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Introducing Atlas for the Edge
September 26, 2023
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AI, Vectors, and the Future of Claims Processing: Why Insurance Needs to Understand The Power of Vector Databases
We’re just under a year since OpenAI released ChatGPT, unleashing a wave of hype, investment, and media frenzy around the potential of generative AI to transform how we do business and interact with the world. But while the majority of the investment dollars and media attention zeroed in on the disruptive capabilities of large language models (LLMs), there’s a crucial component underpinning this breakthrough technology that hasn’t received the attention it deserves; the humble vector database. Vector databases, a type of database that stores numeric representations (or vectors) of your data, allow advanced machine learning algorithms to make sense of unstructured data like images, sound, or unstructured text, and return relevant results. (You can read more about vector search databases and vector search on our Developer Hub .) For industries dealing with vast amounts of data, such as insurance, the potential impact of vector databases and vector search is immense. In this blog, we will focus on how vectors can speed up and increase the accuracy of claim adjustment. The claims process… vectorized! The process of claim adjustment is time-consuming and error-prone. As one insurance client recently told us, “If an adjuster touches it, we lose money.” For each claim, adjusters need to go through past claims from the client and related guidelines, which are usually scattered across multiple systems and formats, making it difficult to find relevant information and time-consuming to produce an accurate estimate of what needs to be paid. For this blog, let’s use the example of a car accident claim. In our example, a car just crashed into another vehicle. The driver gets out and starts taking pictures of the damage, uploading them to their car insurance app, where an adjuster receives the photos. Typically, the adjuster would painstakingly comb through past claims and parse guidelines to work up an estimate of the damage and process the claim. But with a vector database, the adjuster can simply ask an AI to “show me images similar to this crash,” and a Vector Search -powered system can return photos of car accidents with similar damage profiles from the claims history database. The adjuster is now able to quickly compare the car accident photos with the most relevant ones in the insurer's claim history. What’s more, with MongoDB it is possible to store vectors as arrays alongside existing fields in a document. In our car crash scenario, this means that our fictional adjuster can not only retrieve the most similar pictures but also have access to complementary information stored in the same database: claim notes, loss amount, car model, car manufacturing year, etc. The adjuster now has a comprehensive view of past accidents and how they were handled by the insurance company, in seconds. For this use case, we have focused on image search, but most data formats can be vectorized, including text and sound. This means that an adjuster could query using claim notes and find similar notes in the claim history or related paragraphs in the guidelines. Vector Search is an extremely powerful tool as it unlocks access to unstructured data that was previously hard to work with such as PDFs, images, or audio files. How does this work in practice? Let’s go through each step of the process: A search index is configured on an existing collection in MongoDB Atlas An image set is sent to an embedding model that generates the image vectors The vectors are then stored in Atlas, alongside the current metadata found in the collection Figure 1: A dataset of photos of past accidents is vectorised and stored in Atlas We run our query against the existing database and Vector Search returns the most similar images Figure 2: An image similarity query is performed, and the 5 top similar images are returned. Example user interface: A claim-adjuster dashboard leveraging Vector Search Figure 3: UI of the claim adjuster application We can go a step further and use our vectors to provide an LLM with the context necessary to generate more reliable and accurate outputs, also known as Retrieval Augmented Output (RAG). These outputs can include: Natural language processing for tasks such as chatbots and question-answering — think of a claim adjuster that interacts with a conversational interface and asks questions such as: “Give me the average of the loss amount for accidents related to one of the photos of claim XYZ” or “Summarize the content of the guidelines related to this accident” Computer vision and audio processing for image classification and object detection to speech recognition and translation Content generation, including creating text-based documentation, reports, and computer code, or converting text to an image or video Figure 4 brings together the workflow enabling RAG for the LLM. Figure 4: Dynamically combining your custom data with the LLM to generate reliable and relevant outputs If you’re interested in seeing how to do this in practice and start prototyping, check out our GitHub repository and dive right in! Go hands-on! Vector databases and vector search will transform how insurers do business. In this blog we have explored how vectors can be leveraged to speed up the work of claim adjusters, which directly translates to an improved customer experience and, crucially, cost savings through faster claims processing and enhanced accuracy. Elsewhere, vector search could be used for: Enhanced customer service. Imagine being able to instantly pull up comprehensive policyholder profiles, their claims history, and any related information with a simple search. Vector search makes this possible, facilitating better interactions and more informed decisions. Personalized Recommendations. As AI-driven personalization becomes the gold standard, vector search aids in accurately matching policyholders with tailor-made insurance products and services that meet their unique needs. Scaled AI Efforts. Scale AI implementations across the organization. From improving customer service chatbots to detecting fraudulent activities, vector-based models can handle tasks more efficiently than traditional methods. Atlas Vector Search goes one step further. By unifying the operational database and vector store in a single platform, MongoDB Atlas turbocharges the process of building semantic search and AI-powered applications, empowering insurers to quickly build applications that take advantage of the value of your vast troves of data. Find out why leading insurers trust MongoDB .
Data Resilience with MongoDB Atlas
Data is the central currency in today's digital economy. Studies have shown that 43% of companies that experience major data loss incidents are unable to resume business operations. A range of scenarios can lead to data loss, yet within the realm of database technology, they typically fall under three main categories: catastrophic technical malfunctions, human error, and cyber attacks. A data loss event due to a catastrophic breakdown, human error, or cyber attack is not a matter of if, but a matter of when it will occur. Hence, businesses need to focus on how to avoid and minimize the effects as much as possible. Failure to effectively address these risks can lead to extended periods of downtime of a few hours or even a few weeks following an incident. The average cost of cyberattacks is a surprising $4.45 million, with some attacks costing in the hundreds of millions. Reputational harm is harder to quantify but no doubt real and substantial. The specific industry you're in might be subject to regulatory frameworks designed to counter cyber attacks. Businesses that are subject to regulatory regimes must maintain compliance with these requirements. This can determine the configuration of your disaster recovery approach. In this blog post, we'll explain the key disaster recovery (DR) capabilities available with MongoDB Atlas . We'll also cover the core responsibilities and strategies for data resilience including remediation, and recovery objectives (RTO/RPO). Planning for data resilience in Atlas Data resilience is not a one-size-fits-all proposition, which is why we offer a range of choices in Atlas for a comprehensive strategy. Our sensible defaults ensure you're automatically safeguarded, while also offering a variety of choices to precisely align with the needs of each individual application. When formulating a disaster recovery plan, organizations commonly begin by assessing their recovery point objective (RPO) and recovery time objective (RTO). The RPO specifies the amount of data the business can tolerate losing during an incident, while the RTO indicates the speed of recovery. Since not all data carries the same urgency, analyzing the RPO and RTO on a per-application basis is important. For instance, critical customer data might have specific demands compared to clickstream analytics. The criteria for RTO, RPO, and the length of time you need to retain backups will influence the financial and performance implications of maintaining backups. With MongoDB Atlas, we provide standard protective measures by default, with customizable options for tailoring protection to the service level agreements specified by the RPO and RTO in your DR plan. These are enhanced by additional features that can be leveraged to achieve greater levels of availability and durability for your most vital tasks. These features can be grouped into two main categories: prevention and recovery. Backup, granular recovery, and resilience There are many built-in features that are designed to prevent disasters from ever happening in the first place. Some key features and capabilities that enable a comprehensive prevention strategy include multi-region and multi-cloud clusters , encryption at rest , Queryable Encryption , cluster termination safeguards , backup compliance protocols , and the capability to test resilience . (We will discuss the features in-depth in part two of this series.) While prevention might satisfy the resilience needs of certain applications, different applications may demand greater resilience against failures based on the business requirements of data protection and disaster recovery. MongoDB provides comprehensive management of data backups, including the geographic distribution of backups across multiple regions, and the ability to prevent backups from being deleted, all through an automated retention schedule. Recovery capabilities are aimed at supporting RTO and minimizing data loss and include continuous cloud backups with point-in-time recovery. Atlas cloud backups utilize the native snapshot feature of your cluster's cloud service provider, ensuring backup storage is kept separate from your MongoDB Atlas instances. Backups are essentially snapshots that capture the condition of your database cluster at a specific moment. They serve as a safeguard in case data is lost or becomes corrupted. For M10+ clusters, you have the option of utilizing Atlas Cloud Backups, which leverage the cluster's cloud service provider for storing backups in a localized manner. Atlas comes with strong default backup retention of 12 months out of the box. You also have the option to customize snapshot and retention schedules, including the time of day for snapshots, the frequency at which snapshots are taken over time, and retention duration. Another important feature is continuous cloud backup with point-in-time recovery, which enables you to restore data to the moment just before any incident or disruption, such as a cyber attack. To ensure your backups are regionally redundant and you can still restore even if the primary region that your backups are in is down, MongoDB Atlas offers the ability to copy these critical backups, with the point-in-time data, to any secondary region available from your cloud provider in Atlas. For the most stringent regulations, or for businesses that want to ensure backups are available even after a bad actor or cyber attack, MongoDB Atlas can ensure that no user, regardless of role, can ever delete a backup before a predefined protected retention period with the Backup Compliance Policy. Whatever your regulatory obligations or business needs are, MongoDB Atlas provides the flexibility to tailor your backup settings for requirements. Crucially, this ensures you can recover quickly, minimizing data loss and meeting your RPO in the event of a disaster recovery scenario. When properly configured, testing has shown that Atlas can quickly recover to the exact timestamp before a disaster or failure event, giving you a zero-minute RPO and RTO of less than 15 minutes when utilizing optimized restores. Recovery times can vary due to cloud provider disk warming and which point in time you are restoring to. So, it is important to also test this regularly. This means that regardless of your regulatory or business requirements, MongoDB Atlas allows you to configure your backups to ensure that you can meet your recovery requirements and, most importantly, recover with precision and speed to ensure that your data loss is minimal and your recovery point objectives are met should you experience a recovery event. Conclusion As regulations and business needs continue to evolve, and cyber-attacks become more sophisticated and varied, creating and implementing a data resilience strategy can be simple and manageable. MongoDB Atlas comes equipped with built-in measures that deliver robust data resilience at the database layer, ensuring your ability to both avoid incidents and promptly restore operations with minimal data loss if an incident does occur. Furthermore, setting up and overseeing additional advanced data resilience features is straightforward, with automation driven by a pre-configured policy that operates seamlessly at any scale. This streamlined approach supports compliance without the need for manual interventions, all within the MongoDB Atlas platform. For more information on the data resilience and disaster recovery features in MongoDB Atlas, download the Data Resilience Strategy with MongoDB Atlas whitepaper. To get started on Atlas today, we invite you to launch a free tier today .
Building AI with MongoDB: Cultivating Trust with Data
“Trust is like the air we breathe – when it’s present, nobody really notices; when it’s absent, everybody notices.” - Warren Buffett The issue of trust is one that dominates discussions around the safe and responsible adoption of AI across business and society. It was another Warren - this time Warren Bennis, a pioneer in modern leadership principles – who was attributed as saying "Trust is the lubrication that makes it possible for organizations to work." Particularly relevant when we think about how organizations are starting to embed AI into the very fabric of their businesses. On one hand, we have governments around the world that are at varying stages of regulating their way to trustworthy AI. However, this will not be a quick process, and enterprises can’t afford to wait. Businesses need to make progress now if they are going to unlock the opportunities presented by AI. In our latest roundup of AI innovators building with MongoDB, we’re going to focus on three companies tackling trust from different angles. We feature Nomic who are working to make AI more explainable. Robust Intelligence is focused on securing AI models against prompt injections, data poisoning, bias, PII leakage, and more. Finally, VISO TRUST comes at this issue from a totally different perspective. They use AI to help their customers reduce cybersecurity risks and improve trust across the supply chain. Let's dig in. Making AI explainable and accessible Despite the huge advances in AI and its use in almost every industry, very little is known about how the most popular models actually work. What data are they trained on? What are they learning? How can we compare accuracy between different models? These are the questions Nomic AI is seeking to help us answer through its Atlas and GPT4All products. Nomic Atlas is a data engine that allows users to explore, label, search, share, and build on massive datasets using their web browser. With Atlas, users can begin to understand what data their chosen AI models are learning from and the associations they are making during the training phase. Atlas can be used for exploratory data analysis, data labeling and cleansing, and visualizations of vector embeddings. To see Nomic Atlas in action, take a look at the recent blog post with Hugging Face announcing IDEFICS , an open-access reproduction of the visual language model based on Flamingo. The model takes image and text inputs and produces text outputs from them. For example, it can answer questions about images, describe visual content, and create stories grounded in multiple images. Nomic allows users to visually explore the content of the training data, as illustrated in the image below. Atlas can be used to curate high-quality training and instruction-tuned datasets for the GPT4All models. Nomic GPT4All is an ecosystem for training and deploying powerful and customized large language models that run locally on consumer-grade CPUs in Windows, Mac, and Ubuntu Linux clients. With GPT4All, users have access to a free-to-use, locally running, privacy-aware chatbot that doesn’t require expensive and scarce GPUs to train and infer on, or an internet connection. It can power question-answering systems, personal writing assistants, document summarization, and code generation. Demand for GPT4All has been explosive, accruing more than 20,000 GitHub stars within its first week of launch. “Every month MongoDB is adding hundreds of organizations and thousands of developers who are building AI-enabled apps on its multi-cloud developer data platform ,” said Brandon Duderstadt, CEO of Nomic. “It makes sense for us to partner with MongoDB Ventures . They are helping us accelerate our vision of making AI explainable and accessible to everyone.” Securing generative AI, supercharged by your data Robust Intelligence delivers end-to-end AI risk management to protect organizations from security, ethical, and operational risks. The company’s platform automates testing and compliance across the AI lifecycle through continuous validation and protects models in real-time with AI Firewall. This combined approach enables Robust Intelligence to proactively manage risk for any model type, including generative AI and gives organizations the confidence to unleash the true potential of AI. Robust Intelligence is trusted by leading companies including ADP, JPMorgan Chase, Expedia, Deloitte, PwC, and the U.S. Department of Defense. Recent advancements in generative AI have motivated companies to experiment with potential applications, but a lack of security controls has exposed companies to unmanaged risks. This challenge is exacerbated when sensitive company information is used to enrich pre-trained models, such as connecting vector databases, in order to increase the relevance to the end user. Robust Intelligence’s AI Firewall protects large language models (LLMs) in production by validating inputs and outputs in real-time. It assesses and mitigates operational risks such as hallucinations; ethical risks, including model bias and toxic outputs; and security risks such as prompt injections and PII extraction. AI Firewall stops bad or malicious inputs from reaching AI models and prevents undesired AI-generated results from reaching the application. Customers can confidently connect MongoDB Atlas Vector Search to any commercial or open-source LLM for secure retrieval-augmented generation with the AI Firewall integration. Atlas Vector Search serves as the memory and fact database for AI Firewall, ensuring the AI model provides enriched responses without hallucinating. Additionally, it serves as the memory and database to store historical data points. This is important in the context of identifying more advanced security attacks, such as data poisoning and model extraction, which often manifest across a cluster of data points as opposed to a single data point. Yaron Singer, CEO and co-founder at Robust Intelligence commented “By incorporating MongoDB’s Atlas Vector Search into the AI validation process, customers can confidently use their databases to enhance LLM responses knowing that sensitive information will remain secure. The integration provides seamless protection against a comprehensive set of security, ethical, and operational risks.” Being part of the MongoDB Partner Program provides Robust Intelligence with access to specialist technical support to optimize product integrations and provides visibility to the MongoDB customer base. Transforming cyber risk intelligence VISO TRUST is an AI-powered third-party cyber risk and trust platform that enables any company to access actionable vendor security information in minutes. VISO TRUST delivers fast and accurate intelligence needed to make informed cybersecurity risk decisions at scale. Today VISO TRUST has many great enterprise customers like InstaCart, Gusto, and Upwork and they all say the same thing: 90% less work, 80% reduction in time to assess risk, and near 100% vendor adoption. How does VISO TRUST achieve these results? Pierce Lamb, Senior Software Engineer on the Data and Machine Learning team at VISO TRUST provides more detail: “VISO TRUST Platform easily engages third parties, saving everyone time and resources. In a 5-minute web-based session, third parties are prompted to upload relevant artifacts of the security program that already exists, and our supervised AI – which we call Artifact Intelligence – does the rest. First, VISO TRUST deploys discriminator models that produce high-confidence predictions about features of the artifact. Secondly, artifacts have text content parsed out of them which we embed and store in MongoDB Atlas to become part of our dense retrieval system. This dense retrieval system performs Retrieval-Augmented Generation (RAG) using MongoDB features like Atlas Vector Search to provide ranked context to large language model (LLM) prompts. Thirdly, we use RAG results to seed LLM prompts and chain together their outputs to produce extremely accurate factual information about the artifact in the pipeline. This information is able to provide instant intelligence to customers that previously took weeks to produce.” VISO TRUST is the only SaaS third-party cyber risk management platform that delivers the rapid security intelligence needed for modern companies to make critical risk decisions early in the procurement process VISO TRUST uses state-of-the-art models from OpenAI, Hugging Face, Anthropic, Google, and AWS, augmented by vector search and retrieval from MongoDB Atlas. Read our interview blog post with VISO TRUST to learn more. What's next? If you are getting started with building AI-enabled apps on MongoDB, sign up for our AI Innovators Program . Successful applicants get access to expert technical advice, free MongoDB Atlas credits, co-marketing opportunities, and – for eligible startups, introductions to potential venture investors. In the spirit of "Trust, but verify" (Ronald Reagan), if you’re not sure how the program or indeed, MongoDB, could deliver value to you, take a look at earlier blog posts in this series: Building AI with MongoDB: first qualifiers include AI at the network edge for computer vision and augmented reality; risk modeling for public safety; and predictive maintenance paired with Question-answer generation for maritime operators. Building AI with MongoDB: compliance to copilots features AI in healthcare along with intelligent assistants that help product managers specify better products and help sales teams compose emails that convert 2x higher. Building AI with MongoDB: unlocking value from multimodal data showcases open source libraries that transform unstructured data into a usable JSON format; entity extraction for contracts management; and making sense of “dark data” to build customer service apps. You should look at the MongoDB for Artificial Intelligence resources page for the latest best practices that get you started in turning your idea into an AI-driven reality.
Building a Culture of Growth: SVP Simon Eid on MongoDB's Massive Opportunity in APAC
Simon Eid is Senior Vice President Asia-Pacific (APAC) at MongoDB and leads the sales teams across Australia and New Zealand, India, ASEAN, and Japan. Simon's go-to-market organisation in APAC is growing rapidly and has nearly tripled in size in the past three years. They are hiring in all regions . In this article, Simon discusses MongoDB’s opportunity in APAC and how he builds a culture of growth and accountability. Simon Eid, SVP APAC, MongoDB (left) and Anoop Dhankar, RVP ANZ, MongoDB (right) MongoDB's opportunity in Asia-Pacific Out of the top 13 economies by GDP in the world , five of them are located in APAC: China, Japan, Australia, India, and South Korea. And that's to say nothing of the ASEAN countries which alone have more than 650 million inhabitants. Combine this with the worldwide database market, one of the largest markets in the software industry. IDC estimates that it will grow to $137B in 2027, and MongoDB has just reached $1B in ARR. This gives you a sense of the massive market opportunity we have globally. Regardless of industry, product, or service, almost every company is becoming a technology company, which means that every company is becoming a data company. We believe MongoDB is the Developer Data Platform that is best placed to support and accelerate that trend. We’ve already captured thousands of customers around the globe, but it’s important to keep in mind that our world is still in the early stages of shifting to the cloud and changing how applications are built and run. Compared to other software, what's special about the market we play in is that the database is not a “nice-to-have”; it’s mission-critical for organisations. As our world continues to undergo this digital transformation, we have the opportunity to transform how our customers use software and data to innovate, create, and disrupt industries. For example, look at Cathay Pacific , Hong Kong's home airline carrier operating in more than 60 destinations worldwide. The company's digital team turned to MongoDB on their journey to become one of the first airlines to create a truly paperless flight deck. Flight Folder, their application built on MongoDB, consolidates dozens of different information sources into one place. Since the Flight Folder launch, Cathay Pacific has completed more than 340,000 flights with full digital integration in the flight deck. Their innovation is enabled by MongoDB. Building a team across regions and cultures Our team in APAC is unique because of the different markets and cultures within the region. What this means is that we go to market differently in India than we do in Australia, in Singapore than we do in South Korea, and so on. Each market is completely different, but within all of them, there is a huge opportunity. Different from many of our peers, in APAC we've established business leaders who run regionalized teams in India, ASEAN, and ANZ with all functions reporting to them. These teams essentially operate as their own business and implement local best practices into their strategy. But, it doesn’t mean they’re operating in a silo. At the leadership level, there is an immense amount of collaboration and sharing of experiences to identify what’s working and what isn’t within each region. We also have a fantastic global sales organisation that rolls out extensive training and best practices to help enable our local teams to best help our customers and grow the business. Members of our APAC team at a recent offsite in Phuket Culture The most important thing is culture. We have a very high standard around everything we do and how we interact with each other. We don’t entertain politics. You can teach someone new skills and coach them on how to be successful in a new role, but if they’re not aligned with the culture, they will not be a fit. It’s a non-negotiable for me and why the most important aspect of the hiring process is the cultural aspect. If you get the culture right, everything else starts to fall into place. What I hear at MongoDB and from the teams I've built at other companies is that this is the kind of culture they can really thrive and grow. At MongoDB, our culture is defined and shaped by six core values . One of the values that’s most important to my team is “Embrace the Power of Differences”. Within APAC, there are a variety of cultural identities and nuances that can often be difficult to navigate, whether it is cultural values, beliefs, or go-to-market strategy. It’s important that everyone who joins my team is respectful of each other’s regional culture. What we’ve done within the APAC region, and with teams across the globe, is take everyone on a journey to understand and embrace these cultural differences. Our role as leaders is to develop our teams, from the bottom all the way up, which is part of MongoDB’s BDR to CRO career development initiative. We need to develop the next wave of leaders so that they’re prepared to step up when the time comes. For APAC, this means that regardless of where someone is from, each team member has been coached and developed on the cultural nuances so that they can lead people and go to market in each of the different regions. It’s also important that each team member contributes to a culture of psychological safety. Being part of a high-growth tech company requires taking risks and making mistakes. We have a high standard and we hold each other accountable, but it never comes at the cost of creating an environment where people are afraid to fail. When someone faces setbacks, I encourage them to share those experiences so that we can collectively learn. Through mutual support, we foster a stronger team capable of delivering exceptional results. The future of MongoDB in Asia-Pacific For any organisation to be successful, I believe it’s critically important for the entire ecosystem to act as one. As I mentioned earlier, at MongoDB the whole country ecosystem is aligned around one set of goals, so it's not a case of different teams running off in different directions. The teams are willing to lean in and do what's required to help each other build a great business. I can confidently say that in APAC, we are one team. This means sales, marketing, customer success, solutions consulting, and professional services all working together to focus on three things: making customers successful, building technical champions, and driving new workloads. As we continue to grow our team and MongoDB’s footprint in the region, these are the three things that will drive our success. As I mentioned earlier, there's a huge opportunity for MongoDB in APAC. Despite hiring slowing down or stopping completely at many other organisations, we're continuing to invest heavily in the region. To give you a sense of that - we've nearly tripled the size of our APAC go-to-market team in the past three years, and we've got more open roles across the different functions and regions. If you want to be part of this journey, there are three things I want to reiterate: First, we are extremely passionate about our culture, from the field level up to the leadership level. As a team, this is the brand we bring to the market. Second, the opportunity here is massive based on the total addressable market and our current share. And third, we place critical importance on development. By joining this team, I can promise that you’ll be provided with countless opportunities to develop your career and make an impact. I’m confident in my team and the leadership we have in place who are ready to take MongoDB APAC to the next level. Join us !
Welcoming the Grainite Team to MongoDB: Accelerating Atlas Stream Processing
Tens of thousands of customers and millions of developers rely on MongoDB Atlas to run business-critical applications because of its flexible document data model and ability to work with virtually any type of data using a single, intuitive developer data platform that can run on all major cloud providers. Earlier this year, we announced that we would enable developers to apply the benefits of the document model and a unified interface to streaming data with Atlas Stream Processing . In a short amount of time, we have seen a strong response from developers eager to use this capability to simplify building event-driven applications. To accelerate the development of our streaming offering, we are thrilled to announce that the team behind Grainite — a streaming application platform, backed by Sequoia Capital and Menlo Ventures, that makes it easy to read, process, store, and query events in real-time — has now joined MongoDB. Grainite was founded by world-class technologists Ashish Kumar, who spent over ten years running structured storage products and other high-scale infrastructure projects at Google, and Abhishek Chauhan, who previously served as CTO of Cloud Networking at Citrix. Ashish and Abhishek bring with them a team of talented engineers to MongoDB who will help accelerate the development of Atlas Stream Processing and the release of new capabilities that will make it even easier for customers to process and analyze streaming data for real-time application experiences. “We’re excited to be joining a company that shares our vision for making it effortless to build event-driven applications,” said Kumar. “We’re looking forward to continuing to develop a world-class streaming platform that will make it easier for developers to incorporate real-time data into their applications and becoming part of an organization that can significantly amplify the reach and impact of event-driven applications for developers and end users.” We look forward to welcoming the Grainite team to MongoDB and sharing updates on Atlas Stream Processing and our broader streaming roadmap in the near future.
Congratulations to the 2023 EMEA MongoDB Innovation Award Winners
Throughout 2023, MongoDB has recognized the exceptional successes our APAC and North America customers realize when they build with MongoDB Atlas. Today, we are pleased to announce the recipients of the 2023 EMEA MongoDB Innovation Awards. These customers join a premiere group of organizations that have been recognized for boldly exploring the power of data and harnessing it to build the applications of tomorrow. The professionals behind these innovative projects span industries, use cases, and geographies. However, what remains constant is each organization's unfailing commitment to delivering deeper insights, powerful features, and capabilities, as well as seamless experiences, to their customers. We congratulate each of the EMEA MongoDB Innovation Award winners for delivering big results and invite you to find inspiration in their work below. Building the Next Big Thing Since being originally founded in Finland as a food delivery service, Wolt expanded their offering into groceries, gifts, and other items, and makes it possible for people living in hundreds of cities across 25 countries to get whatever they need, delivered quickly and reliably to their front door. Due to the rapidly increasing scale in their business and new partnerships with enterprise retailers, Wolt needed to rethink their existing tooling that is used by their tens of thousands of merchant partners to manage their offering inside Wolt’s delivery platform. To provide their merchants with multi-venue management capabilities, increased automation, and no limitations on amounts of menu items, Wolt chose fully-managed MongoDB Atlas due to its flexible data document model, support for Kafka and event-driven architectures, high performance, and limitless scalability. Industry Transformation Gong ’s Revenue Intelligence Platform uses proprietary AI technology to enable revenue teams to utilize customer interactions to increase business efficiency, improve decision-making, and accelerate revenue growth. Gong chose MongoDB Atlas as a high-performing transactional database due to its ability to handle Gong’s complex data structures and algorithms, and indeed MongoDB Atlas was able to support 30-40 million daily interactions and mission-critical search queries. Through the adoption of MongoDB Atlas, Gong’s queries take only 20-30 milliseconds, and with Atlas’ sharding capabilities, Gong future-proofed its environment for ongoing growth and gained greater control over its customer data. Innovative Leadership in Diversity, Equity, and Inclusion As part of its "Inclusion for All" strategy, Vodafone is committed to ensuring our digital society is accessible to all, and that women feel empowered to participate in it. In addition, Vodafone’s mission is to have 40% of women in management roles by 2030. Earlier this year, Vodafone UK teamed up with MongoDB to host interactive sessions and networking for “Digital & IT Women in Tech Week.” With a shared goal of reducing the gender gap in technology and inspiring young women to build their careers by pursuing leadership roles, the week’s events brought together more than 200 Vodafone employees to discuss breaking workplace biases and enhancing gender equality in the industry. In addition, Vodafone spoke at the 2023 MongoDB.local London event in the IDEA Lounge. Inspiring Innovation Ernst & Young LLP (EY) manages high volumes of transactional data and its clients and internal teams work under strict timelines to file taxes and meet regulatory deadlines. Their cloud-based Global VAT Reporting Tool (GVRT) platform automates and digitizes the preparation of 242 different types of returns across 79 countries. With support from MongoDB Professional Services, EY planned and executed a data migration from their previous database solution to MongoDB Atlas so that the GVRT tool could fully benefit from MongoDB’s developer data platform. Since migrating to Atlas, EY experienced a significant performance boost, they reduced costs by as much as 50%, and they are able to scale without limitations to handle increased data volumes, transactional loads, and concurrent user requests during peak periods. Find out Who Uses MongoDB and read more about our customers’ MongoDB Atlas successes.
MongoDB Atlas Vector Search Makes Real-Time AI a Reality with Confluent
Today, we’re excited to announce our new integration with Confluent Cloud . MongoDB Atlas Vector Search users now have simple access to data streams across their entire business, enabling them to build cutting-edge Generative AI applications that are grounded in a real-time, contextual, and trustworthy knowledge base. Think of an application like ChatGPT, but if it knew everything about your private enterprise data, including constant awareness of what’s happening in the world and your business right now. Atlas Vector Search allows you to search intelligently across any unstructured data, using the power of Large Language models (LLMs). With Confluent’s data streaming platform, you can provide a continuous supply of AI-ready data for the development of sophisticated customer experiences, bridging the gap between legacy data systems and the modern data stack. High-value, trusted AI applications require real-time data Real-time AI needs real-time data from across your organization. The promise of real-time AI is only unlocked when models have all the freshest contextual data they need to respond just in time with the most accurate, relevant, and helpful information. However, building these real-time data connections across on-prem, multi-cloud, public, and private cloud environments for AI use cases is not trivial. Traditional data integration and processing tools are batch-based and inflexible, creating an untenable number of tightly coupled point-to-point connections that are hard to scale and lack governance. As a result, the data made available is stale and of low fidelity. This introduces unavoidable latency into the AI application and may outright block implementation altogether. The difficulty in gaining access to high-quality, ready-to-use, contextual, and trustworthy data in real-time is hindering developer agility and the pace of AI innovation. Confluent's data streaming platform fuels MongoDB Atlas Vector Search with real-time data With the MongoDB Kafka Connector , users can easily configure MongoDB Atlas as a destination for customer 360 data from Confluent Cloud. This data is converted into vector embeddings using various machine learning models (OpenAI, HuggingFace, and more) and orchestrated by Atlas Triggers. Then using Atlas Vector Search, this data can be indexed and searched efficiently to power use cases such as semantic search, recommendation engines, Q&A systems, and many others. We demonstrate a Chatbot for e-commerce that will allow users to ask natural language questions to discover what they need and then get recommendations on products to buy that suit their preferences. Some of the data required in this scenario includes the currently available inventory, the shipping options, and their session browsing history. The users can refine their product recommendations using a conversational interface, all the while ensuring that the products being recommended are rooted in real-time data. The benefits of being able to effectively use real-time data are immense, almost critical, in this scenario, since recommending a product that’s not available or can’t be delivered to a user’s location in the time frame they require would mean a lost sale and a dissatisfied customer. The inventory data is rapidly changing - products go in and out of stock constantly. Hence the customer chat/assistant application will need to quickly come up with new sets of recommendations. With Confluent, MongoDB Atlas Vector Search users can break down data silos, promote data reusability, improve engineering agility, and foster greater trust throughout their organization. This allows more teams to securely and confidently unlock the full potential of all their data with MongoDB Atlas Vector Search. Confluent enables organizations to make real-time contextual inferences on an astonishing amount of data by bringing well-curated, trustworthy streaming data to AI systems, vector databases, and AI-powered applications. With easy access to data streams from across their entire business, MongoDB Atlas Vector Search users can now: Create a real-time knowledge base: Build a shared source of real-time truth for all your operational and analytical data, no matter where it lives for sophisticated model building and fine-tuning Bring real-time context at query time: Convert raw data into meaningful chunks with real-time enrichment and continually update your embedding databases for your GenAI use cases Build governed, secured, and trusted AI: Establish data lineage, quality, and traceability, providing all your teams with a clear understanding of data origin, movement, transformations, and usage Experiment, scale, and innovate faster: Reduce innovation friction as new AI apps and models become available. Decouple data from your data science tools and production AI apps to test and build faster MongoDB Atlas Vector Search and Confluent enable simple development of real-time AI applications Our new Confluent integration enables all your teams to tap into a continuously enriched real-time knowledge base, so they can quickly scale and build AI-enabled applications using trusted data streams. Here’s a demo video to demonstrate how this works: Getting started Get started by creating a MongoDB Atlas account if you don't already have one. Just click on “Register.” MongoDB offers a free-forever Atlas cluster in the public cloud service of your choice. To learn more about Atlas Vector Search, visit the product page . Not yet a Confluent customer? Start your free trial of Confluent Cloud today. New sign-ups receive $400 to spend during their first 30 days—no credit card required.
Recap of Product Announcements at MongoDB.local London, 2023
This post is also available in: Deutsch , Français , Español , Português . We’re now more than three months into our MongoDB.local world tour that kicked off in NYC earlier this June. Since then, we’ve continued to introduce product enhancements and new capabilities, from the GA of MongoDB for VS Code to MongoDB 7.0 and Queryable Encryption . Today, we're excited to share the highlights of recent product announcements from our London conference this morning. Efficient and intelligent developer experiences for building with MongoDB We’ve always been committed to providing the best developer experience because we know that developer time is one of the most precious commodities in any organization. When we looked at the most common tasks developers perform on a daily basis, we recognized two areas for improvement: making development against Atlas more efficient and making it easier to write MongoDB queries. We want to give developers the most ergonomic way to work with MongoDB Atlas throughout their entire journey. For many developers, that journey begins by working with MongoDB locally before moving to the cloud - which is why we’re investing in a great local development experience. Starting today , developers can use the Atlas CLI to manage local development environments with the same experience as Atlas clusters in the cloud. Beyond making it easy to deploy and manage development instances, we also want to bring the breadth of our developer data platform to local environments. The new Atlas CLI experience, available in public preview, also comes with integrated Atlas Search and Atlas Vector Search so developers can create and manage search indexes and queries within their development workflows. This is the first of more investments to come as we continue to build a seamless experience for services in Atlas from sandbox to testing and production. The other problem we want to solve is speed, and we’re excited to use generative AI technology to introduce several new intelligent developer experiences . Querying data should be as easy as asking a question in a language that feels natural to you. Developers can now ask questions in plain English and Compass , our MongoDB GUI, will generate the corresponding query in MongoDB query language syntax. From simple queries to more complex aggregations, this experience will reduce the friction of learning MongoDB’s query language and help developers iterate and build new features more quickly. We’re also introducing a new language interface for Atlas Charts so developers can easily visualize data in MongoDB and an AI chatbot for our documentation resources. For customers embarking on a migration journey from using relational databases to using MongoDB, one of the most difficult and important steps is converting hundreds, if not thousands, of queries and application code. Available now in private preview, SQL query conversion in Relational Migrator can convert queries and stored procedures to MongoDB query language syntax at scale, shifting resources from query creation to review and implementation. Run MongoDB anywhere - from edge to cloud One of the benefits of MongoDB that we’ve been proud of since the beginning is the flexibility to build with it anywhere - on a local machine for development, fully managed across multiple public clouds , on-premises or in a private cloud, and even on mobile and edge devices. As mobility and IoT become more essential to operations across industries, one of the key requirements is being able to sync and move data across environments. Today , we’re excited to announce Atlas for the Edge , which brings data processing and storage capabilities closer to where it’s often most needed - right where data is generated. With Atlas Edge Servers that can be deployed anywhere and built-in conflict resolution, customers can easily create hub and spoke architectures to power customer experiences that require ultra-low latency or heavier computation close to where data is generated. From manufacturing to retail to healthcare , Atlas for the Edge enables customers to unlock more use cases that rely on a connected data layer across public clouds, on-premise or edge computing locations, and sensors and devices. Build the next generation of AI-powered applications with a developer data platform Since our public preview announcement earlier this year, we’ve seen a lot of interest in Atlas Vector Search, particularly in building RAG (retrieval augmented generation) architectures for applications powered by Generative AI . From startups to established companies, customers are eager to build more intelligent applications with the backing of a modern, highly scalable, and performant platform. The ability to store vector embeddings alongside source and metadata has simplified how developers build GenAI into new and existing applications, and with the introduction of the $vectorSearch aggregation stage, it will be even easier to pre-filter and tune results using the MongoDB query language, all in a single platform on Atlas. Finally, we recognize the need to empower developers with practical resources to expand their skills and knowledge. In addition to new content available on MongoDB University , we announced MongoDB Press , a medium for publishing technical and leadership knowledge about MongoDB. The first two books are on aggregations and mastering MongoDB 7.0. We also added a solutions library on our website with use cases organized by industry verticals to show the art of what’s possible with our developer data platform. To see more announcements and get the latest product updates, visit our What’s New page. Head to the MongoDB.local hub to see where we'll be showing up next.
New Intelligent Developer Experiences for Compass, Atlas Charts, Relational Migrator, and Docs
This post is also available in: Deutsch , Français , Español , Português . Today, MongoDB announced a range of innovations in its developer data platform, creating new, intelligent developer experiences in familiar tools like MongoDB Compass, Atlas Charts, Relational Migrator, and MongoDB Documentation that radically simplify and accelerate how developers build modern applications. These new experiences provide developers with guided and intelligent assistance for their development processes in: MongoDB Compass: Where developers can use natural language to compose everything from simple queries to sophisticated, multi-stage aggregations. MongoDB Relational Migrator: Where developers can convert SQL queries to MongoDB Query API syntax. MongoDB Atlas Charts: Where developers can use natural language to generate basic data visualizations. MongoDB Documentation: Where developers can ask questions to an intelligent chatbot, built on top of MongoDB Atlas and Atlas Vector Search, to enable lightning-fast information discovery and troubleshooting during software development. Developer time is one of the most precious commodities in any organization, and with business and customer expectations continuing to rise, developers are under increasing pressure to deliver applications quickly. With more intelligent experiences across the MongoDB developer data platform, it is now simpler and easier than ever to build modern applications for virtually any use case. Natural Language Queries in Compass Building queries and aggregations is one of the most prominent developer use cases for Compass , MongoDB’s popular, downloadable GUI tool. Compass’ new, intelligent experience allows developers to use natural language to compose sophisticated aggregations to query, transform, and enrich data, reducing the complexity and learning curve to build queries into application code. The new experience is being released in Public Preview in version 1.40.0 and will be rolled out incrementally to users starting today until the end of October. To get started, make sure you have 1.40.0 downloaded on your machine and have access to the feature. Then you can navigate to the Documents tab and click on the Generate Query button in the query bar, which opens a second bar below the standard query bar where you can enter natural language prompts to generate the Query API syntax for you to execute against your data. Be sure to hit the “thumb’s up” or “thumb’s down” button to rate the helpfulness of the query generated. SQL Query Conversion in Relational Migrator Migrations are part of many developers’ journeys with MongoDB. Earlier this summer at MongoDB.Local NYC, we announced Relational Migrator to help teams with these projects, and we’re continuing to make it easier to modernize application code. Many legacy systems have hundreds, if not thousands of SQL queries that must be modernized as part of any migration effort, and that can be a time-consuming, if not daunting task. Now in Private Preview, developers can use Relational Migrator to convert existing SQL queries and stored procedures into development-ready MongoDB Query API syntax. With SQL query conversion, developers can leverage Relational Migrator to eliminate the manual effort of creating MongoDB queries at scale - speeding up migration projects. SQL query conversion is currently available in Private Preview, and access can be requested directly from the latest version of Relational Migrator. Natural Language Support in Atlas Charts Atlas Charts is the best way for developers to visualize Atlas data. By offering an effortless and powerful solution for gaining data-driven insights, Charts empowers developers and the businesses they help scale. What has always been easy is now becoming more intelligent too! Available in Private Preview, a new natural language mode allows developers to visualize their data through a simple language query, for example: “show me a comparison of annual revenue by country and product.” This is just the start. Later this year, natural language support will extend to more complex queries and chart types. Sign up today to try out natural language support for building charts! Stay tuned for more updates from the team and check out our documentation to learn more about what’s supported by natural language during Private Preview! Intelligent Chatbot for MongoDB Documentation Documentation is critical to the developer experience, making it easier to discover product features and capabilities and troubleshoot common challenges during software development. MongoDB is now super-charging your experience with an intelligent chatbot that improves information discovery by surfacing and summarizing the most relevant documentation. Built with MongoDB Atlas and Atlas Vector Search, the chatbot allows you to ask questions in natural language like “How do I get started with MongoDB Atlas?” or “How do I add a new IP address to the IP access list for my Atlas project?” and receive a response with reference articles, code examples, and other relevant information. MongoDB will also be open-sourcing and providing educational materials about how we built the intelligent chatbot, making it that much easier for others in the community to use the power of MongoDB Atlas and Atlas Vector Search to create dynamic and educational experiences for their end users. Data Privacy and Security MongoDB is trusted by some of the world's most security-conscious organizations, who use the developer data platform’s robust data security and privacy controls to manage their most sensitive data assets. To maintain this trust, these new developer experiences will always be transparent about what data is accessed and used, allowing customers to make informed decisions within the boundaries of their unique security, privacy, and compliance concerns. Get Started Today With new, intelligent features that allow developers to interact with their data using natural language in Compass, Relational Migrator, and Charts, as well as an intelligent chatbot for MongoDB Documentation, it’s easier than ever to take advantage of the flexibility and scalability of MongoDB's document data model to build any class of application. If you have feedback on these experiences, you can enter a suggestion in our user feedback portal .
Introducing a Local Experience for Atlas, Atlas Search, and Atlas Vector Search with the Atlas CLI
This post is also available in: Deutsch , Français , Español , Português . Today, MongoDB is pleased to announce in Public Preview a new set of features for building software locally with MongoDB Atlas, giving developers greater flexibility and reducing operational overhead throughout the entire software development lifecycle. Developers can now develop locally with MongoDB Atlas deployments, including Atlas Search and Vector Search , using the Atlas CLI , empowering them to create full-text search or AI-powered applications no matter their preferred environment for building with MongoDB. Developers can use the Atlas CLI to set up, connect to, and automate common management tasks from early development through testing, staging, and production. For full-text search use cases, developers can now use the Atlas CLI to create and manage Atlas Search indexes regardless of whether they are working locally or in the cloud. Similarly, developers building applications powered by semantic search and generative AI on MongoDB can now use the Atlas CLI to create and manage local development instances with Vector Search indexes regardless of their development environment. Developer time is one of the most precious commodities in any organization building innovative new application experiences. But all too frequently, developers are burdened with managing repeatable tasks such as setting up development environments. They also often have to wrestle with the cognitive overhead of switching between different user experiences for local versus cloud development, distracting from delivering value. By giving developers the power of Atlas at their fingertips no matter their preferred development environment, MongoDB continues to expand the scope and capabilities of its developer data platform while placing a premium on developer experience. Create a Local Atlas Database Ready to create a local Atlas database, but don’t have the Atlas CLI yet? It’s easy to install with your favorite package manager. To install the Atlas CLI with Homebrew, use the following command: brew install mongodb-atlas In addition to installing via the Homebrew package manager, you can install the MongoDB Atlas CLI via Apt, Yum, Chocolatey, directly downloading the binary, or pulling the Docker image (learn more about our documentation ). You can also download it directly from the MongoDB Download Center . To create a local Atlas deployment with default settings in interactive mode, enter: atlas deployments setup --type local If you want to list your Atlas deployments enter: atlas deployments list If you’re authenticated to Atlas, you will see both your local and cloud Atlas deployments. If you aren’t authenticated to Atlas, you will only see your local deployments. Get Started with Local Atlas Search Building an application with a full-text search feature powered by Atlas Search? If you’re a developer who tends to build and prototype locally, you may be interested in using the Atlas CLI to work with Atlas Search in your local environment. To get started, first, connect to the local deployment on which you’d like to create a Search index: atlas deployments connect Next, you can use the MongoDB Shell to create your Search index. Below you’ll see an example of how to create an Atlas Search index: db.YOURCOLLECTION.createSearchIndex( "example-index", { mappings: { dynamic: true } } ) Then, if you want to run a query you can use the $search stage of an aggregation pipeline. You can learn more about managing Atlas Search indexes in our documentation . Get Started with Local Vector Search If you’re building an application with generative AI or semantic search and MongoDB Atlas, chances are you’ll be interested in our Atlas Vector Search offering. And now with the Atlas CLI, you can work with Vector Search in the cloud and your local environment. To get started with Vector Search locally you can use MongoDB Shell to create a Vector Search index. Notice that this is similar to the Atlas Search example above, except that in this case there is a vector embedding accounted for in search index creation. db.YOURCOLLECTION.createSearchIndex({ "mappings": { "dynamic": true, "fields": { "plot_embedding": { "type": "knnVector", "dimensions": 1536, "similarity": "euclidean" } } } } ) To learn more about running Vector Search queries visit our documentation . Additionally, if you're already familiar with handling your cloud Search indexes using the Atlas CLI, you'll appreciate a fresh set of interactive commands designed to help you efficiently manage Atlas Search and Vector Search indexes both locally and in the cloud: atlas deployments search indexes create From there you can move through an interactive flow that guides you through index creation. For detailed instructions visit our tutorial . Ready to Move to the Cloud? If you’re ready to create an Atlas database in the cloud, that is easy to do with the Atlas CLI. Simply use the following command: atlas deployments setup --type atlas From there, the setup wizard will guide you to: Register for an Atlas account or authenticate to an existing account Create a free MongoDB Atlas database Load sample data Add your IP address to the access list Create a database user and password Connect to the cluster using the MongoDB Shell ( mongosh ) so you can begin interacting with your data To learn more about the Atlas CLI, visit our documentation . And be sure to let us know what you think of the Atlas CLI in our user feedback portal . With the new local experience with the Atlas CLI, it’s easier than ever to work with your data on Atlas no matter your preferred development environment. Get started today with the Atlas CLI as the ultimate developer tool to manage MongoDB Atlas, including Atlas Search and Vector Search, throughout the entire software development lifecycle, from your local environment all the way to the cloud.
Realm is Now Atlas Device SDKs
This post is also available in: Deutsch , Français , Español , Português . Change is essential to growth and progress in open source technology. MongoDB is announcing today that we’re renaming Realm to MongoDB Atlas Device SDKs. We will continue offering MongoDB Atlas Device SDKs as a free and open source project under Apache License 2.0. MongoDB acquired Realm and its technology in 2019 and has continued development of the project to provide developers a synchronized data layer between devices and the cloud that makes it easier to build mobile applications, including support for multiple programming languages, development frameworks, and cloud providers. MongoDB will continue open source development of Realm as Atlas Device SDKs, and developers are free to use the project — with or without MongoDB Atlas — to build reactive mobile applications using the technology of their choice. Regular updates to the project will continue to be available on its Github repository . Please engage with us on the MongoDB Community Forum with questions or feedback.
Hot Off the Press: MongoDB Launches Two New Books at MongoDB.local London
It’s well known that developers today are facing immense demand to build new, modern applications at an accelerated pace. In fact, recent data from IDC predicts that by 2025 there will be a shortfall of 4 million developers . As the industry moves towards more complex and advanced technologies, developers remain the foundation – as well as the key – to expanding emerging, innovative technologies, from AI to IoT and other automation applications. The skills required to develop these technologies are becoming increasingly specialized. In turn, the need for accessible resources, training, and education is only becoming more pressing. That’s why we’re delighted to launch MongoDB Press – our very own official series of educational books – penned by a mix of our in-house experts and trusted industry voices, covering both technical and strategic topics. We’re thrilled that our first two books of the series – Mastering MongoDB 7.0 and Practical MongoDB Aggregations – will be launched at MongoDB .local London 2023 . Practical MongoDB Aggregations was written by our very own Executive Solutions Architect, Paul Done, and is intended for developers with a baseline understanding of the MongoDB Aggregation Framework. The book will allow readers to learn more about building aggregation pipelines. You can get 20% off now by visiting mongodb.com/books . Attendees at MongoDB .local London will be able to get signed copies of the book while supplies last Mastering MongoDB 7.0 is available for pre-order and was written by a team of MongoDB experts. It provides a deep-dive into the latest features of MongoDB. By the end of the book, readers will have gained the practical understanding required to design, develop, administer, and scale MongoDB-based database applications, both on-premises and on the cloud.