MongoDB 7.0 Now Available

Steve Jurczak
September 6, 2023 | Updated: September 8, 2023

MongoDB 7.0 is now available for download. Scalability was one of the driving forces behind the release, which we announced as generally available (GA) at our MongoDB.local event held in Chicago. MongoDB 7.0 makes it easier and faster to horizontally scale large MongoDB deployments. Other highlights of our newest major release include:

New aggregation operators allow developers to write simpler code and push more processing down to the database.
Optimized query execution logic leverages fewer disk reads, fewer compute resources, and less memory.
Cluster-to-cluster sync capabilities make it easier to manage data across deployments for a wider range of use cases.

Delivering the keynote address in Chicago was MongoDB Chief Product Officer, Sahir Azam. In addition to the MongoDB 7.0 major release, Azam announced the general availability of our groundbreaking Queryable Encryption, the industry's first database technology to bring expressive queries to in-use data encryption.

Developers can rest easy knowing that our stable API ensures backwards compatibility with prior versions of Atlas so you can move through newer versions without having to make application changes at the same time. That's just one of several reasons to upgrade now.

The show marches on

Instead of holding one blow-out event in a single city like we did last year, this year we're coming out to see you and to tell you all about the exciting things happening at MongoDB. First came the announcement of Atlas Vector Search at MongoDB.local New York, then the general availability of MongoDB 7.0 in Chicago, and we're just getting started. We will have more exciting product announcements at our next big MongoDB.local event to be held in London. Each event includes technical sessions and demonstrations for developers of all levels hosted by our own MongoDB experts, and London will be no different. The event will be held September 26, 2023 at Tobacco Dock, Tobacco Quay from 8 a.m. to 5:30 p.m. BST, followed by a closing reception. Sessions are tailored for developers, architects, DevOps engineers, and anyone working with or learning MongoDB. You'll learn technologies, tools, and best practices that make it easy to build data-driven applications and get to connect with our experts and community to find new ways to build with MongoDB.

Featured sessions at MongoDB.local London will include End-to-End Design by Boris Bialek, Managing Director, Industry Solutions for MongoDB, and Implementing Time-Series by Cristopher Stauffer, Director of Product Management for MongoDB. Visit our event page for a full agenda, to register, and to get tips on how to convince your boss that you should attend.

Where in the world?

If you're not anywhere near London, check out our list of MongoDB.local events in major cities all around the world and sign up now.

Want to find out what you missed at our recent MongoDB.local events? You can catch any session you missed from MongoDB.local New York or MongoDB.local Chicago on the MongoDB YouTube channel. Or read up on some of our big announcements, like the private preview of MongoDB Atlas Stream Processing, the general availability of MongoDB Relational Migrator, and 4 Big Reasons to Upgrade to MongoDB 7.0.

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Building AI with MongoDB: How VISO TRUST is Transforming Cyber Risk Intelligence

NOTE: We have an updated VISO TRUST story, which goes into the latest and greatest with the VISO TRUST team. You can read the latest customer story here . Since announcing MongoDB Atlas Vector Search preview availability back in June, we’ve seen rapid adoption from developers building a wide range of AI-enabled apps. Today we're going to talk to one of these customers. VISO TRUST puts reliable, comprehensive, actionable vendor security information directly in the hands of decision-makers who need to make informed risk assessments. The company uses state-of-the-art models from Amazon Bedrock, augmented by vector search and retrieval from MongoDB Atlas. We sat down with Pierce Lamb, Senior Software Engineer on the Data and Machine Learning team at VISO TRUST to learn more. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Tell us a little bit about your company. What are you trying to accomplish and how that benefits your customers or society more broadly? 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 the fast and accurate intelligence needed to make informed cybersecurity risk decisions at scale for companies at any maturity level. Our commitment to innovation means that we are constantly looking for ways to optimize business value for our customers. VISO TRUST ensures that complex business-to-business (B2B) transactions adequately protect the confidentiality, integrity, and availability of trusted information. VISO TRUST’s mission is to become the largest global provider of cyber risk intelligence and become the intermediary for business transactions. Through the use of VISO TRUST, customers will reduce their threat surface in B2B transactions with vendors and thereby reduce the overall risk posture and potential security incidents like breaches, malicious injections, and more. 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. Because it’s the only approach that can deliver accurate results at scale, for the first time, customers are able to gain complete visibility into their entire third-party populations and take control of their third-party risk. Describe what your application does and what role AI plays in it The VISO TRUST Platform approach uses patented, proprietary machine learning and a team of highly qualified third-party risk professionals to automate this process at scale. Simply put, VISO TRUST automates vendor due diligence and reduces third-party at scale. And security teams can stop chasing vendors, reading documents, or analyzing spreadsheets. Figure 1: 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 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 – we call Artifact Intelligence – does the rest. Security artifacts that enter VISO’s Artifact Intelligence pipeline interact with AI/ML in three primary ways. First, VISO deploys discriminator models that produce high-confidence predictions about features of the artifact. For example, one model performs artifact classification, another detects organizations inside the artifact, another predicts which pages are likely to contain security controls, and more. Our modules reference a comprehensive set of over 25 security frameworks and use document heuristics and natural language processing to analyze any written material and extract all relevant control information. Secondly, artifacts have text content parsed out of them in the form of sentences, paragraphs, headers, table rows, and more; these text blobs are embedded and stored 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’s risk model analyzes your risk and delivers a complete assessment that provides everything you need to know to make qualified risk decisions about the relationship. In addition, the platform continuously monitors and reassesses third-party vendors to ensure compliance. What specific AI/ML techniques, algorithms, or models are utilized in your application? For our discriminator models, we research the state-of-the-art pre-trained models (typically narrowed by those contained in HuggingFace’s transformers package) and perform fine-tuning of these models using our dataset. For our dense retrieval system, we use MongoDB Atlas Vector Search which internally uses the Hierarchical Navigable Small Worlds algorithm to retrieve similar embeddings to embedded text content. We have plans to perform a re-ranking of these results as well. Today we are using Amazon Bedrock exclusively for all GenAI needs. In the past we have used OpenAI and Anthropic and tested Vertex/Bard. We are now exclusively on Bedrock and do not use any other subservicers. Can you describe other AI technologies used in your application stack? Some of the other frameworks we use are HuggingFace transformers, evaluate, accelerate, and Datasets, PyTorch, WandB, and Amazon Sagemaker. We have a library for ML experiments (fine-tuning) that is custom-built, a library for workflow orchestration that is custom-built, and all of our prompt engineering is custom-built. Why did you choose MongoDB as part of your application stack? Which MongoDB features are you using and where are you running MongoDB? The VISO TRUST Platform relies on effective solutions and tools like MongoDB's distinctive attributes to fulfill specific objectives. MongoDB supports our platform's mechanism to engage third parties efficiently, employing both AI and human oversight to automate the assessment of security artifacts at scale. The fundamental value proposition of MongoDB – a robust document database – is why we originally chose it. It was originally deployed as a storage/retrieval mechanism for all the factual information our artifact intelligence pipeline produces about artifacts. While it still performs this function today, it has now become our “vector/metadata database.” MongoDB executes fast ranking of large quantities of embedded text blobs for us while Atlas provides us with all the ease-of-use of a cloud-ready database. We use both the Atlas search index visualization, and the query profiler visualization daily. Even just the basic display of a few documents in collections often saves time. Finally, when we recently backfilled embeddings across one of our MongoDB deployments, Atlas would automatically provision more disk space for large indexes without us needing to be around which was incredibly helpful. What are the benefits you've achieved by using MongoDB? I would say there are two primary benefits that have greatly helped us with respect to MongoDB and Atlas. First, MongoDB was already a place where we were storing metadata about artifacts in our system; with the introduction of Atlas Vector Search now we have a comprehensive vector/metadata database – that’s been battle-tested over a decade – that solves our dense retrieval needs. No need to deploy a new database we have to manage and learn. Our vectors and artifact metadata can be stored right next to each other. Second, Atlas has been helpful in making all the painful parts of database management easy. Creating indexes, provisioning capacity, alerting slow queries, visualizing data, and much more have saved us time and allowed us to focus on more important things. What are your future plans for new applications and how does MongoDB fit into them? Retrieval-augmented generation is going to continue to be a first-class feature of our application. In this regard, the evolution of Atlas Vector Search and its ecosystem in MongoDB will be highly relevant to us. MongoDB has become the database our ML team uses, so as our ML footprint expands, our use of MongoDB will expand. Getting started Thanks so much to Pierce for sharing details on VISO TRUST’s AI-powered applications and experiences with MongoDB. The best way to get started with Atlas Vector Search is to head over to the product page . There you will find tutorials, documentation, and whitepapers along with the ability to sign up for MongoDB Atlas. You’ll just be a few clicks away from spinning up your own vector search engine where you can experiment with the power of vector embeddings and RAG. We’d love to see what you build, and are eager for any feedback that will make the product even better in the future!

September 5, 2023

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Payments Modernization and the Role of the Operational Data Layer

To stay relevant and competitive, payment solution providers must enhance their payment processes to adapt to changing customer expectations, regulatory demands, and advancing technologies. The imperative for modernization is clear: payment systems must become faster, more secure, and seamlessly integrated across platforms. Driven by multiple factors—real-time payments, regulatory shifts like Payment Services Directive 2 (PSD2), heightened customer expectations, the power of open banking, and the disruptive force of fintech startups—the need for payment modernization has never been more pressing. But transformation is not without its challenges. Complex systems, industry reliance on outdated technology, high upgrade costs, and technical debt all pose formidable obstacles. This article will explore modernization approaches and how MongoDB helps smooth transformations. Approaches to modernization As businesses work to modernize their payment systems, they need to overcome the complexities inherent in updating legacy systems. Forward-thinking organizations embrace innovative strategies to streamline their operations, enhance scalability, and facilitate agile responses to evolving market demands. Two such approaches gaining prominence in the realm of payment system modernization are domain-driven design and microservices architecture : Domain-driven design: This approach focuses on a business's core operations to develop scalable and easier-to-manage systems. Domain-driven design ensures that technology serves strategic business goals by aligning system development with business needs. At its core, this approach seeks to break down complex business domains into manageable components, or "domains," each representing a distinct area of business functionality. Microservices architecture: Unlike traditional monolithic architectures, characterized by tightly coupled and interdependent components, a microservices architecture decomposes applications into a collection of loosely coupled services, each of which is responsible for a specific business function or capability. It introduces more flexibility and allows for quicker updates, facilitating agile responses to changing business requirements. Discover how Wells Fargo launched their next-generation card payments by building an operational data store with MongoDB . Modernizing with an operational data layer In the payments modernization process, the significance of an operational data layer (ODL) cannot be overstated. An ODL is an architectural pattern that centrally integrates and organizes siloed enterprise data, making it available to consuming applications. The simplest representation of this pattern looks something like the sample reference architecture below. Figure 1: Operational Data Layer structure An ODL is deployed in front of legacy systems to enable new business initiatives and to meet new requirements that the existing architecture can’t handle—without the difficulty and risk of fully replacing legacy systems. It can reduce the workload on source systems, improve availability, reduce end-user response times, combine data from multiple systems into a single repository, serve as a foundation for re-architecting a monolithic application into a suite of microservices, and more. The ODL becomes a system of innovation, allowing the business to take an iterative approach to digital transformation. Here's why an ODL is considered ideal for payment operations: Unified data management: Payment systems involve handling a vast amount of diverse data, including transaction details, customer information, and regulatory compliance data. An ODL provides a centralized repository for storing and managing this data, eliminating silos and ensuring data integrity. Real-time processing: An ODL enables real-time processing of transactions, allowing businesses to handle high numbers of transactions swiftly and efficiently. This capability is essential for meeting customer expectations for instant payments and facilitating seamless transactions across various channels. Scalability and flexibility: Payment systems must accommodate fluctuating transaction volumes and evolving business needs. An ODL offers scalability and flexibility, allowing businesses to scale their infrastructure as demand grows. Enhanced security: An ODL incorporates robust security features —such as encryption, access controls, and auditing capabilities—to safeguard data integrity and confidentiality. By centralizing security measures within the ODL, businesses can ensure compliance with regulatory requirements and mitigate security risks effectively. Support for payments data monetization: Payment systems generate a wealth of data that can provide valuable insights into customer behavior, transaction trends, and business performance. An ODL facilitates real-time analytics and reporting by providing a unified platform for collecting, storing, and analyzing this data. Transform with MongoDB MongoDB’s fundamental technology principles ensure companies can reap the advantages of microservices and domain-driven design—specifically, our flexible data model and built-in redundancy, automation, and scalability. Indeed, the document model is tailor-made for the intricacies of payment data, ensuring adaptability and scalability as market demands evolve. Here’s how MongoDB helps with domain-driven design and microservice implementation to adopt industry best practices: Ease of use: MongoDB’s document model makes it simple to model or remodel data to fit the needs of payment applications. Documents are a natural way of describing data. They present a single data structure, with related data embedded as sub-documents and arrays, making it simpler and faster for developers to model how data in the application will be mapped to data stored in the database. In addition, MongoDB guarantees the multi-record ACID transactional semantics that developers are familiar with, making it easier to reason about data. Flexibility: MongoDB’s dynamic schema is ideal for handling the requirements of microservices and a domain-driven design. Domain-driven design emphasizes modeling the domain to reflect the business requirements, which may evolve over time. MongoDB's flexible schema allows you to store domain objects as documents without rigid schema constraints, facilitating agile development and evolution of the domain model. Speed: Using MongoDB for an ODL means you can get better performance when accessing data, and write less code to do so. A document is a single place for the database to read and write data for an entity. This locality of data ensures the complete document can be accessed in a single database operation that avoids the need internally to pull data from many different tables and rows. Data access and microservice-based APIs: MongoDB integrates seamlessly with modern technologies and frameworks commonly used in microservices architectures. MongoDB's flexible data model and ability to handle various data types, including structured and unstructured data, is a great fit for orchestrating your open API ecosystem to make data flow between banks, third parties, and consumers possible. Scalability: Even if an ODL starts at a small scale, you need to be prepared for growth as new source systems are integrated, adding data volume, and new consuming systems are developed, increasing workload. MongoDB provides horizontal scale-out on low-cost, commodity hardware or cloud infrastructure using sharding to meet the needs of an ODL with large data sets and high throughput requirements. High availability: Microservices architectures require high availability to ensure that individual services remain accessible even in the event of failures. MongoDB provides built-in replication and failover capabilities, ensuring data availability and minimal downtime in case of server failures. Payment modernization is not merely a trend but a strategic imperative. By embracing modern payment solutions and leveraging the power of an ODL with MongoDB, organizations can unlock new growth opportunities, enhance operational efficiency, and deliver superior customer experiences. Learn how to build an operational data layer with MongoDB using this Payments Modernization Solution Accelerator . Learn more about how MongoDB is powering industries on our solution library .

May 15, 2024