly prototype proofs of concept for ideation before moving new applications to production. Upskill teams to quickly build modern applications: MongoDB Atlas for Retail provides tailored MongoDB University courses and learning materials, including unlimited access to curated webinars and solutions sessions, to help developers learn how to quickly build modern applications for retail. Organizations can benefit from training new and experienced developers from the ground up on how to build modern, data-driven applications to modernize operations and reimagine end-user experiences. Power retail innovation and digital transformation across your organization by signing up to learn more about MongoDB Atlas for Retail, or visit us at booth E96 at Tech for Retail in Paris, November 28-29, 2023, or booth 1045 at NRF, Retail’s Big Show , in New York, January 13-16, 2024.
Darwinbox Scales Up and Out with MongoDB
Tech startup Darwinbox has seen exponential growth since its first line of code was written in 2015. Today, the HR Software-as-a-Service (SaaS) platform handles all employee interactions from recruitment to retirement, is present in 116 countries, and has over 850 customers and more than 2.2 million daily users. What’s more, Darwinbox has been recognized as the number one human capital management (HCM) solution by Gartner and secured investor backing from such big-name players as Sequoia, TCB, Salesforce, and Lightspeed. "The journey," says Shailendra Gupta, Director of Engineering at Darwinbox – with zero understatement – "has been fantastic." Shailendra and colleague, Brahma Prakash Chary Deegutla, Senior Director – Engineering, Product Development at Darwinbox presented at MongoDB.local Delhi where they shared more of that "fantastic journey" and how MongoDB fits into it. On top of Darwinbox sits a robust framework that gives HR leaders a holistic view of employees and the organization. An AI-powered chatbot provides a single interface whereby employees can interact with the system. If someone wants to request leave, access travel policy documents, or submit expenses–whatever the HR representative needs –the chatbot can help meet it, 24/7. With 2.2 million plus employees accessing the portal the volume of data generated is massive; over 250TB of data is in the system. While the pace at which Darwinbox is growing into different geographies leads to an even greater increase in data volumes and users, prompting this ambitious tech company to ask; "How do we address the data locality, and how do we address scaling?" "The answer," says Brahma, "is MongoDB." Given the multidimensional nature of the data and the scale at which it’s growing, Darwinbox needed, "A flexible schema, good scalability, robust security, and documents-based storage–and all this comes as a package with MongoDB Atlas ." "MongoDB, a document-search, document-based database, with capabilities for the aggregation framework, delivers amazing performance and it’s helping us provide near real-time insights to HR leaders." Before MongoDB Atlas, provisioning and managing the instances had to be done by the Darwinbox team; setting up configurations, upgrades, monitoring, backups, and restorations were all done internally – consuming considerable time and human resources. "With MongoDB Atlas, all these problems are solved" says Shailendra, "The architecture is lean, the search facility is quick, the managed service is amazing." And the flexibility afforded by MongoDB brings added value to a fast-growing and changing company. "MongoDB makes it very easy to adapt for any new changes or customizations," says Brahma. "MongoDB has become an integral part of our infrastructure," he adds, "enabling us for our growth expansion and business operations."
Every Operational Data Layer (ODL) Can Benefit From Search
In today's digital landscape, organizations frequently encounter the daunting challenge of managing complex data architectures. Multiple systems, diverse technologies, and a variety of programming languages become entwined, making smooth operations a significant struggle. A frequent example of this issue is seen in some major banks still relying on a banking system built in the 1970s, continuing to run on a mainframe with minimal updates. The consequence is a complex architecture as seen in Figure 1, where data is scattered across various systems, creating inefficiencies and hindering seamless operations. Offloading the data from one or more monolithic systems is a well-proven approach to increase agility and deliver new innovative services to external and internal customers. In this blog we will speak about how search can make Operational Data Layers (ODL) – an architectural pattern that centrally integrates and organizes siloed enterprise data, making it available to consuming applications – an even more powerful tool. Figure 1: Complex Data Architecture Operational Data Store (ODS) as a solution To tackle the complexities of their existing data architecture, organizations have turned to Operational Data Stores (ODS). An ODS serves as a secondary data store, holding data replicated of primary transactional systems as seen in Figure 2. Organizations can feed their ODS with change data capture technologies. Figure 2: Conceptual model of an Operational Data Layer The evolutionary path of adoption Implementing an ODS requires a thoughtful approach that aligns with the organization's digital transformation journey. Typically, the adoption path consists of several stages as seen in Figure 3. Initially, organizations focus on extracting data from one system into their Operational Data Store, allowing them to operate on a more unified dataset. Gradually, they can retire legacy systems and eliminate the need for intermediate data streams. The key benefit of this incremental approach is that it delivers value (e.g. offloading mainframe operations) to the business at every step by eliminating the need for a complete overhaul and minimizing disruption. Figure 3: Evolution of a basic ODS into a system of records Areas of application ODS are used to support the business in three different ways: Data Access Layers allow organizations to free their data from the limitations imposed by data silos and technological variations. Organizations consolidate data from different sources that often use different data storage technologies and paradigms, creating a unified view that simplifies data access and analysis. This pattern is mainly used to enable modern APIs, speed up development of new customer services, and improve responsiveness and resiliency. Operational Data Layer (ODL): The ODL is an internal-focused layer that aids in complex processing workflows. It serves as a hub for orchestrating and managing data across various stages of processing. The ODL empowers organizations to enrich and improve data iteratively, resulting in more powerful and accurate insights. It provides a holistic view of data and process information, an improved customer experience, and reduced operational costs. Developer ODL: Building a developer-focused ODL can provide significant advantages during the development cycle. By making data readily available to developers, organizations can accelerate the development process and gain a comprehensive understanding of their data structures. This, in turn, helps in identifying and addressing issues early on, leading to improved data models and better system performance. In a nutshell, this pattern helps reduce developer training time, streamlines development and speeds up testing and test automation. The power of search in ODS So how can every ODL benefit from search capabilities and how can MongoDB Atlas Search help? Atlas Search plays a crucial role in maximizing the value of an ODS. When we have questions or are searching for an answer, our natural interaction with information is primarily through search. We excel at interpreting imprecise queries and extracting relevant information from vast datasets. By incorporating search capabilities with Atlas Search into an ODS, organizations can empower their users to explore, analyze, and gain valuable insights from their data. Consider the example of a banking organization with a complex web of interconnected systems. Searching for specific transactions or identifying patterns becomes a daunting task, especially when dealing with numeric identifiers across multiple systems. Traditionally, this involved manual effort and navigating through numerous systems. However, with a search-enabled ODS, users can quickly query the relevant data and retrieve candidate matches. This greatly streamlines the process, saves time, and enhances efficiency. Practical examples: Leveraging ODS and Atlas Search Let's explore a few practical examples that demonstrate the power of ODS and the Atlas Search functionality. Operational Data Layer for Payments Processing: A financial institution implemented an ODS-based operational layer for processing payments. By aggregating data from multiple sources and leveraging search capabilities, they achieved faster and more accurate payment processing. This enabled them to investigate issues, ensure consistency, and deliver a superior customer experience. Customer 360 View: Another organization leveraged an ODS to create a comprehensive view of their customers, empowering relationship managers and bank tellers with a holistic understanding. With search functionality, they could quickly locate customer information across various systems, saving time and improving customer service. Post-trade Trading Platform: A global broker operating across 25 different exchanges utilized an ODS to power their post-trade trading platform. By leveraging search capabilities, they simplified the retrieval of data from various systems, leading to efficient and reliable trading operations. Conclusion In the dynamic world of data management, Operational Data Stores (ODS) have emerged as a crucial component for organizations seeking to streamline their data architectures. By adopting an incremental approach and leveraging search functionality such as Atlas Search , organizations can enhance data accessibility, improve operational efficiency, and drive valuable insights. The power of search within ODS lies in its ability to simplify data retrieval, accelerate development cycles, and enable users to interact with data in a more intuitive and efficient manner. By embracing these practices, organizations can unlock the true potential of their data, paving the way for a more productive and data-driven future. For more information on Atlas Search, check out the following resources: Watch this MongoDB.local talk which expands on this blog: Every ODS Needs Search: A Practical Primer Based on Client Experiences Discover MongoDB’s search functionalities Learn how Helvetia accelerates cloud-native modernization by 90% with MongoDB Atlas and MongoDB Atlas Search
Supercharging Edge-to-Cloud Strategy
The emergence of Big Data and the proliferation of AI/ML, is today more than ever, pushing enterprises' digital strategies to adopt more sophisticated systems that help them become data-driven organizations. This said the constant dependency on legacy systems makes it difficult for many enterprises to even access their edge data and make use of it in time to make operational/business decisions. From healthcare to retail, manufacturing to telecom, companies that can successfully adopt IoT into their operations have proven to grow significantly faster than laggards within their respective industries. Modern IoT solutions enable businesses to capture and visualize edge data in real time, resulting in rich insights into their operations. By bringing computing to the edge, they can also deploy a wide variety of applications that help them take action on critical data right then and there, delivering significant efficiency into operations. The emergence of generative AI tools is the disruptive force that has revolutionized business operations from a strategic and operational level. It has supercharged corporate data strategies by taking on the heavy lifting of processing/analysis and automating business activities by triggering reactions. This streamlining of operations has not only increased productivity but has also enabled faster and more efficient decision-making. By relieving technical and analytics teams of arduous tasks, these tools free up resources to focus on important creative aspects of business, unlocking meaningful business value with relatively low effort. Data from the edge plays a key role in improving a company's AI/ML strategies as it helps enrich their corporate models and improve associated outcomes. For this reason, it is imperative that enterprises modernize their edge-to-cloud stack with solutions that can be easily implemented and adaptable to their growing data needs. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Modernizing applications with MongoDB Successful modernization requires the right service provider with the expertise and right tools that can adapt to an organization's unique needs and business goals. MongoDB Atlas, AppServices, and Device Sync provide the infrastructure needed for enterprises to implement these modern solutions and start reaping their benefits. Below is a reference architecture for IoT solutions developed by WeKan that can be implemented across industries. Highlights of the architecture The solution is cloud-agnostic and compatible with services from any of the cloud providers (AWS, GCP, AZURE) Atlas Device SDK’s Data Ingest provides performant behavior for heavy client-side insert-only workloads of structured & unstructured data that is then streamed to Atlas with automatic clean-up Out-of-the-box synchronization allows seamless and secure transport of data from the device to the cloud using Atlas Device Sync Built-in conflict resolution with document and field-level permissions offers reliable bi-directional sync capabilities and ensures data consistency at all times Atlas Device SDK offers computing at the edge, allowing businesses to take action on field/telemetry data without the need for connectivity to the cloud MongoDB’s native time series collections, with hands-free schema optimization, support high-efficiency storage and low-latency querying Change streams allow applications to access real time data changes in the database without any complexity or risk, allowing IoT applications to subscribe to all data changes in real-time and action on them as needed MongoDB’s Schema Flexibility delivers agility to the business as engineers can seamlessly make changes and additions to the schema without downtime MongoDB Atlas, combined with industry-leading data warehousing solutions, enables businesses with first-in-class and real-time business intelligence capabilities MongoDB and WeKan Together, MongoDB and WeKan offer a powerhouse solution that combines the technical capabilities and the right expertise. Their solution streamlines the adoption process, making it easier and safer for customers to modernize their edge-to-cloud stack. Providing the right expertise and support with the Jumpstart Program: From architecture design to migration strategy definition and implementation support. A mixed team of specialists from MongoDB and WeKan works hand in hand with customers to ensure quick and correct implementation of the MongoDB technology. Offering the right tooling to accelerate time to market: WeKan's Migration Acceleration suite offers full DataBase & Application code analysis to best prepare for the migration and MongoDB's relational Migrator helps accelerate the transformation and transport of data from RDBMS to MongoDB. Together, these tools help reduce overall migration efforts/costs by 70%. A highly specialized service at the best price-point: WeKan’s Global Delivery Model offers architecture design and implementation support at a competitive price-point, making it easier for enterprises to access the expertise needed to migrate away from legacy and safely implement modern solutions around MongoDB Here are a couple of examples of how these IOT solutions can be applied across multiple industries: Automotive Industry: Predictive maintenance with MongoDB & GCP Leveraging Atlas Device SDK and MongoDB Atlas helps automakers deploy applications that use real-time data to proactively detect failures and efficiently schedule maintenance events. Vehicle telemetry data is stored in Atlas Device SDK (onboard) and synced using Atlas Device sync to MongoDB Atlas Smart factory telemetry data about their production lines are synced via MQTT to GCP Cloud Iot core and pub-sub back to MongoDB Atlas Data is transferred from MongoDB Atlas to a data warehouse/data lake house, such as Bigquery or Databricks, for analysis Predictive maintenance ML Models are executed on the data from the data warehouse to infer the assets that require maintenance in the near term These are then processed and stored back in MongoDB Atlas as tickets for further action These tickets are then assigned to users and synced to their mobile application using Atlas Device Sync Manufacturing - Industry 4.0 Leveraging Atlas Device SDK at the Edge and MongoDB Atlas, Manufacturers can seamlessly transport their factory data to the cloud, gain business insights, and action on it as needed. Sensors from the production lines in the manufacturing plant transmit telemetry data over MQTT to the local Atlas Device SDK Gateway about the customer orders being built The Atlas Device SDK Gateway sends the data in real-time via Atlas Device Sync back to MongoDB Atlas. With centralized information, customers, factory managers, and warehouse operators can all see real-time data about orders, inventory, and manufacturing timelines. Conclusion As enterprises grapple with the complexities of data management, real-time synchronization, scalability, and edge computing, the MongoDB and WeKan partnership offers powerful solutions to tackle these challenges head-on. Together, they help customers move away from legacy systems and implement complete edge-to-cloud solutions that harness the full potential of IoT for better data access, improved insights, and, ultimately, enhanced business outcomes.
Apono Streamlines Data Access with MongoDB Atlas
In today's world of ever-evolving cloud technology, many organizations are struggling to effectively manage data access. From companies that have no access policies in place and allow anyone to access any data, to those that have an existing solution but it's only on-premises, there's a desperate need for cloud-based access management. Apono is an easy-to-use platform that allows centralized access management, removing the trouble of having to depend on a single person to control access to the data. Apono brings reliable access management to the cloud, providing organizations with the security they need to protect their valuable information. And, as a member of the MongoDB for Startups program, Apono is accelerating its evolution as it seeks to expand its capabilities and its offering. MongoDB for Startups offers free MongoDB Atlas credits, one-on-one technical advice, co-marketing opportunities, and access to our vast partner network. Access that's as granular as you need it As organizations work to find the right balance of granular data access, they've often relied on a combination of workflow builders to make it happen. The way this often plays out is that just one person becomes the de facto expert in managing this system, leaving everyone else in the dark. And when they're gone, so is the expertise for managing ongoing access. Apono is a go-to solution for securely managing access to the most confidential and sensitive cloud resources businesses possess, from production environments to applications. It simplifies database access management across all three major cloud providers. A lot of database access management solutions only help with cluster access management, self-hosted databases, or cloud databases — but rarely not all of them. Apono enables organizations to manage access to database solutions whether they are self-hosted or in the cloud. Apono enables highly granular permissions, going beyond granting access to a cluster. It allows you to manage access to individual databases. In MongoDB Atlas, Apono goes as far as allowing you to manage access to individual collections. Apono is unique in its ability to offer that level of granular access management. Simplified and streamlined user experience From restricting read and write access to granting temporary permissions, Apono makes it easy for administrators to manage the entire process with a few clicks. According to the company's own internal data, about 80% of administrators are able to create access flows without any help in under two minutes. It's a very intuitive solution that also gives you full visibility into who is accessing or requesting access to resources and for how long. Administrators can choose how they want to interact with the Apono UX. They can use the intuitive administrator portal, the command line interface (CLI), Terraform, or the Apono API. From an end-user standpoint, Apono supports Slack, Teams, CLI, and a web portal with time-saving administrative features like request again and favorites. Additional time-savers include the ability to automate much of the process of granting permissions. Surprisingly, many organizations still handle permissions on an ad hoc basis through informal, one-off requests over text or email. Apono enables administrators to automate access flows, which not only saves time but is also more secure because it reduces the likelihood that someone will assign the wrong permission to a person or group by mistake. Apono also makes it easy to conduct access reviews, which are often required for regulatory purposes. These reviews can also be scheduled and automated so that reports are automatically shared with the stakeholders who need them. The security perimeter in the age of the cloud Back when most systems were primarily on-prem, it was critical to set up a security perimeter that limited access to anything behind the network firewall. Today, with remote work, cloud architectures, and the proliferation of edge devices, there is no longer one single firewall. Rather, identity has become the new security perimeter. "People work from anywhere, any IP, any device, even their phones. So it's becoming increasingly important to make sure that users have just the right amount of privileges," says Sharon Kisluk, Lead Product Manager at Apono. "If I give someone standing admin access to a cluster, what happens if they destroy the entire cluster by accident?" To prevent data loss due to human error or incorrect permissions, Apono works under the principle of least privilege, which means that any user or operation is allowed to access only the information and resources that are necessary for its legitimate purpose. That's why, out of the box, Apono gives you the ability to restrict all access to critical production environments. Multi-cloud access control The maturity of today's cloud computing has led to a large majority — around 87% — of companies to deploy to multiple cloud environments. Like MongoDB Atlas , Apono is available on all three major cloud platforms: AWS, Google Cloud, and Microsoft Azure. Also like MongoDB Atlas, Apono supports self-hosted Kubernetes. "We realized that people hate working with so many different role-based access control systems," says Kisluk. "Each system has its own user management. If you create policies or permissions in AWS, you have to do the same thing in Google Cloud and Azure if you're multi-cloud, and then you have to do the same thing for the databases." With Apono, you can create access flow bundles, which is a role abstraction that works across systems. For example, you can create a role called, "prod access" that enables you to access production databases and grant permission to only those who require access to those systems. And any system that's tagged as a production system will inherit those permissions, even if they're hosted by different cloud providers. Using MongoDB Atlas combined with Apono, administrators can establish global access policies and roll them out across the entire distributed system with just a few clicks. Product roadmap Apono was recently named to the Gartner Magic Quadrant for Privileged Access Management (PAM). While the recognition was unexpected at Apono, Kisluk says it just goes to show how Apono is truly the next thing in cloud PAM. Apono is expanding its cloud PAM by offering more complex access flow scenarios, or what is often referred to as, "if this, then that." These are scenarios that are triggered based on certain conditions being met. For example, if there's a production incident, you can grant access automatically for only the duration of the bug fix without submitting a special request. Get to know Apono Apono is a self-serve solution, so anyone can sign up with their email, connect to their cloud environment and database, and start using the product. Apono will also be at AWS re:Invent to be held in Las Vegas from November 27 to December 1. Don't forget to visit them and, of course, MongoDB and find out how these two powerful solutions are simplifying and streamlining privilege access management for developers and systems administrators. Sign up for our MongoDB for Startups program today!
Retrieval Augmented Generation (RAG): The Open-Book Test for GenAI
The release of ChatGPT in November 2022 marked a groundbreaking moment for AI, introducing the world to an entirely new realm of possibilities created by the fusion of generative AI and machine learning foundation models, or large language models (LLMs). In order to truly unlock the power of LLMs, organizations need to not only access the innovative commercial and open-source models but also feed them vast amounts of quality internal and up-to-date data. By combining a mix of proprietary and public data in the models, organizations can expect more accurate and relevant LLM responses that better mirror what's happening at the moment. The ideal way to do this today is by leveraging retrieval-augmented generation (RAG), a powerful approach in natural language processing (NLP) that combines information retrieval and text generation. Most people by now are familiar with the concept of prompt engineering, which is essentially augmenting prompts to direct the LLM to answer in a certain way. With RAG, you're augmenting prompts with proprietary data to direct the LLM to answer in a certain way based on contextual data. The retrieved information serves as a basis for generating coherent and contextually relevant text. This combination allows AI models to provide more accurate, informative, and context-aware responses to queries or prompts. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Applying retrieval-augmented generation (RAG) in the real world Let's use a stock quote as an example to illustrate the usefulness of retrieval-augmented generation in a real-world scenario. Since LLMs aren't trained on recent data like stock prices, the LLM will hallucinate and make up an answer or deflect from answering the question entirely. Using retrieval-augmented generation, you would first fetch the latest news snippets from a database (often using vector embeddings in a vector database or MongoDB Atlas Vector Search ) that contains the latest stock news. Then, you insert or "augment" these snippets into the LLM prompt. Finally, you instruct the LLM to reference the up-to-date stock news in answering the question. With RAG, because there is no retraining of the LLM required, the retrieval is very fast (sub 100 ms latency) and well-suited for real-time applications. Another common application of retrieval-augmented generation is in chatbots or question-answering systems. When a user asks a question, the system can use the retrieval mechanism to gather relevant information from a vast dataset, and then it generates a natural language response that incorporates the retrieved facts. RAG vs. fine-tuning Users will immediately bump up against the limits of GenAI anytime there's a question that requires information that sits outside the LLM's training corpus, resulting in hallucinations, inaccuracies, or deflection. RAG fills in the gaps in knowledge that the LLM wasn't trained on, essentially turning the question-answering task into an “open-book quiz,” which is easier and less complex than an open and unbounded question-answering task. Fine-tuning is another way to augment LLMs with custom data, but unlike RAG it's like giving it entirely new memories or a lobotomy. It's also time- and resource-intensive, generally not viable for grounding LLMs in a specific context, and especially unsuitable for highly volatile, time-sensitive information and personal data. Conclusion Retrieval-augmented generation can improve the quality of generated text by ensuring it's grounded in relevant, contextual, real-world knowledge. It can also help in scenarios where the AI model needs to access information that it wasn't trained on, making it particularly useful for tasks that require factual accuracy, such as research, customer support, or content generation. By leveraging RAG with your own proprietary data, you can better serve your current customers and give yourself a significant competitive edge with reliable, relevant, and accurate AI-generated output. To learn more about how Atlas helps organizations integrate and operationalize GenAI and LLM data, download our white paper, Embedding Generative AI and Advanced Search into your Apps with MongoDB . If you're interested in leveraging generative AI at your organization, reach out to us today and find out how we can help your digital transformation.
4 Key Considerations for Unlocking the Power of GenAI