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MongoDB and deepset Pave the Way for Effortless AI App Creation
Building robust AI-powered applications has often been a complex, resource-intensive process. It typically demands deep technical and domain expertise, significant development effort, and a long time to value. For IT decision-makers, the goal is clear: enable AI innovation to achieve real business outcomes without compromising scalability, flexibility, or performance, and without creating bottlenecks for development teams serving business teams and customers. Solutions from deepset and MongoDB empower organizations to overcome these challenges, enabling faster development, unlocking AI's potential, and ensuring the scalability and resilience required by modern businesses. Breaking barriers in AI development: The real-time data challenge For many industries, real-time data access is critical to unlocking insights and delivering exceptional customer experiences. AI-driven applications rely on seamless retrieval and processing of structured and unstructured data to fuel smarter decision-making, automate workflows, and improve user interactions. For example, in customer service platforms, instant access to relevant data ensures fast and accurate responses to user queries, improving satisfaction and efficiency. And healthcare applications require immediate access to patient records to enable personalized treatment plans that enhance patient outcomes. Similarly, financial systems rely on real-time analysis of market trends and borrower profiles to make smarter investment and credit decisions to stay competitive in dynamic environments. However, businesses often face challenges when scaling AI applications. These challenges include inconsistent data retrieval, where organizations struggle to efficiently query and access data across vast pools of information. Another challenge is complex query resolution, which involves interpreting multi-layered queries to retrieve the most relevant insights and provide smart recommendations. Data security concerns also pose obstacles, as businesses must ensure sensitive information remains protected while maintaining compliance with regulatory standards. Lastly, AI production-readiness is critical, requiring organizations to ensure their AI applications are properly configured and thoroughly tested to support mission-critical decisions and workflows with accuracy, speed, and adaptability to rapid changes in the AI ecosystem or world events. Addressing these challenges is vital for businesses looking to unlock the full potential of AI-powered innovations and maintain a competitive edge. Transformative solution: Deepset RAG expertise meets MongoDB Atlas Vector Search We’re excited to announce a new partnership between deepset and MongoDB. By integrating deepset’s expertise in retrieval-augmented generation (RAG) and intelligent agents with MongoDB Atlas, developers can now more easily build advanced AI-powered applications that deliver fast, accurate insights from large and complex datasets. We're thrilled to partner with MongoDB and build out an integrated end-to-end GenAI solution to speed up the time to value of customers' AI efforts and help solve their complex use cases to deliver key business outcomes. Mark Ghannam, Head of Partnerships, deepset What sets deepset apart is its product and documentation production-readiness, flexibility for solving complex use cases, and its library of ready-to-use templates, which allow businesses to get started fast to quickly deploy common RAG and agent functionalities, reducing the time and effort required for development. For teams needing customization, Haystack provides a modular, object-oriented design that supports drag-and-drop components , utilizing both standard integrations and custom components . This makes it highly accessible, enabling developers to configure workflows according to their specific application needs, without requiring extensive coding knowledge. On top of Haystack, deepset’s AI Platform makes the prototype to production process of building AI applications even faster and more efficient. It extends Haystack’s building block approach to AI application development, with a visual design interface, qualitative user testing, side-by-side configuration/large language model (LLM) testing, integrated debugging, and hallucination scoring, in addition to expert service assistance and support. The platform’s Studio Edition is free for developers to try. Through seamless integration with MongoDB Atlas Vector Search , deepset equips developers with the ability to incorporate advanced RAG and agent capabilities into their compound AI applications easily through the processes described, known as LLM orchestration. Key features enable several transformative possibilities across industries. Intelligent chatbots allow businesses to deliver precise and context-aware customer interactions, significantly enhancing call center efficiency. Automated content tagging optimizes and streamlines workflows in content management systems, enabling faster categorization and discovery of information. Tailored educational, research, and media platforms personalize learning materials, research, and media content based on user questions and preferences, improving engagement and effectiveness while adhering to institution and brand guidelines. Industry-specific planning systems and workflow automations simplify complex processes, such as lending due diligence. By leveraging the deepset framework alongside MongoDB Atlas Vector Search, developers gain a powerful toolkit to optimize the performance, scalability, and user experience of their applications. This collaboration provides tangible benefits across industries like customer service, content management, financial services, education, defense, healthcare, media, and law—all while keeping complexity to a minimum. Data security and compliance: A foundational priority As organizations adopt advanced AI technologies, protecting sensitive data is paramount. MongoDB Atlas and deepset offer robust protections to safeguard data integrity. MongoDB and deepset provide industry-standard security measures such as encryption, access controls, and auditing, along with compliance certifications like ISO 27001, SOC 2, and CSA STAR. These measures help ensure that sensitive data is handled with care and that client information remains secure, supporting businesses in meeting their regulatory obligations across different sectors. Incorporating MongoDB Atlas into AI solutions allows enterprises using deepset's RAG and Agent capabilities to confidently manage and protect data, ensuring compliance and reliability while maintaining operational excellence. Shaping the future of AI-powered innovation The partnership between MongoDB and deepset is more than a collaboration—it's a driving force for innovation. By merging cutting-edge language processing capabilities with the robust, scalable infrastructure of MongoDB Atlas, this alliance is empowering organizations to create tomorrow's AI applications, today. Whether it’s intelligent chatbots, personalized platforms, or complex workflow automations, MongoDB and deepset are paving the way for businesses to unlock new levels of efficiency and insight. At the core of this partnership is deepset’s advanced RAG and Agent technology, which enables efficient language processing and precise query resolution—essential components for developing sophisticated AI solutions. Complementing this is MongoDB’s reliable cloud database technology, providing unmatched scalability, fault tolerance, and the ability to effortlessly craft robust applications. The seamless integration of these technologies offers developers a powerful toolkit to create applications that prioritize fast time to value, innovation, and precision. MongoDB’s infrastructure ensures security, reliability, and efficiency, freeing developers to focus their efforts on enhancing application functionality without worrying about foundational stability. Through this strategic alliance, MongoDB and deepset are empowering developers to push the boundaries of intelligent application development. Together, they are delivering solutions that are not only highly responsive and innovative but also expertly balanced across security, reliability, and efficiency—meeting the demands of today’s dynamic markets with confidence. Jumpstart your journey Dive into deepset's comprehensive guide on RAG integration with MongoDB Atlas. Then get started with deepset Studio Edition (free) to start building. Transform your data experience and redefine the way you interact with information today! Learn more about MongoDB and deepset's partnership through our partner ecosystem page .
Spring Data MongoDB: Now with Vector Search and Queryable Encryption
MongoDB is pleased to announce new enhancements to the Spring Data MongoDB library with the release of version 4.5.0 , increasing capabilities related to vector search, vector search index creation, and queryable encryption. Spring Data MongoDB makes it easier for developers to integrate MongoDB into their Java applications, taking advantage of a potent combination of powerful MongoDB features and familiar Spring conventions. Vector search Vector embeddings convert disparate types of data into numbers that capture meaning and relationships. Many types of data—words, sentences, images, even videos—can be represented by a vector embedding for use in AI applications. In MongoDB, you can easily store and index vector embeddings alongside your other document data—no need to manage a separate vector database or maintain an ETL pipeline. In MongoDB, an aggregation pipeline consists of one or more stages that process documents, performing operations such as $count and $group . $vectorSearch is an aggregation pipeline stage for handling vector retrieval. It was released in MongoDB 6.0, and improved upon in MongoDB 7.0 and 8.0. Using the $vectorSearch stage to pre-filter your data and perform a semantic search against indexed fields, you can easily process vector embeddings in your aggregation pipeline. Vector search indexes Like other retrieval techniques, indexes are a key part of implementing vector search, allowing you to narrow the scope of your semantic search and exclude irrelevant vector embeddings. This is useful in an environment where it isn’t necessary to consider every vector embedding for comparison. Let’s see how easy it is to create a vector search index with Spring Data MongoDB 4.5.0! VectorIndex index = new VectorIndex("vector_index") .addVector("plotEmbedding", vector -> vector.dimensions(1536).similarity(COSINE)) .addFilter("year"); mongoTemplate.searchIndexOps(Movie.class) .createIndex(index); As you can see, the VectorIndex class offers intuitive methods such as addVector and addFilter that allow you to define exactly, with native Spring Data APIs, the vector you want to initialize. To actually execute a search operation that leverages the index, just issue an aggregation: VectorSearchOperation search = VectorSearchOperation.search("vector_index") .searchType(VectorSearchOperation.SearchType.ENN) .path("plotEmbedding") .vector( ... ) .limit(10) .numCandidates(150) .withSearchScore("score"); AggregationResults<MovieWithSearchScore> results = mongoTemplate .aggregate(newAggregation(Movie.class, search), MovieWithSearchScore.class); Leverage the power of MongoDB to run sophisticated vector search, directly from Spring. Queryable Encryption Support for vector search isn’t the only enhancement found in 4.5.0. Now, you can pass encryptedFields right into your CollectionsOptions class, giving Spring the context to understand which fields are encrypted. This context allows Spring to leverage the power of MongoDB Queryable Encryption (QE) to keep sensitive data protected in transit, at rest, or in use. QE allows you to encrypt sensitive application data, store it securely in an encrypted state in the MongoDB database, and perform equality and range queries directly on the encrypted data. Let’s look at how easy it is to create an encrypted collection with Spring Data MongoDB: CollectionOptions collectionOptions = CollectionOptions.encryptedCollection(options -> options .queryable(encrypted(string("ssn")).algorithm("Indexed"), equality().contention(0)) .queryable(encrypted(int32("age")).algorithm("Range"), range().contention(8).min(0).max(150)) .queryable(encrypted(int64("address.sign")).algorithm("Range"), range().contention(2).min(-10L).max(10L)) ); mongoTemplate.createCollection(Patient.class, collectionOptions); By declaring upfront the options allowed for different fields of the new collection, Spring and MongoDB work together to keep your data safe! We’re excited for you to start incorporating these exciting new features into applications built with Spring Data MongoDB. Here are some resources to help you get started: Explore the Spring Data MongoDB documentation Check out the GitHub repository Read the release notes for Spring Data MongoDB 4.5.0
Teach & Learn: Professor Margaret Menzin, Simmons University
MongoDB’s Teach & Learn blog series interviews students and educators worldwide who are using MongoDB to enhance their classrooms. These stories highlight how MongoDB’s platform and resources are revolutionizing education and preparing tech professionals. The MongoDB for Educators program offers free resources and technology for creating interactive learning environments that connect theory and practice. Educators gain access to MongoDB Atlas credits, curriculum, certifications, and a global community. Unlocking potential: Integrating MongoDB to enhance learning in the classroom Professor Margaret Menzin is a dedicated educator at Simmons University, where she was instrumental in developing one of the first undergraduate data science majors in the United States. With a keen eye on industry trends, she revamped her database course to include NoSQL technologies like MongoDB, recognizing their growing importance in the professional world. Her approach blends practical skills with theoretical understanding, ensuring her students are well-prepared for real-world challenges. Professor Menzin also fosters a vibrant student community around MongoDB technology, empowering students to use these skills in their academic projects and future careers. Her MongoDB insights on curriculum and student engagement offer valuable perspectives for educators adapting to the evolving tech landscape, as you’ll see in our interview. 1. Tell us about your educational and professional journey and what initially sparked your interest in databases and MongoDB. At Simmons, we were one of the first US universities to offer an undergraduate major in data science, so we were very aware of the importance of NoSQL for handling big data. In 2017, I returned to teaching databases after a hiatus of about seven years—and when I looked at the textbooks, they hadn’t changed. But the world sure had. So, I checked the Stack Overflow survey of what professional developers were using and found that 25% of them were using MongoDB. With my colleague’s permission, I revised our course to be about one-third on NoSQL, and I had to develop my own materials. But my students adore using MongoDB. 2. What courses related to databases and MongoDB are you currently teaching? I teach a one-semester database course that’s required for all students majoring in computer science, data science, and information technology/cybersecurity. I also teach a course in full-stack web development, and students learn how to access MongoDB from Node.js. 3. What motivated you to incorporate MongoDB into your curriculum? I was motivated by what is happening in the real world, but as an instructor, I find that having students learn something else in addition to relational databases makes the discussions much livelier about atomicity, consistency, isolation, and durability (ACID) transactions and concurrency in relational database management systems (RDBMSs). Now, students see where ACID transactions are important and where they’re not. (Yes, I know that MongoDB supports ACID transactions.) Similarly, the design process is different for MongoDB and for entity-relationship design, and that highlights the strengths of each. Figure 1. Professor Margaret Menzin's students at Simmons University. 4. You have successfully built an active student community around MongoDB on your campus. Can you share some insights into how you achieved this and the impact it’s had on students? First, I tell students to put MongoDB on their curricula vitae because it gives them an edge. Second, students are so enthusiastic about MongoDB that they turn to it when they have to build projects for senior courses. I do require that students install the MongoDB Community Edition on their own computers, and—without any data to back this claim up—I think that makes it more likely that they will turn to it. And they do. This year, a group of four seniors built a complete software system for a nonprofit on our campus, and they chose to use MongoDB. (I was not the supervisor; they chose MongoDB because they liked it and thought it was the best choice.) 5. How do you design your course content to integrate MongoDB in a way that engages students and ensures practical learning experiences? In my course, I give students a set of comma-separated values (CSVs) for the Northwinds example (a pretty standard project with files for customers, products, orders, line items, etc.), and they denormalize the data. That is, they embed the line-item documents into the orders documents and do some computations, then embed the orders documents into the customers’ documents. They timed various operations with and without indexes. One thing I have learned is to put the exam on MongoDB before the project, so everyone on the team is ready to contribute to the project. I have a file of the approximately 5,000 restaurants in New York City that I use for the exam. 6. How has MongoDB supported you in enhancing your teaching methodologies and upskilling your students? First, my students make extensive use of the MongoDB documentation. Reading documentation is an important skill for students to learn, and MongoDB’s is excellent. Second, I have gone through all the MongoDB videos for teachers, and I especially use the ones on the design process. For the aggregation pipeline, we use the book Practical MongoDB Aggregations , linked to on your site, and the Mosh Hamedani videos on YouTube. And because I was one of the very early adopters among professors, I’ve had to develop a lot of my own materials, which I’ve shared. Figure 2. Professor Margaret Menzin's students at Simmons University. 7. Could you share a memorable experience or success story of a project from your time teaching MongoDB that stands out to you? After the first year I taught MongoDB, I asked my colleagues for feedback, and they suggested that I see what other people were doing on the Association for Computing Machinery (ACM) Special Interest Group on Computer Science Education (SIGCSE) LISTSERV. The result was a panel called “NoSQL is No Problem” for SIGCSE 2020. And there was a curated bibliography for various NoSQL platforms. 8. How has your role as a MongoDB educator impacted your professional growth and the growth of the student community at your university? As a faculty member, I am always trying to see what’s going to be important next and find out how to learn it. Students respond to that attitude. I also lean very heavily on small-group work and team projects in all my courses. Most of my database students are sophomores, and they don’t know each other well yet. So in any small-group work, I say, “Even if it’s your roommate, begin with ‘Hello, my name is…’” and they laugh, but it works. It happens that the database course (occurring fall of the sophomore year) is when we try to build a sense of cohesion among our majors. I also require my students to take out an ACM student membership so I can assign a variety of readings and videos, and that helps them build professional identities. And my students love the fact that this is cutting-edge and that they are moving away from textbooks. I’m sure that listing MongoDB among their skills on LinkedIn and elsewhere also helps them find internships. 9. What advice would you give to educators who are considering integrating MongoDB into their courses to ensure a successful and impactful learning experience for students? Allow about 30% of a first database course for the MongoDB work. It takes me about one and a half to two weeks to get students to install and learn basic MongoDB, and then another week and a half for the project. After that, use MongoDB as a jumping-off point to circle back to topics like forms of consistency, the CAP Theorem, design trade-offs, design decisions for distributed databases, and the choice of a database model. Comparing and contrasting MongoDB with an RDBMS is a very powerful way to summarize many of the key concepts in a database course. Finally, spending the last week on these high-level issues, when all of the students’ other courses are rushing to finish their projects, will make students very grateful. Apply to the MongoDB for Educators program and explore free resources for educators crafted by MongoDB experts to prepare learners with in-demand database skills and knowledge.
Backup MongoDB Enterprise Advanced via Cohesity or Rubrik
In a world where software drives business strategy, data resilience has become a core business imperative. In fact, 90% of IT and security leaders report their organizations experienced a cyberattack in the past year, with 18% facing more than 25 attacks. 1 Every mission-critical workload must be secure, compliant, and able to recover quickly from any disruption. To help customers meet these demands, MongoDB is introducing a major data resilience enhancement: third-party backup integrations in MongoDB Enterprise Advanced . As the most flexible way to run MongoDB across on-premises, private, or hybrid cloud environments, MongoDB Enterprise Advanced now makes it even easier to integrate with customers’ existing enterprise backup tools. Previously, MongoDB Enterprise Advanced customers relied on our self-hosted database management platform, MongoDB Ops Manager , to handle backup and restore operations. For the first time, MongoDB Ops Manager now supports certified integrations with trusted vendors Cohesity and Rubrik . This enables organizations to unify MongoDB backups with the platforms they already use, streamlining operations and reinforcing existing resilience and compliance strategies. Streamlined and secure backups for enterprises As modern applications grow more complex, backup requirements scale alongside them. Enterprises managing multi-terabyte workloads or operating in highly regulated environments often need tailored solutions that match their infrastructure standards and processes. Policies may also require cold storage, where backup snapshots are stored for the long term. Cohesity DataProtect and Rubrik Security Cloud are two trusted solutions for securely backing up large volumes of data and recovering with minimal downtime. While MongoDB Ops Manager offers native backup features, these integrations provide alternatives for customers with specific vendor preferences or compliance mandates without compromising on resilience or speed. These integrations enable customers to run MongoDB on-premises or in private or hybrid clouds and: Reduce complexity by consolidating backup management into existing enterprise tools. Streamline recovery using familiar vendor platforms optimized for scale. Support compliance through enterprise-grade features like backup immutability and policy enforcement. Deliver greater support for sophisticated backup policies, including long-term storage of snapshots. Easy startup Getting started with third-party backup integrations for MongoDB Enterprise Advanced is straightforward. While the bulk of the configuration is handled on the backup provider’s side (Cohesity DataProtect or Rubrik Security Cloud), there are a few setup steps within Ops Manager to ensure a successful integration: Enabling the integration: Setting a feature flag to enable third-party backup management. Generating API keys: Creating global and project-level API keys to enable secure communication with MongoDB Ops Manager. Installing MongoDB Agents: Deploying the MongoDB Ops Manager agent on each server in the cluster. Setting permissions: Verifying that agents have read/write access to the configured directory. Connecting third-party software: Using the generated API keys to integrate with Cohesity DataProtect or Rubrik Security Cloud. Synchronizing system clocks: Ensuring consistent timestamps across machines using Network Time Protocol. Configuring the oplog export path: Defining a directory for MongoDB to store oplog data. Activating monitoring and backup: Turning on both services for each server. Marking the deployment as third-party managed: Using the UI or API to flag the cluster. For detailed setup and integration guidance, refer to the MongoDB Ops Manager documentation , as well as the Cohesity demo and Rubrik demo . With these steps complete, backup operations are managed through the third-party platform—no additional complexity inside MongoDB. For more information on these integrations, check out the announcements from Cohesity and Rubrik .
Now in Public Preview: The MongoDB for IntelliJ Plugin
The MongoDB for IntelliJ plugin empowers Java developers to build and ship applications quickly and confidently by enhancing the Database Explorer experience in the IntelliJ IDEA. After first announcing the plugin in private preview at .local London in the fall of 2024, we’ve partnered with our friends at JetBrains to release a new and improved experience in public preview. Using the MongoDB for IntelliJ plugin, developers can analyze their application code alongside their database, accelerating query development, validating accuracy, and highlighting anti-patterns with proactive performance insights. What’s in the MongoDB for IntelliJ plugin? As part of the public preview, we’re committed to ensuring that the MongoDB for IntelliJ plugin not only meets developers' technical requirements but also paves the way for a seamless developer experience with MongoDB Atlas . The MongoDB for IntelliJ plugin Public Preview offers developers the following capabilities: Field-level autocompletion for Java queries - Auto-suggests field names from MongoDB collections as developers write queries. Schema and type validation - Surfaces inline warnings when query values don’t match the expected field type based on the collection schema, and validates that a field exists in your collection’s schema. Java query execution in IntelliJ console - Allows developers to test Java queries with a single click without needing to switch tools or translate syntax. Proactive anti-pattern detection - Identifies potential performance issues (such as a query missing an index) and provides inline warnings and documentation links. Spring and Java driver support - Supports query syntax across popular Java patterns, criteria API, and aggregation patterns. Code smarter with your AI - Plugin-generated linting insights help your in-IDE AI assistant detect and resolve code issues. Figure 1. Code smarter with your AI. Benefits of using the official MongoDB for IntelliJ plugin Java development often involves working with complex, evolving data models, making MongoDB’s flexible document model an ideal choice for Java applications' data layer. The plugin provides developers with a unified experience for building with MongoDB directly inside IntelliJ, enabling faster and more focused development. By eliminating the need to switch between IntelliJ and external tools, the plugin streamlines query development and testing workflows. Features like field-level autocomplete and inline schema validation reduces errors before runtime, allowing developers to build and validate MongoDB queries with confidence and speed. Whether writing queries with the MongoDB Java driver, Spring Data, or aggregation pipelines, the plugin provides context-aware suggestions and real-time feedback that accelerate development. Additionally, the plugin proactively flags common MongoDB query anti-patterns—such as missing indexes or inefficient operators—within your line of code, helping teams catch performance issues before they hit production. With the ability to test queries directly in the IntelliJ MongoDB console and view execution metadata like query plans and durations, the plugin brings performance awareness and code correctness to where developers actually write the code for their applications. How to get started with the MongoDB for IntelliJ plugin You can get started using the MongoDB for IntelliJ plugin through the JetBrains marketplace . Questions? Feedback? Please post on our community forums or through UserVoice . We value your input as we continue to develop a compelling offering for the Java community.
Introducing Kingfisher: Real-Time Secret Detection and Validation
Foreword from Kingfisher’s developer As a Staff Security Engineer at MongoDB, I spend a lot of time thinking about how to further harden the environments that our customers rely on to protect their data. Central to that is detecting and managing exposed secrets before they turn into security risks. My role involves using an array of tools, from static code analyzers 1 to secrets managers. 2 However, I have never been fully satisfied with the tools at my disposal. Frustrated by the performance issues, limited flexibility, and high false positive rates of existing open source secret scanners, I started building my own tool in July 2024. Ten months later, that project became Kingfisher , an open-source secret scanner that goes beyond detection. It also verifies the validity of the secrets it detects. What began as a pet project has grown into a core component of MongoDB’s internal security workflows. Kingfisher now helps MongoDB’s engineering teams rapidly scan and verify secrets across Git repositories, directories, and more. Kingfisher, along with moving to short-term credentials, is our answer to the growing challenges of stolen credentials and credential-stuffing attacks. I am happy to announce that we are now releasing Kingfisher to the broader community so all developers and security teams can benefit from it. And by releasing Kingfisher as open source, we’re continuing a tradition that goes back to MongoDB’s roots—empowering developers through open, accessible tools. What is Kingfisher? Kingfisher is a high-performance, open-source secret scanning tool that combs through code repositories, Git commit histories, and file systems. Kingfisher performs this to rapidly uncover hard-coded credentials, API keys, and other sensitive data. It can be used seamlessly across GitHub and GitLab repositories, both remote and local, as well as files and directories on disk, helping security teams quickly catch exposed secrets wherever they live. However, Kingfisher goes a step beyond traditional secret scanners. Most tools simply flag anything that may look like a secret, which means engineers need to sift through false positives. Kingfisher is different. It actively validates the secrets it detects by testing them against external systems, such as the relevant cloud services or API endpoints. This dynamic approach helps identify which secrets are truly active and, thus, high-risk. Figure 1. An example of an active AWS secret access key detected and validated by Kingfisher. Figure 2. An example of an inactive Slack app token discovered and validated by Kingfisher. Figure 3. An example scan summary produced by Kingfisher showing one active secret and four inactive secrets detected. Kingfisher is designed for on-premises use, running entirely within the user’s own infrastructure. As a result, discovered secrets never leave the environment or pass through a third-party service. This ensures that developers and security teams retain full control over sensitive data without inheriting a third party’s security posture or introducing yet another external store of credentials. Kingfisher is also cloud-agnostic: It verifies credentials from AWS, Azure, Google Cloud, and any other platform in use. Unlike cloud provider-specific tools that overlook cross-cloud risks, Kingfisher supports security teams’ unified visibility and control, no matter where secrets live. Built with both performance and security in mind, Kingfisher combines extremely fast pattern matching, source code parsing, entropy analysis, and real-time validation. This all reduces noise to surface only what actually matters. It is designed for practical, real-world use, whether scanning a single repo or integrating it into a larger CI/CD pipeline. Why MongoDB built Kingfisher The threat landscape is constantly evolving, and credential-related attacks are on the rise. Stolen credentials are frequently sold on underground markets. Attackers use automated tools to launch credential-stuffing attacks that can lead to unauthorized access and serious data breaches. Traditional secret-scanning tools have not kept up. Such tools often flood teams with false positives, are slow to run, and do not confirm whether a detected secret remains active or dangerous. This means developers and security teams waste time and effort chasing down dead ends while missing actual threats. Kingfisher was built to solve this challenge. It is fast, lightweight, and designed to detect exposed secrets. It then validates them in real time by checking whether the secret remains active. By cutting through the noise and focusing on active risks, Kingfisher enables teams to respond faster and protect systems effectively. Kingfisher also helps security teams progress toward higher Supply-chain Levels for Software Artifacts (SLSA) compliance. It does this by supporting secure configuration management through proactive detection and verification of exposed secrets across codebases and repositories. At the foundational level, it supports SLSA’s core requirement of preventing secrets from being embedded in source code. This is one of the most common and critical vulnerabilities in the software supply chain. For organizations targeting SLSA Levels 2 and above, Kingfisher also helps strengthen source code integrity by reducing the risk of malicious or accidental secret exposure, which could compromise the trustworthiness of builds. Secure configuration management is a critical part of achieving higher SLSA levels. Kingfisher helps teams adopt these best practices by helping keep secrets out of source code and managing them securely throughout the development lifecycle. Figure 4. Runtime chart comparing Kingfisher with two other popular open-source secrets scanning tools. The runtime chart above presents the results of internal testing conducted by MongoDB engineers. It compares Kingfisher against two other popular open-source secret scanning tools: TruffleHog and GitLeaks . In this comparison, lower runtime values indicate superior performance. This underscores Kingfisher’s balance of speed and robust, real-time secret validation. How Kingfisher works Kingfisher is built in Rust, which was chosen for its speed, safety, and concurrency capabilities. Initially inspired by and built on top of a forked version of the Apache 2 licensed " Nosey Parker " code, Kingfisher re-engineers and extends its foundation with modern, high-performance technologies. Kingfisher’s features include: table, th, td { border: 1px solid black; border-collapse: collapse; } th, td { padding: 5px; } Feature Description Rust-powered performance Writing Kingfisher in Rust maximizes performance while providing memory safety. This makes it ideal for scanning large codebases without sacrificing reliability. High-speed regex matching with Hyperscan Kingfisher uses Hyperscan to handle complex and high-volume pattern matching. This engine delivers high-speed regular expression matching that enables real-time scanning on the largest code repositories. Multi-language source parsing with Tree-sitter Kingfisher employs Tree-sitter to parse source code accurately across 20+ programming languages. This enables Kingfisher to understand language-specific syntax, reducing false positives and improving detection accuracy. Efficient scanning engine In addition to its advanced parsing and regex capabilities, Kingfisher uses multi-threaded scanning to traverse files, commit histories, and binary blobs. Custom-built rules combine pattern matching with Shannon entropy checks 3 , flagging only high-confidence secret exposures. Dynamic validation Once a potential secret is detected, Kingfisher validates it by performing external checks. This includes testing database connectivity and calling cloud service APIs to confirm whether the secret is active and poses an immediate risk. Extensible rulesets Kingfisher supports a rich set of rules defined in YAML files. These rules describe the patterns and metadata to look for. This includes confidence levels, examples, and dependency rules to provide nuanced secret detection and validation. Integration ready Kingfisher is designed to be easily integrated into automated CI/CD pipelines and be used in conjunction with GitHub’s secret scanning program. This enhances its role as part of a comprehensive security strategy. How MongoDB uses Kingfisher internally At MongoDB, Kingfisher plays a critical role in safeguarding code repositories and internal systems. As part of the company's comprehensive security strategy, Kingfisher is used across various stages of MongoDB’s development and deployment pipeline. This helps secure MongoDB’s codebase and complements our move away from long-lived secrets. Below are four key ways Kingfisher is used at MongoDB: Pre-commit scanning: MongoDB developers run Kingfisher locally to catch accidentally hard-coded secrets before they commit code. CI/CD integration: Kingfisher is integrated into MongoDB’s continuous integration and deployment (CI/CD) pipelines. Thus, it automatically ensures that every build is scanned for potential secret exposure. Historical code analysis: Kingfisher scans Git commit histories to identify and remediate legacy exposures in MongoDB’s code repositories. Cloud and database validation: Kingfisher automatically tests whether a detected credential is still valid using its dynamic validation capabilities. This allows MongoDB engineers to take immediate action if a secret has been compromised. Get started with Kingfisher The development—and now release—of Kingfisher represents a major leap forward in MongoDB’s approach to securing code and infrastructure. More than a tool, it embodies our ongoing commitment to contribute open-source solutions that empower organizations to protect their critical assets against evolving cyber threats. Kingfisher builds on a solid foundation and introduces significant improvements. This includes: Real-time secret validation Enhanced accuracy with source code parsing with Tree-sitter, Over 700 rules for detecting and validating a broader range of secrets, Cross-platform support for macOS, Linux, and Windows To learn more about Kingfisher and start using it in your own workflows, visit our GitHub repository for detailed documentation and join the community discussions. 1 Tools that examine source code without executing it to identify potential errors, vulnerabilities, or code quality issues. 2 Tools used to securely store, manage, and access sensitive information like API keys, credentials, and tokens. 3 A method of measuring randomness in a string, often used to identify high-entropy values like passwords or API keys that may indicate a secret.
Digital Receipts: Mining for Customer & Business Insight with MongoDB
Imagine walking out of your favorite store and moments later receiving a personalized recommendation for a matching item, based not only on what you just bought, but your entire purchase history. This level of tailored experience has long been difficult to achieve in brick-and-mortar retail, but that’s changing thanks to digital receipts. Digital receipts are gaining traction, with Realtimes UK reporting that a quarter of UK retailers now offer them exclusively . In physical stores, traditional paper receipts represent missed opportunities: static, one-time records that serve little purpose beyond proof of purchase. In contrast, digital receipts unlock a dynamic stream of customer insights, which are a gateway to AI-powered personalization, enabling retailers to transform sales data into timely, relevant recommendations. Retailers are also seeing greater adoption of their customer loyalty apps by embedding features like digital receipts and personalized offers, giving shoppers more reasons to engage after leaving the store. Retailers are increasingly investing in digital receipts, and MongoDB enables them to digitize in-store transactions, understand shopper behavior, and deliver personalized product suggestions immediately after checkout. With MongoDB’s flexible document model , retailers can efficiently store and analyze rich transactional data, powering real-time personalization and adaptive customer experiences. It’s a smarter, data-driven approach to customer engagement, built for the physical retail world. The challenge in capturing the in-store customer journey Personalized shopping experiences are a proven driver of customer loyalty and revenue, but to deliver them effectively, retailers need a complete view of each customer’s journey. For retailers who have a brick-and-mortar presence, that’s where the gap lies. Today, many retailers are making personalization decisions based on incomplete data. While loyalty programs and customer profiles may capture some purchase history, in-store transactions often go unrecorded or take too long to turn into actionable insights. Paper receipts dominate the checkout process, and without a digital trail, these interactions are lost to the retailer’s systems. This means that even a highly engaged, in-store shopper may appear invisible when it comes to targeting and recommendations. The impact of this is twofold. First, it limits the retailer’s ability to offer relevant product suggestions, personalized promotions, or timely follow-ups, missing key opportunities to increase basket size and repeat visits. Second, it affects the customer experience, particularly in the retailer’s mobile app. Shoppers who frequent physical stores often find that their app doesn’t reflect their recent purchases or preferences, making it feel disconnected and less useful. By digitizing receipts, retailers can close this gap. Every in-store purchase becomes a rich source of insight, directly tied to the customer profile. This enables more accurate, real-time personalization, both right after checkout and in future interactions. It also adds meaningful value to the retailer’s mobile app: customers see their full purchase history, receive smarter recommendations, and access personalized offers that feel relevant. The business impact is significant: better personalization drives more revenue, while a more engaging app experience leads to higher adoption, increased usage, and stronger loyalty. Getting the most out of day-to-day data: Building a digital receipt solution Retailers aiming to enhance personalization must first digitize in-store transactional data, particularly the information generated at checkout from point-of-sale (POS) systems. However, the majority of existing POS systems have fixed, non-changeable data formats, designed primarily for payment processing. These systems often vary across store locations, lack integration with customer profiles, and don't support rapid data access. To address these challenges, retailers should centralize transaction data from all stores into a consistent and accessible format. Ensuring each purchase is reliably linked to a customer identity, through loyalty sign-ins or digital prompts, and storing that information in a manner that supports immediate, personalized engagement is crucial. Integration with POS systems is essential, allowing retailers to capture transaction data instantly and store it. A flexible document model (like MongoDB’s) stores structured, unstructured, and AI-ready data in one format, making it ideal for managing complex customer profiles and purchase histories. It captures detailed transaction data, including items, prices, context, and nested info like product attributes, preferences, and loyalty activity, all within a single document. Figure 1. MongoDB’s document model contains the data used to render the digital receipts. This image shows how MongoDB's document model supports digital receipts by instantly ingesting all receipt details. It features a MongoDB document (left) containing both purchased product information and personalized recommendations, and the digital receipt on PDF (right). It also makes the data instantly usable for personalization engines and AI models, without the need for heavy transformation or complex joins across multiple systems. Should the retailer have several different brands or types of PoS systems which data in different formats, the flexible document model allows them to be combined more easily, including fast onboarding if new types are introduced. Seamless integration allows connectivity with existing POS systems and third-party analytics tools, reducing friction in adoption. MongoDB enables this through features like real-time data ingestion with change streams, flexible data connectors for systems like Kafka, and an API-driven approach that supports REST. Combined with MongoDB Atlas ’s multi-cloud deployment support, retailers can connect and scale across diverse infrastructures without needing to re-architect their existing systems. Retailers can surface digital receipts directly in the customer-facing app, enhancing the post-purchase experience. Shoppers gain instant access to their full purchase history, enabling features like receipt lookups, easy reorders, warranty tracking, and personalized product suggestions. This drives more app adoption and keeps customers engaged beyond the store visit. To support this experience at scale, retailers need an architecture that can handle high volumes of receipt data from numerous store locations. MongoDB Atlas supports this through horizontal scalability and workload isolation, ensuring operational workloads like customer app interactions remain fast and reliable as data grows. Some retailers optimize storage by keeping receipt metadata in MongoDB while storing the full receipt in an object store like Azure Blob Storage or Google Cloud Storage, enabling a cost-effective approach. Figure 2. Architecture diagram showing the Digital Receipts components. MongoDB’s ability to serve real-time queries with low latency ensures that every tap or search in the app feels instant, helping reinforce customer trust and satisfaction. This makes the app not just a digital companion but a key driver of loyalty and repeat visits. By making digital receipts easily accessible in the app, alongside personalized recommendations and seamless post-purchase interactions, retailers create a more engaging and convenient experience that keeps customers coming back. Increased app adoption leads to more touchpoints, better data collection, and more opportunities to upsell or cross-sell, ultimately boosting revenue and retention. A notable example of a retailer leveraging MongoDB for digital receipts is Albert Heijn, the largest supermarket chain in the Netherlands . By utilizing MongoDB Atlas, Albert Heijn developed a digital receipts feature within their customer-facing app, providing shoppers with real-time and historical insights into their in-store purchases. This adoption of MongoDB Atlas led to annual savings of 25%, improved developer productivity, and a more efficient customer experience. Retailers use digital receipt data to improve personalized recommendations by combining purchase history, preferences, and behavior. Digitized receipts enable tracking of items, frequency, and context, allowing real-time linking of in-store purchases to customer profiles for more accurate, timely offers. Figure 3. Diagram showing the Digital Receipts process flow. The image illustrates the digital receipts process: 1. A customer makes a purchase in-store, 2. receives a digital receipt via email or SMS, 3. verifies it through an app, 4. accesses purchase history and personalized recommendations, and 5. can repurchase items through the app. Using MongoDB’s aggregation pipelines and change streams, retailers can process data efficiently and enable AI-driven personalization immediately after checkout. This streamlined handling of structured and unstructured receipt data supports rapid analysis of customer preferences and purchasing patterns. MongoDB's workload isolation ensures that analytical processes do not impact the performance of customer-facing applications, maintaining a seamless user experience. Retailers can enhance customer engagement by leveraging this data to offer personalized promotions, loyalty rewards, and cross-selling opportunities. Ready to embrace digital receipts? Digital receipts are reshaping how brick-and-mortar retailers unlock customer insights and deliver AI-driven personalization. With MongoDB Atlas, retailers can instantly analyze transactional data, customer preferences, and purchase history within a flexible document model, powering real-time, tailored recommendations that increase basket size, drive repeat purchases, and boost conversions. Beyond personalization, digital receipts reduce printing costs and support sustainability by eliminating paper waste, while offering customers a convenient, app-based way to access and search past purchases. The real value lies in the data: by capturing rich, real-time insights from every in-store transaction, retailers can unify physical and digital touchpoints, improving customer engagement and business agility. MongoDB’s scalable architecture and real-time processing empower retailers to adapt quickly to changing behavior and deliver seamless, data-driven experiences. Now is the time to modernize your customer engagement strategy. Digital receipts aren’t just a convenience; they’re a competitive advantage! Discover how MongoDB Atlas can help you deliver seamless customer experiences across all channels through our solutions page .
PointHealth AI: Scaling Precision Medicine for Millions
For years, the healthcare industry has grappled with a persistent, frustrating challenge: the absence of a unified, precise approach to patient treatment. Patients often endure "trial-and-error prescribing," leading to delayed recovery and a system bogged down by inefficiency. The core problem lies in scaling precision medicine—making advanced, individualized care accessible to millions of people. This was the big obstacle that Rachel Gollub, CTO and co-founder of the VC-backed startup PointHealth AI , set out to overcome. With a vision to integrate precision medicine into mainstream healthcare, Gollub and her team are transforming how care is delivered, a mission significantly bolstered by their pivotal partnership with MongoDB . Uncovering the gaps in healthcare treatment decisions Over a decade working within the insurance industry, Gollub and her co-founder, Joe Waggoner, observed a frustrating reality: persistent gaps in how treatment decisions were made. This wasn't just about inefficiency; it directly impacted patients, who often experienced "trial-and-error prescribing" that delayed their recovery. As Gollub states, they witnessed "the frustrating gaps in treatment decision-making." It motivated them to seek a better solution. The fundamental challenge they faced was scaling precision medicine. How could something so powerful be made accessible to millions rather than just a select few hundred? The biggest obstacle wasn't solely about the technology itself; it was about seamlessly integrating that technology into existing healthcare workflows. How PointHealth AI eliminates treatment guesswork PointHealth AI's approach involves a proprietary AI reinforcement learning model. This system analyzes a range of data, including similar patient cases, detailed medical histories, drug interactions, and pharmacogenomic insights. When a physician enters a diagnosis into their health record system, PointHealth AI generates a comprehensive patient report. This report offers tailored treatments, actionable insights, and clinical considerations, all designed to guide decision-making. Gollub explains the company’s mission: "to integrate precision medicine into mainstream healthcare, ensuring every diagnosis leads to the right treatment from the start." Its focus is on "eliminating guesswork and optimizing care from the very first prescription." The objective is "to deliver personalized, data-driven treatment recommendations." Its strategy for implementation involves direct partnerships with insurance companies and employers. By embedding its technology directly into these healthcare workflows, PointHealth AI aims to ensure widespread accessibility across the entire system. It’s also collaborating with health systems, electronic health record (EHR) companies, and other insurers. The natural choice: Why PointHealth AI chose MongoDB Atlas A significant enabler of this progress has been PointHealth AI's partnership with MongoDB. Gollub's prior experience with both self-hosted and managed MongoDB provided confidence in its performance and reliability. MongoDB Atlas was a "natural choice" when selecting a data platform for PointHealth AI. It offered the features the team was looking for, including vector search , text search , and managed scalability . The provision of Atlas credits also swayed the decision. PointHealth AI had specific requirements for its data platform. It needed "high security, HIPAA compliance, auto-scaling, fast throughput, and powerful search capabilities." The fact that MongoDB Atlas provided these features within a single, managed solution was huge. MongoDB Atlas ensures seamless backups and uptime through its managed database infrastructure. Its vector and text search capabilities are critical for effectively training AI models. The scaling experience has been "seamless," according to Gollub. The MongoDB team has offered "invaluable guidance in architecting a scalable system." This support has enabled PointHealth AI to optimize for performance while remaining on budget. Gollub emphasizes that "HIPAA compliance, scalability, expert support, and advisory sessions have all played critical roles in shaping our infrastructure." The MongoDB for Startups program has proven impactful. The "free technical advisor sessions provided a clear roadmap for our database architecture." The Atlas credits offered flexibility, allowing the team to "fine-tune our approach without financial strain." Furthermore, the "invaluable expert recommendations and troubleshooting support from the MongoDB advisor team" have been a vital resource. Gollub extends a "huge thank you to the MongoDB Atlas team for their support in building and scaling our system, and handling such an unusual use case." From pilots to Series A: PointHealth AI's next steps Looking forward, PointHealth AI has an ambitious roadmap for the current year. Its focus includes launching pilot installations and expanding partnerships with insurance and EHR companies. It’s also dedicated to refining its AI model to support a wider range of health conditions beyond depression. The overarching goal is to bring "precision-driven treatment recommendations to physicians and patients." The aim, Gollub said, is to "launch successful pilots, acquire new customers, and complete our Series A round." As Gollub states, "Precision medicine isn’t the future—it’s now." The team possesses the technology to deliver targeted treatment options, aiming to ensure patients receive the correct care from the outset. Their vision is to shape a healthcare system where personalized treatments are the standard. Visit PointHealth AI to learn more about how this innovative startup is making advanced, individualized care accessible to millions. Join the MongoDB for Startups program to start building faster and scaling further with MongoDB!
What I Wish I’d Known Before Becoming a Solutions Architect
My journey to becoming a solutions architect (SA) has been anything but straightforward. After working as an engineer in telecom, receiving my PhD in computer science, and spending time in the energy efficiency and finance industries, I joined MongoDB to work at the intersection of AI and data solutions, guiding enterprises to success with MongoDB’s flexible, scalable database platform. It’s a role that requires having both deep technical knowledge and business acumen, and while the nature of the SA role has evolved over time, one thing has remained constant: the need to understand people, their problems, and how the technology we use can solve them. As I reflect on my career journey, here are some key lessons I’ve learned about being an SA—and things I wish I’d known when I first started. 1. Influence comes from understanding In my earlier roles, I thought that presenting clients with a perfect technical solution was the key to success. However, I quickly learned that being a successful solutions architect requires much more than technical excellence. Instead, the solutions that you offer need to be aligned with customers’ business needs. You also need to understand the underlying challenges driving the conversation. In my role, I frequently work with clients facing complex data challenges, whether in real-time analytics, scaling operations, or AI applications. The first step is always understanding their business goals and technical pain points, which is more important than simply proposing the “best” solution. By stepping back and listening, you can not only better design a solution that addresses their needs but also gain their trust. I’ve found that the more I understand the context, the better I can guide clients through the complexities of data architecture—whether they're building on MongoDB Atlas, optimizing for performance, or leveraging our data products to drive innovation. What I wish I’d known: Influence doesn’t come from showing how much you know—it comes from showing how much you understand. Listening is your most powerful design tool. 2. Building champions drives success You can build the most scalable, secure, and elegant system in the world — but if it doesn’t align with stakeholder priorities, it will stall. In reality, architecture is rarely a purely technical exercise. Success depends on alignment with a diverse set of stakeholders, each with their own priorities. Whether you're collaborating with engineering teams, product managers, security specialists, or leadership, the key to success is to engage everyone early and often. Stakeholders are not just passive recipients of your solution; they are active participants who co-own the outcome. In many cases, your design will be shaped by their feedback, and finding a champion within the organization can make all the difference. This champion—whether from the technical side or the business side—will help advocate for your solution internally, align the team, and overcome any resistance. This is particularly important for MongoDB SAs because we’re often addressing diverse needs, from data privacy concerns to performance scalability. Building a strong internal advocate ensures that your design gains the necessary momentum and credibility within the client’s organization. What I wish I’d known: Success doesn’t come from being right—it comes from being aligned. Influence is earned through empathy, clarity, and trust. As a solutions architect, your greatest value is not just in solving technical problems—it’s in helping diverse teams pull in the same direction. And nothing accelerates that more than having a strong, trusted internal champion on your side. 3. Winning deals requires teamwork At MongoDB, we’re not just selling a product—we’re selling a solution. Winning deals involves close collaboration with Sales, Engineering, and Client Services. The most successful deals come when the entire team is aligned, from understanding the customer’s unique needs to crafting a solution that fits their long-term goals. You want to win? Here’s what that actually looks like: You prep with sales like it’s a final exam. Know the account history, know the politics, know what was promised six months ago that never landed. Be the person who connects past pain to future value. You do dry runs and anticipate the tough questions. Then you hand those questions to someone else on your team who can knock them out of the park. That’s trust. You turn strategy decks into conversations . A flashy diagram is great, but asking “Does this actually solve the headache you told us about last week?” — that’s where momentum starts. You loop in Professional Services early to pressure-test feasibility. You loop in CSMs to ask, “If we win this, what does success look like a year from now?” You help sales write the follow-up — not just with a thank-you, but with a crisp summary of what we heard, what we proposed, and what comes next. You make the path forward obvious. One of the most valuable lessons I’ve learned is that winning a deal doesn’t rely solely on delivering a flawless demo. It’s the little things that matter—anticipating questions, making quick adjustments based on client feedback, and being agile in your communication. Being part of a unified team that works seamlessly together is the key to winning deals and ensuring client success. What I wish I’d known: Winning a deal is a series of micro-decisions made together, not a solo act. Great architecture doesn’t close a deal—great alignment does. Your best asset isn’t the system you design—it’s the trust you build with your team and the confidence you project to your client that we’ve got this. Together. 4. You don’t have to know everything When I first transitioned into this role, I felt the pressure to master every piece of the tech stack—especially at MongoDB, where our solutions touch on everything from cloud data platforms to AI, real-time data processing, and beyond. It was overwhelming to think that I needed to be an expert in all of it. But here’s the truth: As a solutions architect, your real value lies not in knowing every detail, but in understanding how the pieces fit together. You don’t need to be the deepest expert in each technology—what’s important is knowing how MongoDB’s platform integrates with client needs and when to bring in the right specialists. The role is about connecting the dots, asking the right questions, and collaborating across teams. The more you embrace curiosity and rely on your colleagues, the better your solutions will be. What I wish I’d known: Mastery isn’t about knowing all the answers. It’s about knowing which questions to ask, and who to ask them to. Focus on principles, patterns, and clarity. Let go of the pressure to be the smartest person at the table—you’re there to make the table work better together. Curiosity is your compass, and collaboration is your fuel. 5. Architecture lives beyond the diagram When most people think of a solutions architect, they picture designing systems, building technical architectures, and drawing elegant diagrams. While that’s part of the job, the true value lies in how well those designs are communicated, understood, and adopted by the client. Specifically, your architecture needs to work in real-world scenarios. You’re not just drawing idealized diagrams on a whiteboard—you’re helping clients translate those ideas into actionable steps. That means clear communication, whether through shared documentation, interactive walkthroughs, or concise explanations. Understanding your client’s needs and constraints is just as important as the technical design itself. And when it comes to sizing and scaling, MongoDB’s flexibility makes it easy to adapt and grow as the business evolves. What I wish I knew: Architecture doesn’t end at the diagram—it begins there. The real value is realized in how well the design is communicated, contextualized, sized, and adopted. Use whatever format helps people get it. And before you document the system, understand the system of people and infrastructure you’re building it for. 6. It’s not just about data Data may be the foundation of my work as a solutions architect, but the real magic happens when you connect with people. Being a great architect means being a great communicator, listener, and facilitator. You’ll frequently find yourself between business leaders seeking faster insights and developers looking for the right data model. Translating these needs and building consensus is a big part of the role. The solutions we design are only valuable if they meet the diverse needs of the client’s teams. Whether it’s simplifying data operations, optimizing query performance, or enabling AI-driven insights, your ability to connect with stakeholders and address their unique challenges is key. Emotional intelligence, empathy, and collaboration are essential. What I wish I’d known: Being a great architect means being a great communicator, listener, and facilitator. Emotional intelligence is your secret weapon. The more time you invest in understanding your stakeholders’ pain points, motivations, and language, the more successful your architecture will be—because people will actually use it. 7. The job is constantly evolving and so are you The field of data architecture is rapidly evolving, and MongoDB is at the forefront of this change. From cloud migrations to AI-driven data products, the technology landscape is always shifting. As a solutions architect, you have to be adaptable and prepared for the next big change. At MongoDB, we work with cutting-edge technologies and constantly adapt to new trends, whether it’s AI, machine learning, or serverless computing. The key is to embrace change and continuously learn. The more you stay curious and open to new ideas, the more you’ll grow in your role and your ability to drive client success. As MongoDB continues to innovate, the learning curve is steep, but that’s what keeps the job exciting. What I wish I knew: You don’t “arrive” as a solutions architect—you evolve. And that evolution doesn’t stop. But everything you learn builds on itself. No effort is wasted. Every challenge adds depth. Every mistake adds clarity. The technologies may change, but the thinking compounds—and that’s what makes you valuable over the long run. It’s not just a role–it’s a journey Reflecting on my path to becoming a solutions architect at MongoDB, I realize that the journey is far from linear. From network protocols to financial systems and AI-driven data solutions, each role added a new layer to my experience. Becoming a solutions architect didn’t mean leaving behind my past—it meant integrating it into a broader perspective. At MongoDB, every day brings new challenges and opportunities. Whether you’re designing a solution for a global enterprise or helping a startup scale their data operations, the core of the job remains the same: solving problems, connecting people, and helping others succeed. And as you grow in the role, you’ll find that the most powerful thing you bring to the table isn’t just your expertise—it’s your ability to keep learning, to show up with intention, and to simplify complexity for everyone around you. To anyone stepping into this role at MongoDB: welcome. The journey is just beginning! 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Navigating the AI Revolution: The Importance of Adaptation
In 1999, Steve Ballmer gave a famous speech in which he said that the “key to industry transformation, the key to success is developers developers developers developers developers developers developers, developers developers developers developers developers developers developers! Yes!” A similar mantra applies when discussing how to succeed with AI: adaptation, adaptation, adaptation! Artificial intelligence has already begun to transform how we work and live, and the changes AI is bringing to the world will only accelerate. Businesses rely ever more heavily on software to run and execute their strategies. So, to keep up with competitors, their processes and products must deliver what end-users increasingly expect: speed, ease of use, personalization—and, of course, AI features. Delivering all of these things (and doing so well) requires having the right tech stack and software foundation in place and then successfully executing. To better understand the challenges organizations adopting AI face, MongoDB and Capgemini recently worked with the research organization TDWI to assess the state of AI readiness across industries. The road ahead Based on a survey “representing a diverse mix of industries and company sizes,” TDWI’s “The State of Data and Operational Readiness for AI ” contains lots of super interesting findings. One I found particularly compelling is the percentage of companies with AI apps in production: businesses largely recognize the potential AI holds, but only 11% of survey respondents indicated that they had AI applications in production. Still only 11%! We’re well past the days of exploring whether AI is relevant. Now, every organization sees the value. The question is no longer ‘if’ but ‘how fast and how effectively’ they can scale it. Mark Oost, VP, AI and Generative AI Group Offer Leader, Capgemini There’s clearly work to be done; data readiness challenges highlighted in the report include managing diverse data types, ensuring accessibility, and providing sufficient compute power. Less than half (39%) of companies surveyed manage newer data formats, and only 41% feel they have enough compute. The report also shows how much AI has changed the very definition of software, and how software is developed and managed. Specifically, AI applications continuously adapt, and they learn and respond to end-user behavior in real-time; they can also autonomously make decisions and execute tasks. All of which depends on having a solid, flexible software foundation. Because the agility and adaptability of software are intrinsically linked to the data infrastructure upon which it's built, rigid legacy systems cannot keep pace with the demands of AI-driven change. So modern database solutions (like, ahem, MongoDB)—built with change in mind—are an essential part of a successful AI technology stack. Keeping up with change The tech stack can be said to comprise three layers: at the “top,” the interface or user experience layer; then the business logic layer; and a data foundation at the bottom. With AI, the same layers are there, but they’ve evolved: Unlike traditional software applications, AI applications are dynamic . Because AI-enriched software can reason and learn, the demands placed on the stack have changed. For example, AI-powered experiences include natural language interfaces, augmented reality, and those that anticipate user needs by learning from other interactions (and from data). In contrast, traditional software is largely static: it requires inputs or events to execute tasks, and its logic is limited by pre-defined rules. A database underpinning AI software must, therefore, be flexible and adaptable, and able to handle all types of data; it must enable high-quality data retrieval; it must respond instantly to new information; and it has to deliver the core requirements of all data solutions: security, resilience, scalability, and performance. So, to take action and generate trustworthy, reliable responses, AI-powered software needs access to up-to-date, context-rich data. Without the right data foundation in place, even the most robust AI strategy will fail. Figure 1. The frequency of change across eras of technology. Keeping up with AI can be head-spinning, both because of the many players in the space (the number of AI startups has jumped sharply since 2022, when ChatGPT was first released 1 ), and because of the accelerating pace of AI capabilities. Organizations that want to stay ahead must evolve faster than ever. As the figure above dramatically illustrates, this sort of adaptability is essential for survival. Execution, execution, execution But AI success requires more than just the right technology: expert execution is critical. Put another way, the difference between success and failure when adapting to any paradigm shift isn’t just having the right tools; it’s knowing how to wield those tools. So, while others experiment, MongoDB has been delivering real-world successes, helping organizations modernize their architectures for the AI era, and building AI applications with speed and confidence. For example, MongoDB teamed up with the Swiss bank Lombard Odier to modernize its banking tech systems. We worked with the bank to create customizable generative AI tooling, including scripts and prompts tailored for the bank’s unique tech stack, which accelerated its modernization by automating integration testing and code generation for seamless deployment. And, after Victoria’s Secret transformed its database architecture with MongoDB Atlas , the company used MongoDB Atlas Vector Search to power an AI-powered visual search system that makes targeted recommendations and helps customers find products. Another way MongoDB helps organizations succeed with AI is by offering access to both technology partners and professional services expertise. For example, MongoDB has integrations with companies across the AI landscape—including leading tech companies (AWS, Google Cloud, Microsoft), system integrators (Capgemini), and innovators like Anthropic, LangChain, and Together AI. Adapt (or else) In the AI era, what organizations need to do is abundantly clear: modernize and adapt, or risk being left behind. Just look at the history of smartphones, which have had an outsized impact on business and communication. For example, in its Q4 2007 report (which came out a few months after the first iPhone’s release), Apple reported earnings of $6.22 billion, of which iPhone sales comprised less than 2% 2 ; in Q1 2025, the company reported earnings of $124.3 billion, of which 56% was iPhone sales. 3 The mobile application market is now estimated to be in the hundreds of billions of dollars, and there are more smartphones than there are people in the world. 4 The rise of smartphones has also led to a huge increase in the number of people globally who use the internet. 5 However, saying “you need to adapt!” is much easier said than done. TWDI’s research, therefore, is both important and useful—it offers companies a roadmap for the future, and helps them answer their most pressing questions as they confront the rise of AI. Click here to read the full TDWI report . To learn more about how MongoDB can help you create transformative, AI-powered experiences, check out MongoDB for Artificial Intelligence . P.S. ICYMI, here’s Steve Ballmer’s famous “developers!” speech. 1 https://ourworldindata.org/grapher/newly-funded-artificial-intelligence-companies 2 https://www.apple.com/newsroom/2007/10/22Apple-Reports-Fourth-Quarter-Results/ 3 https://www.apple.com/newsroom/pdfs/fy2025-q1/FY25_Q1_Consolidated_Financial_Statements.pdf 4 ttps://www.weforum.org/stories/2023/04/charted-there-are-more-phones-than-people-in-the-world/ 5 https://ourworldindata.org/grapher/number-of-internet-users
Luna AI and MongoDB Throw Lifeline to Product Teams
Product and engineering leaders face a constant battle: making crucial real-time decisions amidst a sea of fragmented, reactive, and disconnected progress data. The old ways—chasing updates, endlessly pinging teams on Slack, digging through Jira, and enduring endless status meetings—simply aren't cutting it. This struggle leaves product and engineering leads wasting precious hours on manual updates, while critical risks silently slip through the cracks. This crucial challenge is precisely what Luna AI , powered by its robust partnership with MongoDB , is designed to overcome. Introducing Luna AI: Your intelligent program manager Luna AI was founded to tackle this exact problem, empowering product and engineering leaders with the visibility and context they need, without burying their PMs in busy work. Imagine having an AI program manager dedicated to giving you clear insights into goals, roadmap ROI, initiative progress, and potential risks throughout the entire product lifecycle. Luna AI makes this a reality by intelligently summarizing data from your existing tools like Jira and Slack. It can even automatically generate launch and objective and key result (OKR) status updates, create your roadmap, and analyze your Jira sprints, drastically reducing the need for manual busywork. From concept to command center: The evolution of Luna AI Luna AI’s Co-founder, Paul Debahy, a seasoned product leader with experience at Google, personally felt the pain of fragmented data during his time as a CPO. Inspired by Google's internal LaunchCal, which provided visibility into upcoming launches, Luna AI initially began as a launch management tool. However, a key realization quickly emerged: Customers primarily needed help "managing up." This insight led to a pivotal shift, focusing Luna AI on vertical management—communicating status, linking execution to strategy, and empowering leaders, especially product leaders, to drive decisions. Today, Luna AI has evolved into a sophisticated AI-driven insights platform. Deep Jira integration and advanced LLM modules have transformed it from a simple tracker into a strategic visibility layer. Luna AI now provides essential capabilities like OKR tracking, risk detection, resource and cost analysis, and smart status summaries. Luna AI believes product leadership is increasingly strategic, aiming to be the system of record for outcomes, not just tasks. Its mission: to be everyone’s AI program manager, delivering critical strategy and execution insights for smarter decision-making. The power under the hood: Building with MongoDB Atlas Luna AI’s robust technology stack includes Node.js, Angular, and the latest AI/LLM models. Its infrastructure leverages Google Cloud and, crucially, MongoDB Atlas as its primary database. When selecting a data platform, Luna AI prioritized flexibility, rapid iteration, scalability, and security. Given the dynamic, semi-structured data ingested from diverse sources like Jira, Slack, and even meeting notes, a platform that could handle this complexity was essential. Key requirements included seamless tenant separation, robust encryption, and minimal operational overhead. MongoDB proved to be the perfect fit for several reasons. The developer-friendly experience was a major factor, as was the flexible schema of its document database, which naturally accommodated Luna AI’s complex and evolving data model. This flexibility was vital for tracking diverse information such as Jira issues, OKRs, AI summaries, and Slack insights, enabling quick adaptation and iteration. MongoDB also offered effortless support for the startup’s multi-tenant architecture. Scaling with MongoDB Atlas has been smooth and fast, according to Luna AI. Atlas effortlessly scaled as the company added features and onboarded workspaces ranging from startups to enterprises. The monitoring dashboard has been invaluable, offering insights that helped identify performance bottlenecks early. In fact, index suggestions from the dashboard directly led to significant improvements to speed. Debahy even remarked, "Atlas’s built-in insights make it feel like we have a DB ops engineer on the team." Luna AI relies heavily on Atlas's global clusters and automated scaling . The monitoring and alerting features provide crucial peace of mind, especially during launches or data-intensive tasks like Jira AI epic and sprint summarization. The monitoring dashboard was instrumental in resolving high-latency collections by recommending the right indexes. Furthermore, in-house backups are simple, fast, and reliable, with painless restores offering peace of mind. Migrating from serverless to dedicated instances was seamless and downtime-free. Dedicated multi-tenant support allows for unlimited, isolated databases per customer. Auto-scaling is plug-and-play, with Atlas handling scaling across all environments. Security features like data-at-rest encryption and easy access restriction management per environment are also vital benefits. The support team has consistently been quick, responsive, and proactive. A game-changer for startups: The MongoDB for Startups program Operating on a tight budget as a bootstrapped and angel-funded startup, Luna AI found the MongoDB for Startups program to be a true game changer. It stands out as one of the most founder-friendly programs the company has encountered. The Atlas credits completely covered the database costs, empowering the team to test, experiment, and even make mistakes without financial pressure. This freedom allowed them to scale without worrying about database expenses or meticulously tracking every compute and resource expenditure. Access to technical advisors and support was equally crucial, helping Luna AI swiftly resolve issues ranging from load management to architectural decisions and aiding in designing a robust data model from the outset. The program also opened doors to a valuable startup community, fostering connections and feedback. Luna AI’s vision: The future of product leadership Looking ahead, Luna AI is focused on two key areas: Building a smarter, more contextual insights layer for strategy and execution. Creating a stakeholder visibility layer that requires no busy work from product managers. Upcoming improvements include predictive risk alerts spanning Jira, Slack, and meeting notes. They are also developing ROI-based roadmap planning and prioritization, smart AI executive status updates, deeper OKR traceability, and ROI-driven tradeoff analysis. Luna AI firmly believes that the role of product leadership is becoming increasingly strategic. With the support of programs like MongoDB for Startups, they are excited to build a future where Luna AI is the definitive system of record for outcomes. Ready to empower your product team? Discover how Luna AI helps product teams thrive. Join the MongoDB for Startups program to start building faster and scaling further with MongoDB!
Mongoose Now Natively Supports QE and CSFLE
Mongoose 8.15.0 has been released, which adds support for the industry-leading encryption solutions available from MongoDB. With this update, it’s simpler than ever to create documents leveraging MongoDB Queryable Encryption (QE) and Client-Side Level Field Encryption (CSFLE), keeping your data secure when it is in use. Read on to learn more about approaches to encrypting your data when building with MongoDB and Mongoose. What is Mongoose? Mongoose is a library that enables elegant object modeling for Node.js applications working with MongoDB. Similar to an Object-Relational Mapper (ORM), the Mongoose Object Document Mapper (ODM) simplifies programmatic data interaction through schemas and models. It allows developers to define data structures with validation and provides a rich API for CRUD operations, abstracting away many of the complexities of the underlying MongoDB driver. This integration enhances productivity by enabling developers to work with JavaScript objects instead of raw database queries, making it easier to manage data relationships and enforce data integrity. What is QE and CSFLE? Securing sensitive data is paramount. It must be protected at every stage—whether in transit, at rest, or in use. However, implementing in-use encryption can be complex. MongoDB offers two approaches to make it easier: Queryable Encryption (QE) and Client-Side Level Field Encryption (CSFLE). QE allows customers to encrypt sensitive application data, store it securely in an encrypted state in the MongoDB database, and perform equality and range queries directly on the encrypted data. An industry-first innovation, QE eliminates the need for costly custom encryption solutions, complex third-party tools, or specialized cryptography knowledge. It employs a unique structured encryption schema, developed by the MongoDB Cryptography Research Group , that simplifies the encryption of sensitive data while enabling equality and range queries to be performed directly on data without having to decrypt it. The data remains encrypted at all stages, with decryption occurring only on the client side. This architecture supports solidified strict access controls, where MongoDB and even an organization’s own database administrators (DBAs) don’t have access to sensitive data. This design enhances security by keeping the server unaware of the data it processes, further mitigating the risk of exposure and minimizing the potential for unauthorized access. Adding QE/CSFLE auto-encryption support for Mongoose The primary goal of the Mongoose integration with QE and CSFLE is to provide idiomatic support for automatic encryption, simplifying the process of creating encrypted models. With native support for QE and CSFLE, Mongoose allows developers to define encryption options directly within their schemas without the need for separate configurations. This first-class API enables developers to work within Mongoose without dropping down to the driver level, minimizing the need for significant code changes when adopting QE and CSFLE. Mongoose streamlines configuration by automatically generating the encrypted field map. This ensures that encrypted fields align perfectly with the schema and simplifies the three-step process typically associated with encryption setup, shown below. Mongoose also keeps the schema and encrypted fields in sync, reducing the risk of mismatches. Developers can easily declare fields with the encrypt property and configure encryption settings, using all field types and encryption schemes supported by QE and CSFLE. Additionally, users can manage their own encryption keys, enhancing control over their encryption processes. This comprehensive approach empowers developers to implement robust encryption effortlessly while maintaining operational efficiency. Pre-integration experience const kmsProviders = { local: { key: Buffer.alloc(96) }; const keyVaultNamespace = 'data.keys'; const extraOptions = {}; const encryptedDatabaseName = 'encrypted'; const uri = '<mongodb URI>'; const encryptedFieldsMap = { 'encrypted.patent': { encryptedFields: EJSON.parse('<EJSON string containing encrypted fields, either output from manual creation or createEncryptedCollection>', { relaxed: false }), } }; const autoEncryptionOptions = { keyVaultNamespace, kmsProviders, extraOptions, encryptedFieldsMap }; const schema = new Schema({ patientName: String, patientId: Number, field: String, patientRecord: { ssn: String, billing: String } }, { collection: 'patent' }); const connection = await createConnection(uri, { dbName: encryptedDatabaseName, autoEncryption: autoEncryptionOptions, autoCreate: false, // If using createEncryptedCollection, this is false. If manually creating the keyIds for each field, this is true. }).asPromise(); const PatentModel = connection.model('Patent', schema); const result = await PatentModel.find({}).exec(); console.log(result); This example demonstrates the manual configuration required to set up a Mongoose model for QE and CSFLE, requiring three different steps to: Define an encryptedFieldsMap to specify which fields to encrypt Configure autoEncryptionOptions with key management settings Create a Mongoose connection that incorporates these options This process can be cumbersome, as it requires explicit setup for encryption. New experience with Mongoose 8.15.0 const schema = new Schema({ patientName: String, patientId: Number, field: String, patientRecord: { ssn: { type: String, encrypt: { keyId: '<uuid string of key id>', queries: 'equality' } }, billing: { type: String, encrypt: { keyId: '<uuid string of key id>', queries: 'equality' } }, } }, { encryptionType: 'queryableEncryption', collection: 'patent' }); const connection = mongoose.createConnection(); const PatentModel = connection.model('Patent', schema); const keyVaultNamespace = 'client.encryption'; const kmsProviders = { local: { key: Buffer.alloc(96) }; const uri = '<mongodb URI>'; const keyVaultNamespace = 'data.keys'; const autoEncryptionOptions = { keyVaultNamespace, kmsProviders, extraOptions: {} }; await connection.openUri(uri, { autoEncryption: autoEncryptionOptions}); const result = await PatentModel.find({}).exec(); console.log(result); This "after experience" example showcases how the integration of QE and CSFLE into Mongoose simplifies the encryption setup process. Instead of the previous three-step approach, developers can now define encryption directly within the schema. In this implementation, fields like ssn and billing are marked with an encrypt property, allowing for straightforward configuration of encryption settings, including the keyId and query types. The connection to the database is established with a single call that includes the necessary auto-encryption options, eliminating the need for a separate encrypted fields map and complex configurations. This streamlined approach enables developers to work natively within Mongoose, enhancing usability and reducing setup complexity while maintaining robust encryption capabilities. Learn more about QE/CSFLE for Mongoose We’re excited for you to build secure applications with QE/CSFLE for Mongoose. Here are some resources to get started with: Learn how to set up use Mongoose with MongoDB through our tutorial. Check out our documentation to learn when to choose QE vs. CSFLE . Read Mongoose CSFLE documentation .