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.

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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
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MongoDB and Partners: Building the AI Future, Together

If you’re like me, over the past year you’ve closely watched AI’s developments—and the world’s reactions to them. From infectious excitement about AI’s capabilities, to impatience with its cost and return on investment, every day has been filled with AI twists and turns. It’s been quite the roller coaster. During the ride, from time to time I’ve wondered where AI falls on the Gartner hype cycle, which gives "a view of how a technology or application will evolve over time." Have we hit the "peak of inflated expectations" only to fall into the "trough of disillusionment?" Or is the hype cycle an imperfect guide, as The Economist argues? The reality is that it takes time for any new technology—even transformative ones like AI—to take hold. And every advance, no matter how big, has had its detractors. A famous example is that of Picasso (!), who in 1968 said, “Computers are useless. They can only give you answers.” (!!) For our part, MongoDB is convinced that AI is a once-in-a-generation technology that will enhance every future application—a belief that has been reinforced by the incredible work our partners have shared at MongoDB’s 2024 events. Speeding AI development MongoDB is committed to helping organizations of all sizes succeed with AI, and one way we’re doing that is by collaborating with the MongoDB partner ecosystem to create powerful, user-friendly AI development tools and solutions. For example, Fireworks.ai —which is a member of the MongoDB AI Applications Program ecosystem —created an inference solution that hosts gen AI models and supports containerized deployments. This tool makes it easier for developers to build and deploy powerful applications with a range of easy-to-use tools and customization options. They can choose to use state-of-the-art, open-source language, image, and multimodal foundation models off the shelf, or they can customize and fine-tune models to their needs. Jointly, Fireworks.ai and MongoDB provide a solution for developers who want to leverage highly curated and optimized open-source models and combine these with their organization’s own proprietary data—and to do so with unparalleled speed and security. “MongoDB is one of the most sophisticated database providers, and it’s very easy to use,” said Benny Chen , cofounder of Fireworks.ai. "We want developers to be able to use these tools, and we want to work with providers who enable and empower developers." Nomic , another MAAP ecosystem member, also enables developers with best-in-class solutions across the entire unstructured data workflow. Their Embed offering, available through the Nomic API , allows users to vectorize large-scale datasets for use in text, image, and multimodal retrieval applications, including retrieval-augmented generation (RAG), using only their web browser. The Nomic-MongoDB solution is a highly efficient, open-weight model that developers can use to visualize the unstructured datasets they store in MongoDB Atlas . These insights help users quickly discover trends and articulate data-driven value propositions. Nomic also supported the recently announced vector quantization in MongoDB Atlas Vector Search , which reduces vector sizes while preserving performance. Last—but hardly least!—there’s our new reference architecture with MAAP partners AWS and Anthropic. Announced at MongoDB.local London , the reference architecture supports building memory-enhanced AI agents, and is designed to streamline complex processes and develop smarter, more responsive applications. For more—including a link to the code on Github— check out the MongoDB Developer Center . Making AI work for anyone and everyone The companies MongoDB partners with aren’t just making gen AI easier for developers—they’re building tools for everyone. For example, Capgemini has invested $2 billion in gen AI and is training 100,000 of its employees in the technology. GenYoda, a solution that helps insurance professionals with their daily work, is a product of this investment. GenYoda leverages MongoDB Atlas Vector Search to analyze large amounts of customer data, like policy statements, premiums, claims history, and health information. Using GenYoda, insurance professionals can quickly analyze underwriters’ reports to make informed decisions, create longitudinal health summaries, and streamline customer interactions to improve contact center efficiency. GenYoda can ingest 100,000 documents in just a few hours and respond to users’ queries in two to three seconds—a metric on par with the most widely used gen AI models. And it produces results: in one example, by using Capgemini’s solution an insurer was able to increase productivity by 15%, add new reports 25% faster (thus speeding decision-making), and reduce the manual effort of searching PDFs, increasing efficiency by 10%. Building the future of AI together So, what’s next? Honestly, I’m as curious as you are. But I’m also incredibly excited. At MongoDB, we’re active participants in the AI revolution, working to embrace the possibilities that lie ahead. The future of gen AI is bright, and I can’t wait to see what we’ll build together. To learn more about how MongoDB can accelerate your AI journey, explore the MongoDB AI Applications Program .

November 4, 2024