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Enhancing the MongoDB Atlas Go SDK with Automated Code Generation

The MongoDB Atlas API Experience team is committed to offering a seamless developer experience to customers who build and automate against the MongoDB Atlas developer data platform. We offer various programmatic tools, such as the Atlas CLI and the Terraform Atlas Provider , and maintain the Atlas Admin API with key features, including versioning and Open API specification . Our latest offering, the MongoDB Atlas Go SDK , empowers developers to manage Atlas using Go . It eliminates the need for low-level Admin API calls by utilizing higher-level abstractions. New SDK versions are automatically generated on each Atlas update, ensuring access to the latest Atlas administrative capabilities. In this post, we’ll discuss the advantages of using an SDK over direct API calls, explore our decision for code generation over manual development, share insights from engineering challenges, review our adoption experience, and discuss some next steps for the Atlas SDKs. Benefits of using an SDK over direct API calls Direct API calls offer flexibility and fine-grained control but become cumbersome for complex interactions. Developers manually handle tasks like authentication, error handling, and response parsing, which can be time-consuming and error-prone. Additionally, keeping up with API updates can require regular low-level code updates. The Atlas Go SDK simplifies development with higher-level abstractions. Pre-built functions and structs encapsulate API interactions, reducing boilerplate code. This frees developers to focus on core application logic rather than API integration intricacies. The SDK automates authentication elements, error handling, and response parsing, reducing boilerplate code and errors. Plus, staying up-to-date becomes low-effort, as new SDK versions are auto-generated with each Atlas release. While leveraging new functionalities might require some code updates, the SDK significantly simplifies integration compared to direct API calls. Moving from a manual client to an auto-generated SDK Previously, MongoDB’s bespoke Go Client for MongoDB Atlas powered internal tools and customer applications. However, maintaining a manually written client increased toil and impacted the team’s productivity. Keeping code quality high required constant effort, leading to a backlog of unsupported Atlas features and a struggle to keep pace with new functionalities. As Go-based Atlas applications proliferated, the limitations of manual maintenance became clear. We needed a more scalable solution. We had previously built a mechanism to automatically generate the Open API specification for the Atlas Admin API. This machine-readable document details how to interact with the Atlas functionality through its REST API, accessible through the “Download” option in the MongoDB Atlas Administration API portal . This led us to explore leveraging that specification for client code generation as well. Building on this foundation, the Atlas Go SDK leverages the Open API spec and a robust delivery pipeline to streamline client generation. The pipeline scans for changes in the spec and triggers the generation of a new SDK version upon detection, producing a pull request as an intermediate output. Following our review and merge, the pipeline requires no other manual steps to continue working on the release process, ultimately publishing a new SDK version. Code generation has improved output consistency and speed over manual development, freeing up resources for other impactful projects. To showcase the time savings, adding support for a new resource in the old manual Go Client could take roughly two engineer days. The new SDK reduces that time to a less-than-an-hour code review, as all other steps are automated. Major challenges and solutions Transitioning to an auto-generated SDK wasn’t without its hurdles. Selecting the right tooling was crucial, and we explored both commercial and open-source solutions for Open API-based code generation. We opted for openapi-generator due to its open-source nature. The choice prioritized flexibility and control, ensuring long-term project autonomy. It also allowed us to open-source the Go SDK generation codebase, fostering community contributions and improvements. Leveraging the Atlas Open API specification, originally intended for documentation , presented unique challenges. While this approach offered a single source of truth, we encountered discrepancies between the spec and actual API behavior. Notably, minor, non-breaking API changes sometimes resulted in significant, breaking changes in the generated client. For instance, marking a field as optional in the API (non-breaking) turns the equivalent Go model property into a pointer (breaking). This discovery necessitated a two-fold solution. We addressed the root cause by improving the Open API specification, ensuring better alignment with code generation requirements. Then we developed an automated transformation process to optimize the spec for generating code. This transformed version remains optimized for code generation while the original spec continues to serve its original purpose of live API documentation. Having a pull request review as part of the release process has helped us ensure quality. In some early cases, it allowed us to catch issues, apply SDK-wide improvements, and add linting rules to prevent reoccurrence. Maintaining compatibility with the existing Go client was crucial for an easy migration experience. However, achieving perfect compatibility wasn’t always feasible due to inconsistencies in the handwritten client. We meticulously evaluated each change, balancing compatibility with the benefits of code generation. We also created a migration guide and best practices to aid with migrating existing applications. Finally, to minimize confusion and client bloat, we decided that each Go SDK release should target a single Admin API version. That simplified integration but created a versioning challenge: Go’s semantic versioning uses major bumps for breaking changes. We needed to communicate the targeted Atlas API version within the SDK version while simultaneously denoting breaking SDK changes unrelated to API updates. Our solution was to develop a unique major versioning scheme for the Go SDK. It incorporates the Admin API’s date as a prefix, with additional digits signaling breaking SDK changes for that specific API version. While unconventional, it adheres to semantic versioning and keeps developers informed. Adoption experience Our Atlas CLI tool was the proving ground for the new Go SDK. In 2023, it became the first application to migrate from the manual Go client by leveraging pre-release versions of the Go SDK. That staged adoption approach yielded significant benefits. By migrating early, we uncovered valuable improvement areas for the SDK, directly influencing its architecture. This early feedback loop also provided crucial insights into effective SDK generation. The migration wasn’t without its complexities. It addressed cross-cutting issues across three distinct layers: the Open API spec, the SDK generation, and the CLI integration, each presenting unique development challenges. However, this comprehensive approach ensured that all layers benefitted from the process, and we raised quality across the board. Following the successful CLI integration, we confidently expanded the SDK’s reach across our product portfolio. Now, it serves as a prominent and trusted middleware solution, not only for the Atlas CLI , but also for several Atlas DevOps tools. Recent additions include the Atlas integrations for AWS CloudFormation and CDK . The Terraform Atlas Provider and the Atlas Kubernetes Operator are also on the immediate roadmap, further solidifying the SDK as a core component within our DevOps ecosystem. External adoption of the Atlas Go SDK is also gaining momentum. Over 30% of customer-developed Go-based interactions with the Atlas Admin API now leverage the new SDK, a trend we see increasing monthly. Looking ahead As Atlas continues to scale, the pace of features will only continue to accelerate. Our primary focus for the Go SDK is to continuously expose new Atlas features as they are released in the Admin API while introducing quality-of-life improvements for developers at every opportunity. We strive to reduce boilerplate code, improve Error Handling, simplify Authentication / Authorization, and to enrich documentation with high-quality examples. The success of code generation has us exploring what else can be automated. Our team is looking at which other offerings could benefit from automation to free up development time for impactful projects that might be less readily automatable. Finally, we understand that our developer community thrives on various languages and technologies. Not using Go? Let us know what other languages you’d like to see supported (e.g. Python, Java, TypeScript) by sharing your feedback . Conclusion The Atlas Go SDK empowers Go developers to streamline interactions with the Atlas cloud platform. By leveraging code generation and a focus on developer experience, the SDK offers several advantages over manual API calls. Reduced complexity: pre-built functions and higher-level abstractions simplify development by tackling authentication, error handling, and response parsing. Improved maintainability: auto-generation of updated SDK versions with each Atlas release ensures access to the latest functionalities, minimizing manual code changes. Enhanced reliability: Tailored API models promote code reliability and catch potential errors at compile time. Our in-house adoption experience was a valuable proving ground, influencing the SDK’s development and uncovering key optimization points. Today, the SDK is a cornerstone within our DevOps ecosystem, accelerating the development of downstream DevOps tools. This successful transition proved the value of code generation for developer productivity and code quality. It also opened up the possibility of generating SDKs for more programming languages in the future. The Atlas Go SDK can be a valuable asset for Go developers who build solutions on Atlas. To get started with the SDK, see our Docs Page . We also welcome community contributions, so visit our Contributing Guidelines for more details. We invite you to try it , and we would love to hear your feedback. Go build with the new Atlas Go SDK today!

May 22, 2024
Engineering Blog

What’s New From MongoDB at Microsoft Build 2024

This week, thousands of engineers, database administrators, and developers are gathering in Seattle for Microsoft Build , Microsoft’s annual developer event. In addition to being on site for meetings and learning sessions, MongoDB is excited to showcase our latest innovations for building generative AI apps and more. First, we’re happy to announce that MongoDB now offers dedicated Search Nodes on Microsoft Azure . We offer both compute-optimized nodes for text or application search workloads, and memory-optimized nodes for vector, semantic search, or gen AI workloads. Search Nodes enhance performance and availability through workload isolation while reducing architectural complexity. The availability of Search Nodes on Azure is the latest example of how the partnership between MongoDB and Microsoft helps organizations of all sizes boost developer productivity and build modern applications faster. Keep reading for more on how MongoDB’s capabilities and integrations with Microsoft are helping customers create, innovate, and scale applications. Integrating services and technology to speed AI development The last year of AI innovation set a clear imperative for every organization—to meet customer expectations, they need to modernize their applications. However, many companies aren’t sure where to start with AI, so MongoDB recently announced the launch of the MongoDB AI Application Program (MAAP) alongside industry-leading AI partners. MAAP will provide customers with strategic advisory, professional services, and an integrated end-to-end technology stack from MongoDB and key partners like Microsoft. We’ve also made several technology announcements to enable building gen AI applications, including native support for MongoDB Atlas Vector Search in Microsoft Semantic Kernel , and a dedicated MongoDB Atlas integration for OpenAI’s ChatGPT Plugin . With the new integration, developers can seamlessly and securely enhance the power of large language models from OpenAI, Azure OpenAI, and Hugging Face with proprietary data in Atlas to build powerful retrieval-augmented generation applications using Python or C#. Developing faster with intelligent tools and frameworks In addition to helping developers build powerful gen AI applications through services like Atlas Vector Search, we’ve been working to enhance developer productivity, making it easier than ever to build applications with MongoDB. For example, we’ve introduced intelligent features to first-party tools like MongoDB Compass and Atlas Charts that support natural language. We also recently announced AI-powered SQL query conversions in Relational Migrator to help teams easily move their workloads to MongoDB. MongoDB is expanding the use of AI to enhance its integration with the world’s most popular integrated development environment, Visual Studio Code. We’re excited to announce the MongoDB Participant for the Github CoPilot chat experience, designed to empower developers to generate queries from natural language, understand collection schemas, and instantly access MongoDB documentation. Sign up for the private preview here . MongoDB also supports a variety of programming frameworks to improve productivity and accelerate application development—while ensuring data consistency and quality. Now generally available, the MongoDB Provider for Entity Framework Core (EF Core), encourages C# developers to build their next project on MongoDB. This new offering helps C# developers—39% of whom use EF Core—unlock the full power of MongoDB using the EF Core APIs and design patterns they already know and love. Streamlining comprehensive data analysis For years, MongoDB and Microsoft have facilitated the large-scale analysis of application-generated data to aid business development. Tools like Microsoft Power BI provide a comprehensive view of business intelligence data for developers and analysts with complex data estates using relational databases alongside MongoDB. MongoDB’s Power BI Connector for Atlas —previously supporting Import Mode—now also supports DirectQuery, which we announced a few weeks ago at MongoDB.local NYC . This allows for real-time querying of MongoDB data and is ideal for large datasets. To further enable customers working in the Microsoft ecosystem, we’ve recently made Atlas Data Federation and Atlas Online Archive generally available on Azure . These services enable users to query, transform, and create views across multiple Atlas databases and Azure cloud storage solutions, like Blob Storage and Data Lake Storage Gen2, simplifying data management and archiving within the Azure ecosystem. Building the future together MongoDB's partnership with Microsoft has made developing modern applications faster and easier. We're thrilled to announce these new capabilities at Microsoft Build 2024 , and look forward to helping our joint customers build amazing things together this year. “MongoDB’s relationship with Microsoft has never been better, and with these latest integrations, our momentum continues to grow,” said Alan Chhabra, MongoDB’s EVP of Worldwide Partners. “Already, many of the largest enterprises and most advanced startups in the world run MongoDB Atlas on Microsoft Azure. These latest innovations will empower even more of our customers to take full advantage of their data to build truly transformational generative AI-powered applications.” MongoDB’s partnership with Microsoft sets projects up for success today and tomorrow by delivering robust, integrated solutions that cater to developers' needs. MongoDB and Microsoft are pushing the boundaries for innovation and service for the developer community. To learn more about our recent announcements and for the latest product updates, visit our What’s New page. And head to our campaign page to learn more about how to build smarter and develop faster with MongoDB Atlas on Microsoft Azure.

May 21, 2024
Updates

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
Applied

How the NFSA is Using MongoDB Atlas and AI to Make Aussie Culture Accessible

Where can you find everything from facts about Kylie Minogue, to more than 6,000 Australian home movies, to a 60s pop group playing a song with a drum-playing kangaroo ? The NFSA! Founded in 1935, the National Film and Sound Archive of Australia (NFSA) is one of the oldest archives of its kind in the world. It is tasked with collecting, preserving, and sharing Australia’s audiovisual culture. According to its website, the NFSA “represents not only [Australia’s] technical and artistic achievements, but also our stories, obsessions and myths; our triumphs and sorrows; who we were, are, and want to be.” The NFSA’s collection includes petabytes of audiovisual data—including broadcast-quality news footage, TV shows, and movies, high-resolution photographs, radio shows, and video games—plus millions of physical and contextual items like costumes, scripts, props, photographs, and promotional materials, all tucked away in a warehouse. “Today, we have eight petabytes of data, and our data is growing from one to two petabytes each year,” said Shahab Qamar, software engineering manager at NFSA. Making this wealth of data easily accessible to users across Australia (not to mention all over the world) has led to a number of challenges, which is where MongoDB Atlas—which helps developers simplify and accelerate building with data—comes in. Don’t change (but apply a few updates) Because of its broad appeal, the NFSA's collection website alone receives an average of 100,000 visitors each month. When Qamar joined the NFSA in 2020, he saw an opportunity to improve the organization’s web platform. His aim was to ensure the best possible experience for the site’s high number of daily visitors, which had begun to plateau. This included a website refresh, as well as addressing technical issues related to handling site traffic, due to the site being hosted on on-premises servers. The site also wasn’t “optimized for Google Analytics,” said Qamar. In fact, the NFSA website was invisible to Google and other search engines, so he knew it was time for a significant update, which also presented an opportunity to set up strong data foundations to build deeper capabilities down the line. But first, Qamar and team needed to find a setup that could serve the needs of the NFSA and Australia’s 26 million residents more robustly than their previous solution. Specifically, Qamar said, the NFSA was looking for a fully managed database that could also implement search at scale, as well as a system that his small team of five could easily manage. It also needed to ensure high levels of resiliency and the ability to work with more than one cloud provider. The previous NFSA site also didn’t support content delivery networks , he added. MongoDB Atlas supported all of the use cases the NFSA was looking for, Qamar said, including the ability to support multi-cloud hosting. And because Atlas is fully managed, it would readily meet the NFSA's requirements. In July 2023, after months of development, the new and greatly improved NFSA website was launched in July 2023. The redesign was immediately impactful: Since the NFSA’s redesigned site was launched, the number of users visiting the collection search website has gone up 200%, and content requests—which the NFSA access team responds to on a case-by-case basis—have gone up 16%. (Getting search) back in black While the previous version of the NFSA site included search, the prior functionality was prone to crashing, and the quality of the results was often poor, Qamar said. For example, search results were delivered alphabetically rather than based on relevance, and the previous search didn’t support fine-tuning of relevance based on matches in specific fields. So, as part of its site redesign, the NFSA was looking to add full text search, relevance-based search results, faceting, and pagination. MongoDB Atlas Search —which integrates the database, search engine, and sync mechanism into a single, unified, fully managed platform—ticked all of those boxes. A search results page on the NFSA website Indeed, the NFSA compared search results from its old site to its new MongoDB Atlas site and “found that MongoDB Atlas-based searches were more relevant and targeted,” Qamar said. Previously, configuring site search required manual coding and meant downtime for the site, he noted. “The whole setup wasn’t very developer friendly and, therefore, a barrier to working efficiently with search configuration and fine-tuning,” Qamar said. In comparison, MongoDB Atlas allowed for simple configuration and fine-tuning of the NFSA's search requirements. The NFSA has also been using MongoDB Atlas Charts . Charts help the NFSA easily visualize its collection by custom grouping (like production year or genre), as well as helping the NFSA see which items are most popular with users. “Charts have helped us understand how our collection is growing and evolving over time,” Qamar said. NFSA’s use of MongoDB Charts Can’t get you (AI) out of my head Now, the NFSA—inspired by Qamar’s own training in machine learning and the broad interest in all things AI—is exploring how it can use Atlas Vector Search and generative AI tools to allow users to explore content buried in the NFSA collection. One example cited is putting transcriptions of audiovisual files in NFSA’s collection into a vector database for retrieval-augmented generation (RAG). The NFSA has approximately 27 years worth—meaning, it would take 27 years to play it all back—of material to transcribe, and is currently developing a model to accurately capture the Australian dialect so the work is transcribed correctly. Ultimately, the NFSA is interested in building a RAG-powered AI bot to provide historically and contextually accurate information about work in the NFSA’s archive. The NFSA is also exploring how it can use RAG to deliver accurate, conversation-like search results without training large language models itself, and whether it can leverage AI to help restore some of the older videos in its collection. Qamar and team are also interested in vectorizing audio-visual material for semantic analysis and genre-based classification of collection material at scale, he said. “Historically, we’ve been very metadata-driven and keyword-driven, and I think that’s a missed opportunity. Because when we talk about what an archive does, we archive stories,” Qamar said of the possibilities offered by vectors. “An example I use is, what if the world ended tomorrow? And what if aliens came to Earth and only saw our metadata, what image of Australia would they see? Is that a true image of what Australia is really like?” Qamar said. “How content is described is important, but content’s imagery, the people in it, and the audio and words being spoken are really important. Full-text search can take you somewhere along the way, but vector search allows you to look things up in a semantic manner. So it’s more about ideas and concepts than very specific keywords,” he said. If you’re interested in learning how MongoDB helps accelerate and simplify time-to-mission for federal, state, and local governments, defense agencies, education, and across the public sector, check out MongoDB for Public Sector . Check out MongoDB Atlas Vector Search to learn more about how Vector Search helps organizations like the NFSA build applications powered by semantic search and gen AI. *Note that this story’s subheads come from Australian song titles!

May 14, 2024
Artificial Intelligence

Announcing DirectQuery Support for the MongoDB Atlas Connector for Power BI

Last year, we introduced the MongoDB Atlas Power BI Connector , a certified solution that has transformed how businesses gain real-time insights from their MongoDB Atlas data using their familiar Microsoft Power BI interface. Today, we’re excited to announce a significant enhancement to this integration: the introduction of DirectQuery support. DirectQuery mode provides a direct connection to your MongoDB Atlas database, allowing Power BI to query data in real-time. This means that your Power BI visualizations and reports will always reflect the latest data without importing and storing data within Power BI. This is especially beneficial for analyzing large datasets where up-to-date information is crucial, ensuring decisions are made efficiently without losing performance due to repetitive data imports and storage complexities. How DirectQuery in MongoDB Atlas Power BI Connector works: The Power BI Connector is supported through MongoDB’s Atlas SQL Interface , which is easily enabled from the Atlas console. Atlas SQL, powered by Atlas Data Federation , allows you to integrate data across sources and apply transformations directly, enhancing your analytics. Once enabled, you’ll receive a SQL Endpoint or URL to input into your MongoDB Atlas SQL Connection Dialog within Power BI Desktop. Here, you can choose between two connectivity modes: Import or DirectQuery. Once connected through DirectQuery, Query folding takes place with Power Query , which is how data retrieval and transformation of source data is optimized. You can also achieve data transformation using a SQL Statement, either with the SQL Statement option in the Atlas SQL Interface or within the M Code script accessed via the Power Query Advanced Editor. After your data is transformed and ready for analysis, start building reports with your Atlas data within the Power BI Desktop! Then, simply save, publish, and distribute within the Power BI online app, which is now part of the Microsoft Fabric platform. Watch our comprehensive tutorial below covering how to connect your Atlas data to Power BI , control SQL schemas in Atlas, and use DirectQuery to gain real-time access to your data for business insights. Power BI Connector for MongoDB Atlas is a Microsoft-certified solution. It not only supports the advanced capabilities of DirectQuery but also continues to offer Import Mode for scenarios where data volume is manageable and detailed data modeling is preferred. Whether you’re analyzing real-time data streams or creating comprehensive reports, the Atlas Power BI Connector adapts to your needs, ensuring your business leverages the full power of MongoDB Atlas. DirectQuery Support is available now and can be accessed by updating your existing MongoDB Atlas Power BI Connector or downloading it here . Start transforming your data analysis and making more informed decisions with real-time Atlas data. Log in and activate the Atlas SQL Interface to try out the Atlas Power BI Connector ! If you are new to Atlas or Power BI, get started for free today on Azure Marketplace or Power BI Desktop .

May 13, 2024
Updates

The Developers' Developers: Two Australian Developers Share Their Connections to Customers

The world’s 28 million software developers are writing the foundations of our future, propelling innovation for their organizations through lines of code by creating game-changing new apps. Indeed, the US Bureau of Labor Statistics predicts that between 2022 and 2032, the number of software developers, quality assurance analysts, and testers will grow 24%, “much faster than the average for all occupations.” Fueling this innovative workforce is another group of developers, the people working behind the scenes to build the tools, technologies and platforms that other developers need to be successful: the developers’ developers. Many developers at MongoDB—which after all was built by developers for developers, and is beloved by enterprises and startups alike—fall into this camp. To learn more about what makes these developers tick, we talked with two Australia-based senior software engineers at MongoDB who love to code for their peers. For Lavender Chan and Angus Lee, there’s nothing like seeing the ripple effect of the code they have been working on and the impact it has on their customers. What’s more, the opportunity to be a “developer’s developer” has allowed Chan and Lee to find a space for deep technical work while thriving in an autonomous environment. At MongoDB, we believe developers will build the future. First, can you share more about your roles and what you’re working on? Lavender Chan (LC): I work on the Relational Migrator tool, which allows developers to migrate SQL data onto MongoDB. I joined the company two and a half years ago, and have been part of the Sydney technology scene for the last 10 years. The appeal of joining MongoDB was that it’s a large global company, but in the engineering team, you are able to have a big impact and a lot of autonomy. Relational Migrator was a greenfield project, and our team has been able to take the original product idea built out of the US and run with it. I’m a full stack developer and have touched on every feature of the tool. A lot of the engineers were able to contribute and work on new ideas. There’s also a strong emphasis on culture here which practically means a lot of the people I work with are excited to be here and passionate about their roles. Angus Lee (AL): I work in the MongoDB Charts team in Australia and think our team is a sweet spot for developers. I’ve interned for other tech companies and started my career at MongoDB. Since then I’ve been given responsibilities where I can create a lot of impact. My role at MongoDB in Sydney has also given me great opportunities to connect directly to the developers we are creating products for in a way that pushes my work to a higher level. Your roles are focused on products targeted to other developers. How does developing for developers affect your approach to your work? LC: In our roles we are creating directly for other developers, so the work that I am doing is deeply technical and specific. As Migrator is a newer product, we are able to interact directly with our customers—other developers—and often a lot of their questions are quite complex and specific, which means I go on a learning journey in debunking and fixing their problems. AL: We have a strong team culture in that as developers we want to be our own users. That means we want to use other MongoDB team products, and they use ours, so we can better identify pain points and issues for our customers. There’s a term that developers use called “dogfooding” that really sums up how we think on this, which basically means to use your own product. It means for me that I think about writing clean code to help any other developers extend on this, and how effective what I do will be for the user. What I’ve also learnt is how our product helps other products thrive. We should have done all the hard work to transform data and show it through data visualizsation tools so it’s easy for the customer. Can you tell us more about this connection to customers and how MongoDB empowers developers? LC: When Relational Migrator was released as a general product, I went to MongoDB World to work at the booth, and I talked to the developers and customers using the platform. As an engineer, it was an amazing experience and opportunity to see how it was being used and what else we could be doing. This connection of engineers with customers, as well as the ability to speak to them regularly in my role, is unique. In other companies I would need to go through support teams, to go through someone else just to push out a bug fix. Our team is very customer focused, so we can prioritizse features that our customers want. AL: One of the best moments for me at MongoDB was when I went to MongoDB World and I sat down with a customer to talk through a feature of Charts. It was a pivotal moment to see the improvements it makes for the businesses that use it, and the impact it generates for their customers beyond that. I could sit back and see the ripple effect of the code I’m writing. There is also a great feedback engine where our users can submit ideas and other users can vote for that feature. The product managers pick from these and push out features that are directly relevant to the developers using it. We really connect through our aim to create an open forum for developers and customers to provide feedback and suggest ideas. Developers are problem solvers. As part of the MongoDB Love Your Developers campaign, we believe in championing the voice of developers and giving them the freedom to experiment and innovate. How do you see this in action? LC: In other places, I was a small cog in a massive system. At MongoDB, I really have an impact and can see directly how my work translates to our final product. In Sydney, we’re a satellite office, but it’s indicative of our company culture that there is huge trust placed in these teams. We’re given high impact projects and can run with them, which means I’ve been able to watch the Relational Migrator product go from a tiny product used by only a few customers, to one that is now generally available. Not many engineers get to work for a well-established, large company and still have the opportunity to work on and release products like a startup. There is a strong global interest in AI-driven innovations. How have your connections to customers led to innovations in this area? AL: We’ve been able to take an idea for a new AI feature, Natural Language Charts , and take it from concept to being released as a feature at MongoDB.local in London. We could see from our conversations with customers, as well as broader industry trends, that there was strong interest in new AI features, so we were able to prioritize it for Charts. We started with nothing and were given the freedom to research how this feature could work using AI, create a new proof of concept, and from there we were able to push it out into a feature which was a really proud moment. Having this agility and flexibility to prioritize something new is exactly what we want to provide to our customers. I never feel like I'm just churning out code. We are connected to the work and to our customers. MongoDB is built by developers, for developers. Become part of the team changing the way the world works with data!

May 9, 2024
Culture

Empowering Aspiring Developers in Africa: The MongoDB, MyTechDev Partnership

It's been nearly a year since the announcement of the partnership between MongoDB University and MyTechDev (Dev-Net), marking a significant milestone in empowering African developers with practical coding skills and enterprise technology pathways. This partnership aims to certify 500 people in Nigeria, South Africa, Kenya, and Egypt, aligning with the rising demand for skilled software developers globally. “At MongoDB, we love developers and are pleased to provide free, on-demand educational content for new learners and professional developers who want to expand their existing skill sets on the learning platform of their choice,” said Raghu Viswanathan, Vice President, Education, Documentation, and Academia at MongoDB. One of the remarkable outcomes of this collaboration is showcased in a recent video from MyTechDev (Dev-Net), where students share their experiences with MongoDB. Through hands-on learning facilitated by MyTechDev (Dev-Net) and free on-demand courses on MongoDB University , these students not only gained technical expertise but also found a supportive community that encouraged them through challenges, paving the way for future career aspirations and entrepreneurial ventures. The MyTechDev (Dev-Net) students' testimonials reflect the impact of accessible education and industry partnerships in fostering innovation and career opportunities in technology. By equipping individuals with in-demand skills like MongoDB proficiency, this collaboration addresses the skills gap. It fuels the ambitions of aspiring developers across Africa, promising a brighter future for the tech ecosystem on the continent. Investing in education isn't just about boosting opportunities; it's about saving lives. A recent study in The Lancet Public Health says what many have long suspected: education significantly impacts health outcomes. Regardless of age, gender, or socio-economic status, every year of schooling reduces mortality by 2%. This finding is particularly pertinent in sub-Saharan Africa, home to the world's youngest population, 70% under 30, and a GDP lagging far behind. Many of these young people face dire circumstances, with poverty and lack of resources hindering their access to education. The MongoDB for Academia program provides free resources for students and educators to make the most of MongoDB. The program also offers students MongoDB Atlas credits and free certification through the GitHub Student Developer Pack. These benefits are available globally, allowing students to enter the workforce with industry-relevant skills and certifications. To learn more, students and educators can register for the MongoDB for Students or MongoDB for Educators programs.

May 8, 2024
News

New Atlas Administrator Learning Path and Certification

Say hello to MongoDB’s newest education addition—the MongoDB Atlas Administrator Path , your guide to successfully prepare for and pass the new Associate Atlas Administrator certification ! This is the second certification and learning path we launched in 2024, a testament to our unwavering dedication to helping developers validate their skills. Certifications offer concrete evidence of expertise, bolstering credibility and marketability to employers. Thousands of developers have reaped the benefits of being MongoDB certified! Unlock your potential: Follow the learning path to success The new MongoDB Atlas Administrator Path will guide you through the foundations of MongoDB Atlas, the multi-cloud developer data platform. You'll learn to quickly get up and running with a free MongoDB Atlas Cluster. Additionally, this path will cover the basic steps for creating, securing, monitoring, and administering a new cluster using both the UI and CLI. Upon completing the learning path, not only will you feel prepared to take the certification exam, but you'll also automatically unlock a 50% discount on the exam. What’s more, the new Associate Atlas Administrator certification is designed to validate a candidate's mastery and competence, demonstrating their proficiency as a MongoDB Atlas Administrator. Once certified, you're equipped to effectively administer MongoDB Atlas, implement security measures, optimize performance, and manage version upgrades with confidence. MongoDB’s certifications hold the official seal of recognition from the worldwide tech community, affirming and validating your MongoDB expertise. These certifications aren't just pieces of paper—they're powerful catalysts for propelling your career to new heights and boosting your appeal to future employers. According to the Microsoft Certification Program Satisfaction Study, 91% of hiring managers report certification as an important criterion for hiring. As a certified pro, you'll have the opportunity to showcase your accomplishments with pride, gaining visibility in the Credly Talent Directory and earning a distinctive Credly badge that sets you apart from the crowd. But you don’t just take our word for it. Samuel Molling, MongoDB certified as an Associate DBA, Associate Developer, and Associate Data Modeler, has benefited firsthand from MongoDB certifications: “As soon as I got my first DBA certification, I received an exciting offer from a company to be a MongoDB specialist with a very attractive salary,” he said. “The company found me through MongoDB's certificates page (Credly Talent Directory). Due to the certifications, I acquired a lot of experience, grew my network, and received offers from both national and international companies. I managed to close contracts with clients for my company, and this gives me more and more visibility.“ Having a technical certification is invaluable in today's competitive job market, showcasing expertise and dedication to professional growth. Our decision to create a tailored learning path for our customers came from the belief in equipping developers with the tools necessary to succeed. As evidenced by Samuel's testimonial, obtaining a certification can be a game-changer, opening up new opportunities and advancing one's professional trajectory. Do you ever feel stuck and unsure of where to start? Look no further! Dive into MongoDB's free online educational resources for hands-on learning, quizzes, and labs. Elevate your skills with our new learning path and certification tailored to boost your Atlas Admin expertise. Get started now!

May 6, 2024
News

MongoDB Provider for Entity Framework Core Now Generally Available

We are pleased to announce that the MongoDB Provider for Entity Framework Core (EF Core) is now generally available. This allows developers using EF Core to build C# and .NET applications with MongoDB and to take advantage of our powerful developer data platform while continuing to use APIs and design patterns they already know and love. This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . Building for the C# and .NET communities Nearly one-third of all developers use C# to build applications, with the population of C# developers reaching upwards of 10 million developers worldwide . What’s more, 39 percent of C# developers use EF Core , which is beloved as an abstraction layer to simplify working with data during development. In the past, C# developers could use MongoDB’s C# driver but didn’t have first-party support for EF Core, so some turned to community-built projects that could be helpful—but lacked official backing or ongoing support from MongoDB. With the official MongoDB Provider for EF Core now generally available, developers can confidently use C# and EF Core when building with MongoDB for production-grade workloads. Gaurav Seth, Partner Director, Product Management at Microsoft, shared his excitement about the new integration, highlighting its importance for the .NET developer community: We are pleased to deepen the relationship between .NET developers and MongoDB through the new MongoDB Provider for Entity Framework Core,” said Gaurav Seth. “This advancement bridges the gap between MongoDB and Entity Framework Core, enabling .NET developers to leverage the full spectrum of MongoDB’s capabilities within the familiar EF environment. With this integration, .NET developers can now more easily incorporate MongoDB’s powerful features into their EF-based applications, further enhancing the robustness and scalability of their solutions. Gaurav Seth, Partner Director, Product Management at Microsoft What's in the new Provider for EF Core With the general availability release, the MongoDB Provider for EF Core offers developers the following capabilities, building upon the foundational features released in the public preview: Compatibility with Entity Framework Core 8 & .NET 8: Fully compatible with the latest EF Core and .NET versions, ensuring your projects are up-to-date with the newest features and improvements. Advanced Querying and Data Operations: Provides a comprehensive suite of querying options, including complex operations and aggregates like Where, OrderBy, and ThenBy, enabling precise data retrieval and deeper analytical insights within your applications. Mapping and Configuration Flexibility: Extended mapping capabilities for properties and entities, including support for various data types and composite keys, providing greater flexibility and precision in how data is structured and stored. Array and List Handling: Improved handling of arrays and lists, enabling more complex data structures to be easily managed and manipulated within your applications. Logging: Enhanced logging for better visibility of operations. We will continue to offer support for the following capabilities launched in the Public Preview: Support for code-first workflows : Allows users to build without an initial database; you create the classes for your application and then match your data model to the classes, not the other way around. Basic CRUD methods: Basic create, read, update, and delete (CRUD) operations are supported. String and numeric type operators: String and numeric type operators needed for basic CRUD operations will be supported. We anticipate supporting more complex operators in future iterations of the Provider. Embedded documents: The Provider supports embedded documents, making it easier to store related information in the same database record. Class mapping and serialization: Your classes in C# will map to MongoDB in a predictable way, including when working with IDs as well as date and/or time values. LINQ query support: The Provider will support LINQ queries with fluent query syntax. Change tracking: The Provider allows you to track and save changes made to entities with each DbContext instance back to your MongoDB database. Benefits of using the Provider for EF Core With the MongoDB Provider for EF Core, C# developers can unlock the full power of MongoDB's developer data platform to build modern applications while leveraging a familiar API interface, query paradigm (LINQ), and design patterns. Developers looking to modernize their data layer can do so with MongoDB while remaining free from cloud vendor lock-in since MongoDB works with all major cloud providers and for multi-cloud deployments. How to get started with MongoDB Provider for Entity Framework Core All you need to do is download the MongoDB Provider for EF Core from the NuGet package manager and build a DbContext that points to a MongoDB Provider instance. The Provider connects to MongoDB and handles the rest, so you can quickly harness the joint value of EF Core and MongoDB. Learn more by diving into our documentation . After you try the new Provider for EF Core, leave us feedback . Your input is important for helping us continue to improve the product experience. Get started today to unleash the power of your data with MongoDB and EF Core.

May 3, 2024
Updates

Building the Next Big Thing Together: Announcing MongoDB’s Partners of the Year

Customers demand leading organizations to innovate, scale, and build modern applications at an unprecedented pace. At MongoDB, tens of thousands of companies in over 100 countries trust us with these critical workloads—the workloads that power their modern and gen AI applications. But MongoDB is hardly working alone. The broad MongoDB Partner Ecosystem is critical to serving customer needs across a number of industries, areas of excellence, and geographies. “Our partners are critical to MongoDB’s success. They help ensure that customers can access our technology easily and that they have the best possible experience when using MongoDB. “This is increasingly important as organizations look to innovate with generative AI, and our partners will play a big role in making this a reality,” said Alan Chhabra, Executive Vice President of Partners at MongoDB. “We work closely together all year, so it’s great to bring our partners together to celebrate everyone’s hard work and success. MongoDB’s partner awards shine a light on growth, determination, and an unwavering dedication to customer needs.” At .local NYC 2024, we’ve been thrilled to highlight MongoDB’s incredible partners in sessions, hands-on labs, and announcements, including as part of the new MongoDB AI Application Program, which brings together industry-leading consultancies and foundation model providers, cloud infrastructure providers, and generative AI framework providers to help organizations rapidly build and deploy modern applications enriched with generative AI. We’re also very happy to announce MongoDB’s 2024 partners of the year. The 2024 list spans multiple partner types, industries, and categories and includes both established companies as well as emerging players. See below for more! Cloud - AI Partner of the Year: AWS Over the past year, AWS and MongoDB joined forces to make it even easier for customers to augment their gen AI-powered applications with enterprise data, most recently with the integration of MongoDB Atlas as a vector database in Amazon Bedrock’s fully managed Knowledge Base retrieval-augmented generation (RAG) workflow. In addition, MongoDB was named a launch partner for the AWS Generative AI Competency. With additional joint gen AI related integrations in the pipeline, there’s so much more to come! Cloud - Certified DBaaS Partner of the Year: Alibaba Alibaba Cloud consistently provides top-tier cloud services, integrating the latest MongoDB features and enhancements to deliver robust, scalable, and secure database solutions to a wide range of industries in China. Alibaba Cloud has the most up-to-date MongoDB services in China, via their ApsaraDB for MongoDB offering, and, along with their dedication to localizing products and services to meet the unique needs of the Chinese market, has solidified Alibaba Cloud’s position as a leading DBaaS provider. Cloud - Modernization Partner of the Year: Google Cloud MongoDB has partnered with Google Cloud since 2018, with MongoDB Atlas now available in 32 Google Cloud regions around the world, helping thousands of companies—like Ulta , Keller Williams , and Rent the Runway —adopt cloud-native data strategies. MongoDB and Google Cloud are fundamentally committed to breaking down data silos and ensuring that customers can build using data from any source, in any location, and on any platform. We're proud of our achievements in empowering businesses with data capabilities for digital transformations, and we are committed to further collaboration to help businesses succeed in their journeys. Cloud - Marketplace Partner of the Year: Microsoft Azure Microsoft and MongoDB are committed to empowering developers and organizations to build innovative, scalable, and intelligent applications on the Azure Marketplace. Through this partnership, Microsoft has enabled thousands of customers to leverage the power of MongoDB Atlas on Azure, the Data Developer Platform, accelerating their digital transformation journeys. Services - AI Industry Solutions: Capgemini Capgemini has developed innovative, MongoDB-based, Gen AI solutions over the last year, addressing multiple industries including insurance, banking, healthcare, retail, energy, and the public sector. Capgemini is one of MongoDB’s most innovative partners, and we congratulate and thank them for translating value propositions into business value! Services - Modernization Partner of the Year: Accenture This year, Accenture collaborated with MongoDB—both internally within their Centers of Excellence (COEs) and externally—to modernize client applications and data platforms. Our collaboration enhanced agility, reduced costs, and facilitated smooth integrations into multi-cloud environments. By leveraging MongoDB's modern data platform and gen AI technologies, Accenture used their modernization capabilities to deliver more efficient client outcomes. Services - Public Sector Partner of the Year: Clarity Solutions Clarity is our trusted partner for delivering quality professional services for our government customers. Clarity understands the air-gapped environments and have continuously helped MongoDB expand our footprint within various agencies in the public sector. We expect to continue this momentum! Services - Emerging Markets Partner of the Year: CloudMile In 2023, CloudMile launched a strategic partnership with MongoDB, leveraging Atlas, our cloud-native developer data platform. This partnership has resulted in both the delivery of modern application and modernization projects to customers across South East Asia. CloudMile enables their teams to convey and deliver the benefits of MongoDB Atlas, and we’re excited to continue growing our business across the region for years to come. Services - Jumpstart Partner of the Year: gravity9 gravity9 partners with MongoDB Professional Services to consistently deliver high quality Jumpstart engagements for our customers. Their work has accelerated consumption on MongoDB's data platform by consistently securing repeat business from some of our most strategic customers. Thank you gravity9! Services - Transformation Partner of the Year: McKinsey McKinsey’s transformational work with MongoDB on their Iguazio MLOps Platform makes them a fitting winner for this award. MongoDB & Iguazio provide unprecedented ease in delivery of data and data science on multi-cloud platforms. Utilizing the combination of MongoDB Cloud and Iguazio’s Data Science Platform allows for rapid implementation of data science projects. We’re thrilled to continue building with McKinsey for years to come. Services - AI Partner of the Year: Pureinsights Pureinsights, MongoDB's premier services partner for search and AI, has built one of the best RAG architectures for a large European car manufacturer using MongoDB's tech stack and by leveraging our comprehensive ecosystem for AI. Once deployed in production, the GAI solution will help this manufacturer save millions of dollars in their after sales division. Startup Program - AI Partner of the Year: Arcee Arcee, one of MongoDB’s most successful AI startup partners, specializes in training Smaller, Specialized Models (SLMs) that are tailored to customer’s specific data. They then employ metric-guided model merging to seamlessly integrate these custom-trained models with other large language models. This process not only enhances efficiency but also ensures limitless scalability across a broad spectrum of business applications, delivering exceptional performance tailored to meet diverse enterprise needs. ISV - Industry Solutions Partner of the Year: commercetools Commercetools’s world-leading composable commerce platform—fully powered by MongoDB Atlas—is driving the future of digital commerce. Through this partnership, commercetools can scale data to match the speed of your commerce experiences, processing real-time operational and analytical workloads for omnichannel and personalized commerce. Commercetools wins this award as they expand MongoDB’s reach into top retail brands, redefine digital commerce, and collaborate with us to deliver innovative solutions to the market. Technology - AI App Framework Partner of the Year: LangChain This year, LangChain and MongoDB partnered to simplify the development and deployment of gen AI applications through innovations like vector search, semantic caching, conversation history integrations, dedicated packages, and templates for MongoDB Atlas. The outstanding collaboration between LangChain and MongoDB continues to benefit our joint customers and all developers. Technology - AI Hosting Partner of the Year: Fireworks Fireworks AI and MongoDB Atlas came together this year to provide a solution for developers who want to leverage highly curated and optimized open-source models, and to combine these with their organization’s own proprietary data—and to do it all with unparalleled speed and security. Our continued partnership, including our joint go-to-market efforts, represents a milestone in our shared mission of helping developers to unlock the full potential of AI with confidence and efficiency. Congratulations Fireworks! Technology - Build with Partner of the Year: Informatica Informatica’s world-class MDM and 360 Applications leverages MongoDB to provide developers and organizations the opportunity to create data-first business applications, where data will drive the function and action of users and provide more opportunities for automation. Together, Informatica and MongoDB enable customers to efficiently create modern, cloud-native, data-driven, industry-tailored applications powered by MongoDB Atlas and with a secure foundation of trusted data from Informatica’s AI-powered MDM application. Thank you to all of our incredible Partners of the Year recipients! We are grateful to the entirety of our MongoDB partner ecosystem for their dedication in helping deliver incredible experiences for our customers. We will continue working with our partners to build and scale modern, genAI applications on MongoDB. To learn more about the MongoDB Partner Program, please visit our partners page .

May 2, 2024
News

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: This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . 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. 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.

May 2, 2024
Updates

Top AI Announcements at MongoDB.local NYC

The AI landscape is evolving so quickly that it’s no surprise customers are overwhelmed by their choices. Between foundation models for everything from text to code, AI frameworks, and the steady stream of AI-related companies being founded daily, developers and organizations face a dizzying array of AI choices. MongoDB empowers customers through a developer data platform that helps them avoid vendor lock-in from cloud providers or AI vendors in this fast-moving space. This freedom allows customers to choose the large language model (LLM) that best suits their needs - now or in the future, whether it's open source or proprietary. Today at MongoDB.local NYC, we announced many new product capabilities, partner integrations, services, and solution offering that enable development teams to get started and build customer-facing solutions with AI. This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . Run everywhere, with whatever technology you are using in your AI stack MongoDB’s flexible document model is built on the ethos of “data that is accessed and used together is stored together.” Vectors are a natural extension of this capability, meaning customers can store their source data, metadata, and related vector embeddings in the same document. All of this is accessed and queried with a common Query API, making vector data easy to combine and work with other types of data stored within MongoDB. MongoDB Atlas—our fully managed, multi-cloud developer data platform—makes it easy to build AI-powered applications and experiences, with the breadth and depth of MongoDB’s AI partnerships and integrations—no matter which language, application framework, foundation model, or technology partner is used or preferred by developers. This year, we’re continuing to focus on our AI partnerships and integrations to make it easier for developers to build innovative applications with generative AI, including: Python and JavaScript using the dedicated Langchain-MongoDB package Python and C# Microsoft Semantic Kernel integration for Atlas Vector Search AI models from Mistral and Cohere AI models on the Fireworks AI platform Addition of Atlas Vector Search as a knowledge base in Amazon Bedrock Atlas as a datastore enabling storage, query, and retrieval using natural language in ChatGPT Atlas Vector Search as a datastore on Haystack Atlas Vector Search as a datastore on DocArray Collaboration with Google Gemini Code Assist and Amazon Q to quickly prototype new features and accelerate application development. Google Vertex AI Extension to harness natural language with MongoDB queries MongoDB integrates well with a rich ecosystem of AI developer frameworks, LLMs, and embedding providers. We continue investing in making the entire AI stack work seamlessly, enabling developers to take advantage of generative AI capabilities in their applications easily. MongoDB’s integrations and our industry-leading multi-cloud capabilities allow organizations to move quickly and avoid lock-in to any particular cloud provider or AI technology in a rapidly evolving space. Build high-performance AI applications securely and at scale Workload isolation, without data isolation, is critical for building performant, scalable AI applications. Search Nodes in MongoDB Atlas provide dedicated computing and enable users to isolate memory-intensive AI workloads for superior performance and higher availability. Users can optimize resource consumption for their use case, upsizing or downsizing the hardware for that specific node irrespective of the rest of the database cluster. Search Nodes make optimizing performance for vector search queries easy without over or under-provisioning an entire cluster. The IaC integrations with Hashicorp Terraform Atlas Provider and Cloudformation enable developers to configure and programmatically deploy Search Nodes at scale. Search Nodes are an integral part of Atlas - our fully managed, battle-tested, multi-cloud platform. Previously, we announced the availability of Search Nodes for our AWS and Google Cloud customers. We are excited to announce the preview of Search Nodes for our Azure customers at MongoDB.local NYC. Search Nodes on Atlas helps developers move faster by removing the friction of integrating, securing, and maintaining the essential data components required to build and deploy modern AI applications. Improve developer productivity with AI-powered experiences Today, we also announced new and improved releases of our intelligent developer experiences in MongoDB Compass , MongoDB Relational Migrator , and MongoDB Atlas Charts , aiming to enhance developer productivity and velocity. With the updated releases, developers can use natural language to query their data using MongoDB Compass, troubleshoot common problems during development, perform SQL-to-Query API conversion right from within MongoDB Relational Migrator , and quickly build charts and dashboards using natural language prompts in MongoDB Atlas Charts. Collectively, these intelligent experiences will help developers build differentiated features with greater control and flexibility, making it easier than ever to build applications with MongoDB. Enable development teams to get started and build customer-facing solutions faster and easier with AI MongoDB makes it easy for companies of all sizes to build AI-powered applications. To provide customers with a straightforward way to get started with generative AI, MongoDB is announcing the MongoDB AI Application Program (MAAP). Based on usage patterns for common AI use cases, customers receive a functioning application built on a reference architecture backed by MongoDB Atlas, vetted AI models and hosting solutions, technical support, and a full-service engagement led by our Professional Services team. We’re launching with an incredible group of industry-leading partners, including Anthropic, Anyscale, AWS, Cohere, Credal.ai, Fireworks.ai, Google Cloud, gravity9, LangChain, LlamaIndex, Microsoft Azure, Nomic, PeerIslands, Pureinsights, and Together AI. MongoDB is in a unique position in the market to be able to pull together such an impressive AI partner ecosystem in a single customer-focused program, and we’re excited to see how MAAP will help customers more easily go from ideation to fully functioning generative AI applications. Last year, to further enable startups to build AI solutions with MongoDB Atlas, we launched the AI Innovators Program , an extension of MongoDB for Startups , which offers an additional $5000 in Atlas credits to our AI startups. This year, we are expanding the program by introducing an AI Startup Hub , which features a curated guide for getting started with MongoDB and AI, quickstarts for MongoDB and select AI partners, and startup credit offerings from our AI partners. We provide two new AI Accelerator consulting packages for larger enterprise companies: AI Essentials and AI Implementation. While MAAP is aimed exclusively at building highly vetted reference architectures, these consulting packages allow customers to design, build, and deploy open-ended AI prototypes and solutions into their applications. Data has always been a competitive advantage for organizations, and MongoDB makes it easy, fast, and flexible to innovate with data. We continue to invest in making all the other parts of the AI stack easy for organizations: vetting top partners to ensure compatibility with different parts of the application stack, building a managed service that spans multiple clouds in operation, and ensuring the openness that's always been a part of MongoDB which avoids vendor lock-in. How does MongoDB Atlas unify operational, analytical, and generative AI data services to streamline building AI-enriched applications? Check out our MongoDB for AI page to learn more.

May 2, 2024
Updates

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