Recap of Product Announcements at MongoDB.local NYC, 2023

Leo Zheng
June 22, 2023 | Updated: November 1, 2023

This post is also available in: Deutsch, Français, Español, Português, 中文

Today at MongoDB.local NYC, we unveiled several new capabilities across our developer data platform to help users and customers build, iterate, and scale their applications with MongoDB.

Development teams are being asked to deliver compelling and differentiated user experiences that are faster and smarter than ever before. At the same time, they must do so as quickly and as efficiently as possible.

MongoDB’s developer data platform is essential for teams who strive to innovate quickly and efficiently. It allows developers to support a wide variety of application use cases in their organization through a unified API and eliminates the need to bolt-on, learn, and maintain separate database point solutions.

Expanding the range of modern applications you can build on MongoDB

A new generation of AI-powered experiences are being developed where vectors serve as a foundational element that makes these applications possible. Vectors are mathematical representations of the characteristics of unstructured data — including text, images, videos, audio files, and more — and occupy n-dimensional space where n is the number of characteristics in the dataset. Whether data is similar or not is based on the distance between vectors in this n-dimensional vector space. A vector database allows users to query vectors to determine what’s similar or related, without relying on keyword matching. Announced today, Atlas Vector Search allows you to store, index, and query vectors alongside your operational and transactional data in documents without the overhead of adding, learning, and maintaining another database system. The addition of vector search to MongoDB Atlas enables teams to deliver more relevant, context-aware results to end-users, including the ability to augment applications built on Large Language Models (LLMs) with proprietary data to improve accuracy and performance. Atlas Vector Search is available today in public preview.

Applications today also need to be more real-time than ever, but processing streams of data and bringing them into applications is complex and challenging. Most organizations introduce a stream processing point solution with a different set of APIs, drivers, and tools that create a fragmented developer experience, operational complexity, and additional cost. Coming soon in private preview, Atlas Stream Processing will transform how development teams build event-driven applications. Developers can use the same query language and flexible document data model to work with both streaming data and their data in the database for use cases ranging from monitoring network traffic for intrusions to conducting live route planning based on current road conditions.

Companies such as Albertsons, Glassdoor, and Anywhere Real Estate, the largest franchisor of residential real estate brands in the world, depend on Atlas Search to deliver full-text functionality in their applications without having to deploy and sync data from their database to a separate search engine. With new search query analytics, developers will gain insight into what their end users are searching for, enabling them to better refine and customize their search logic. In addition, Atlas Search indexes can now be created and managed in language drivers (starting with Node.js), MongoDB Compass, and the MongoDB Shell, making it easy for developers who prefer to work with their indexes programmatically. And finally, dedicated search nodes were announced today in private preview, which will enable teams to independently scale and optimize resources for their search workloads for improved performance at scale, higher availability, and faster index builds.

Improving the foundation of performance, scale and security

The work we’re putting into improving the performance, scalability, and security of our developer data platform is just as important as increasing its range of functional capabilities.

Starting with MongoDB 7.0, improvements to query execution will reduce the number of disk reads, compute resources, and memory required to execute certain queries, delivering improved performance and more efficient resource consumption. In particular, the new query execution strategy will speed up grouping and reshaping documents, filtering and sorting documents, and $lookups used for joining data across collections.

Last year we introduced queryable encryption in preview. This industry-first searchable encryption scheme allows users to encrypt sensitive data fields — such as personally identifiable information — client-side, and store that data as fully randomized encrypted data in the database, all while preserving the ability to run expressive queries. With MongoDB 7.0, queryable encryption will support equality search, with support for range, prefix, suffix, and substring queries to follow.

MongoDB 7.0 will be generally available later this summer.

Continued focus on a first-class developer experience

Few things are more critical to software development than ensuring a top-notch developer and operational experience. We remain dedicated to improving the usability of our products and expanding the set of tools available to developers for building with MongoDB.

Kotlin is emerging as a popular language for both mobile development and server-side development. Today we unveiled a new official Kotlin driver, allowing Kotlin developers to build applications on MongoDB with confidence, knowing we are committed to supporting this fast-growing language community. We also released PyMongoArrow, a new library for developers and data analysts to easily export data in MongoDB to Python-based analytics stacks, including Apache Arrow, Pandas, and NumPy.

As more companies optimize their DevOps pipeline, tooling that enables programmatic automation can greatly improve efficiency and productivity. Developers can now provision resources on MongoDB Atlas using the Amazon Web Services Cloud Development Kit (AWS CDK) in C#, Go, Java, and Python, as well as with Node.js and Typescript. Companies who leverage Kubernetes can use the MongoDB Atlas CLI to install the Atlas Kubernetes Operator and export existing deployments for simpler infrastructure management and provisioning.

Path to application modernization

Application modernization continues to be a key investment for many enterprises to unlock rapid software iteration to meet ever-evolving application requirements. MongoDB Relational Migrator, now generally available, helps accelerate and de-risk migrations from common relational databases — including Oracle, SQL Server, MySQL, and PostgreSQL — to MongoDB. Not only does this tool handle the data migration itself, it also allows migration teams to view MongoDB data modeling recommendations, define schema changes with a visual interface, and generate application code in their programming language or framework to get a head start on refactoring their applications to reflect the newly designed MongoDB schema.

To see more announcements and get the latest product updates, visit our What’s New page.

Head to the MongoDB.local hub to see where we'll be showing up next.

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News From MongoDB.local NYC: Game-changing Migration Tool Now Available

This post is also available in: Deutsch , Français , Español , Português The database has always been the heart of an application. Teams recognize that the choice of a database is one of the most critical decisions in developing software, influencing not only the speed at which developers can build and iterate, but also an application’s performance, functionality, and overall user experience. Today, tens of thousands of organizations are being held back by their legacy systems built on relational databases. They struggle with the headaches, high costs, and poor developer agility caused by data silos and architecture sprawl built up over decades as they try to address new requirements and build transformative experiences. Modernizing the data infrastructure means reducing architectural complexity, and adopting flexible data platforms like MongoDB that can support a wider range of use cases and data types. But every IT leader knows that transitioning away from legacy databases is easier said than done, and that they are often overwhelming and time-consuming projects. This is even more true when trying to shake the relational database mindset and instead adopt a developer-first approach to data. MongoDB's Relational Migrator is now available to help organizations streamline their transition from relational databases to MongoDB by tackling some of the most common challenges we see in migration projects: effective data modeling, migrating data, and modernizing app code. Today, Relational Migrator supports migrations from Oracle, SQL Server, MySQL, and PostgreSQL directly. By switching to MongoDB, companies can reap major rewards when it comes to modernizing an essential component of their software. Not only do they enjoy faster development and performance, but also improved reliability, scalability, and user experience. Additionally, MongoDB's advanced features, such as full-text search, time-series data support, and edge-to-cloud synchronization enable applications to take on more use cases on an efficient, easy-to-maintain data infrastructure. With Relational Migrator, teams preparing for app modernization projects can accelerate and reduce the risk of their relational database migrations so they can start taking advantage of everything MongoDB has to offer. Take the guesswork out of the migration process Data modeling in MongoDB is one of the most common barriers we see when organizations come over from a relational background. MongoDB can be treated like a relational database, but to unlock the full potential of the document model organizations need to change how they think about their data and model it for how their applications are going to use it. Relational Migrator takes the guesswork out of data modeling by analyzing an application's existing relational schema and proposing a recommended MongoDB schema based on best practices. As teams use the visual interface to compare and build their schema, Relational Migrator will continue to offer recommended mappings to further expedite schema design, but more importantly, help developers do it well. Not only does Relational Migrator visually compare schemas, but it also applies the transformations as it moves data into MongoDB in an efficient manner, providing users with the ability to run both systems in parallel and apply changes from the source database to MongoDB on an ongoing basis. Migrating data is only one part of application modernization. To give developers a head start, Relational Migrator even generates app code for various development languages and frameworks that reflects the new MongoDB schema they’ve designed. With the launch of Relational Migrator, MongoDB is unlocking the full potential of its powerful developer data platform , providing new implementation possibilities for customers and a wider range of applications. Learn more about what Relational Migrator can do for you. Head to the MongoDB.local hub to see where we'll be showing up next.

June 22, 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