Building Modern Applications Faster: New Capabilities at MongoDB.local NYC 2024

Today, we kicked off MongoDB.local NYC and unveiled new capabilities across our developer data platform. The updates and capabilities announced today pave the way for a new era of app modernization and will allow developers to unleash the full potential of transformative technology like AI.

Here’s an overview of our announcements, from a comprehensive update to MongoDB to AI-powered intelligent developer experiences:

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Modern applications need a modern database

Cutting-edge modern applications must deliver both an exceptional experience and additional revenue. To meet these demands, developers require a database solution that offers optimal performance, scale, and operational resilience—while maintaining cost efficiency.

So today, we’re thrilled to announce the preview of MongoDB 8.0—the next evolution of MongoDB’s modern database.

MongoDB 8.0 is focused on delivering unparalleled performance, scalability, security, and operational resilience to support the creation of next-generation applications, including sophisticated AI-driven solutions. It provides optimal performance by dramatically increasing query performance, improving resilience during periods of heavy load, making scalability easier and more cost-effective, and making time series collections faster and more efficient.

Modernizing your next application with MongoDB is now easier

As application modernization projects gain momentum, migrations are becoming a pressing reality for development and database teams. Transitioning from legacy relational systems to modern databases like MongoDB is essential to keeping up with technological shifts like AI. However, modernization and migrations have many challenges, from converting complex schemas and translating large amounts of application code to keeping databases in sync during long modernization projects. Announced in June 2023, MongoDB Relational Migrator streamlines the migration process by automating tasks like schema design, data migrations, and application code generation.

Maintaining data synchronization is paramount in long-running modernization projects—where legacy relational databases must coexist with MongoDB until the project is complete. Today, we are pleased to announce that MongoDB Relational Migrator is now integrated with Confluent Cloud to support long-running change data capture (CDC) sync jobs. These jobs ensure operational resilience and observability, addressing the complexities of phased transitions without the added burden of managing Apache Kafka independently.

Screenshot of the AI-powered SQL Query Converter Tool that lives within MongoDB Relational Migrator

Furthermore, migrating from legacy relational databases often involves significant effort in rewriting SQL queries, stored procedures, and triggers, which has traditionally been time-consuming and difficult. Now available in public preview, an AI-powered SQL Query Converter Tool has been introduced to MongoDB Relational Migrator that automates the process of converting existing SQL queries, stored procedures, and triggers to work with MongoDB in languages like JavaScript, Java, or C#. This streamlined approach—paired with MongoDB professional services—enables a simplified migration process that can scale effectively.

Helping developers build faster with confidence on MongoDB

We recognize the vital role that developers play in the success of every project, which is why we’re dedicated to making their MongoDB experience as seamless as possible. Frameworks are a great way for developers to boost productivity, improve code consistency and quality, and ultimately deliver code faster. For the C# developer community, we are pleased to announce that the MongoDB Provider for Entity Framework Core (EF Core) is now generally available. This allows C# developers building with EF Core to unlock the full power of MongoDB's developer data platform—while still using the EF Core APIs and design patterns they already know and love.

And, recognizing the needs of the PHP community, we’re also proud to introduce the Laravel Aggregation Builder. This feature simplifies the process of building complex aggregation queries within Laravel, the most popular framework among PHP developers. By enhancing the integration of MongoDB with Laravel, we aim to boost productivity and ease the complexity of query operations, ensuring PHP developers can also enjoy an optimized development experience with MongoDB.

Generating queries and visualizations with AI

Since its initial release in 2015, MongoDB Compass has helped developers quickly build and debug queries and aggregations for their application code. Today, MongoDB Compass introduces an AI-powered, natural language query experience, making it even easier for developers to use MongoDB’s powerful Query API. Now generally available, this feature lets developers use natural language to generate executable MongoDB Query API syntax for everything from simple queries to sophisticated aggregations through an intelligent and guided experience. For example, a developer can input "Filter vacation rentals by location, group the remaining documents by number of bedrooms, and calculate the average nightly rental price," MongoDB Compass will suggest code to execute the stages of the aggregation pipeline.

Data visualizations are a powerful way of understanding application data, and embedding charts into user-facing applications further enhances their utility and appeal to developers. However, creating visualizations is often hampered by the need for in-depth knowledge of the dataset and proficiency in using business intelligence tools—skills that many developers may not have. Now available in public preview, we introduced an easy-to-use visualization tool with generative AI capabilities in MongoDB Atlas Charts. Using natural language prompts, developers can easily render charts and build dashboards, making visualizing data and enriching their apps simple and fast. For example, developers can input ‘Show me the list of movies released in the last year sorted by genre,’ and MongoDB Atlas Charts will gather data and quickly generate the requested visualization.

Today’s announcements underscore MongoDB’s commitment to helping developers innovate quickly and easily. For more about the MongoDB.local NYC 2024 updates, check out the product announcements page on our website.

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Welcome to MongoDB.local NYC 2024!

AI promises to upend how enterprises operate and reach customers … if only they could first find the "On" button. Despite the tremendous promise of AI, most companies still find themselves in the experimentation phase, working through proofs of concept, hampered by unfamiliar technologies that don't work well together. But MongoDB is uniquely positioned to help developers turn all this AI noise into "signal" that benefits customers. This week at MongoDB.local NYC, thousands of developers and executives—representing Fortune 500 companies and cutting-edge startups—have gathered to discuss and demonstrate the real-world successes they've had building on MongoDB's developer data platform. MongoDB is fast becoming the industry’s go-to memory database for retrieval-augmented generation (RAG) and agentic systems, offering a unified data model across the entire AI stack. But this isn’t just a technology story, as important as that is. MongoDB also now offers essential programs and services to make AI much more accessible. In short, MongoDB is taking developers from experimentation to impact, and advancing our long-standing mission of making it easy to work with data. Demystifying AI Businesses are eager to adopt generative AI, but they don’t know where to start. The AI landscape is incredibly complex—and seems to get more so by the minute. This complexity, coupled with limited in-house AI expertise and concerns about the performance and security risks of integrating disparate technologies, is keeping too many organizations on the sidelines. MongoDB can help. To get organizations started, we’re announcing the MongoDB AI Applications Program (MAAP) . With MAAP, we give customers the blueprints and reference architectures to easily understand how to build AI applications. We also take on the heavy lifting of integrating MongoDB's developer data platform with leading AI partners like Anthropic, Cohere, Fireworks AI, Langchain, LlamaIndex, Nomic, Anyscale, Credal.ai, and Together AI, all running on the cloud provider of your choice. MAAP will be available to customers in early access starting in July. In addition to MAAP, we’re also introducing two new professional services engagements to help you build AI-powered apps quickly, safely, and cost-effectively: An AI Strategy service that leverages experts to help customers identify the highest-impact AI opportunities and to create specific plans on how to pursue them. For customers who have already identified use cases to pursue, an AI Accelerator service that brings expert consulting—from solution design through prototyping—to enable customers to execute their AI application roadmap from idea to production. Once developers get to building AI apps, they’ll find that MongoDB allows them to speak the data “language” of AI. Our developer data platform unifies all different data types alongside your real-time operational data—including source data, vector embeddings, metadata, and generated data—and supports a broad range of use cases. Not only do we give developers the most intuitive way to work with their data, we also keep improving where they can do so. Many developers first experience MongoDB in a local environment before moving to a fully managed cloud service like MongoDB Atlas. So, I'm excited to share that we will be introducing full-text search and vector search in MongoDB Community Edition later this year, making it even easier for developers to quickly experiment with new features and streamlining end-to-end software development workflows when building AI applications. These new capabilities also enable support for customers who want to run AI-powered apps on devices or on-premises. As customers begin to mature these applications, cost becomes an important consideration. Last year, we introduced dedicated nodes for Atlas Search on AWS. Using dedicated nodes, customers can isolate their vector search workloads and scale them up or down independently from operational workloads, improving performance and ensuring high availability. By giving customers workload isolation without data isolation, they can manage resources efficiently without additional complexity. Today, we’re announcing Atlas Search nodes on all three cloud providers, which customers can configure programmatically using the Atlas CLI or our Infrastructure-as-Code integrations . Learn more about how MongoDB is the best solution to the challenges posed by the fast-moving generative AI landscape . Real-time and highly performant Though AI rightly claims center stage at MongoDB .local NYC this week, it's not the only way we're helping developers. From real-time fraud detection , to predictive maintenance , to content summarization , customers need to efficiently process large volumes of high-velocity data from multiple sources. Today, we’re also announcing the general availability of Atlas Stream Processing , the public preview of Atlas Edge Server , and improved performance of time series workloads with MongoDB 8.0. Together, these capabilities enable customers to design applications that solve virtually any business challenge. Learn more about how MongoDB powers modern application requirements . These are just a few of the things we're announcing this week. Whether you’re just dipping your toes into the world of generative AI or are well on your way, MongoDB’s developer data platform, strong and diverse network of partners, and proven industry solutions will give you a competitive edge in a fast-moving market. Please take a minute to see what we've built for you, so that you can more easily build for your customers. Enjoy the conference, and we hope to see you soon! To see more announcements and get the latest product updates, visit our What’s New page. And head to the MongoDB.local hub to see where we’re stopping along our 2024 world tour.

May 2, 2024
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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