DataGrip: a great database IDE to work with MongoDB Atlas

Massimiliano Marcon and Maxim Sobolevskiy
June 16, 2020 | Updated: September 9, 2024
#partners

>> Announcement: Some features mentioned below will be deprecated on Sep. 30, 2025. Learn more.

JetBrains released a new version of DataGrip that includes support for MongoDB and ships with the MongoDB Shell out of the box.

A few months ago, JetBrains released the first version of DataGrip that supports MongoDB. DataGrip is a professional DataBase IDE and is a great tool for advanced data exploration and analytical queries for your data in MongoDB and Atlas.

If you use any of the other JetBrains commercial products for development (e.g. IntelliJ, PhpStorm, PyCharm), you can install the Database Tools plugin and work with MongoDB right inside your IDE.

As we announced last week at MongoDB.live, the MongoDB plugin in DataGrip and in the other JetBrains IDE now includes a database console built on top of the MongoDB Shell. This is the result of a close collaboration between engineering teams at MongoDB and JetBrains and we are excited to finally release it to all our users.

With this integration, when you connect to MongoDB and Atlas with DataGrip, you get a console/shell experience that is 100% consistent with what you get in the terminal or in the other MongoDB’s developer tools.

DataGrip and Atlas

When your data is stored in Atlas, navigating through it with DataGrip is extremely easy.

For example, to connect to my free Atlas cluster, I can just grab a connection string from the Atlas UI, use it to add a new data source in DataGrip and I am connected and ready to explore my data.

Using Consoles

If you need to run queries, aggregations, or other database commands, consoles are a great way to do that.

The engineering teams at JetBrains and at MongoDB worked together to bring the new, embeddable MongoDB Shell to DataGrip and use it as the engine behind database consoles. This means the same commands that you can run in the MongoDB Shell are available in DataGrip and the other JetBrains IDEs and you can expect them to work exactly in the same way with the addition of the beautiful UI you are already used to on top.

Embedded MongoDB Shell

A great fit for Atlas Data Lake

DataGrip works well MongoDB deployments, whether self-managed or in Atlas. You can also use it with Atlas Data Lake. If you have data is stored in Amazon S3 and you want to query and analyze it with MQL, you can create a Data Lake and start working with it right away.

DataGrip’s search capabilities give you a quick way to find the data you are interested in, and its powerful export tool makes it easy to extract it in the format you need to use it with other tools like Excel, for example.

Get started with MongoDB Atlas and DataGrip

We are really happy with the result of our collaboration with the team at JetBrains and we are looking forward to working more together to bring all the future capabilities of the MongoDB Shell to DataGrip and the other JetBrains IDEs.

You can experience for yourself how DataGrip is a perfect companion for MongoDB Atlas:

Get a free MongoDB Atlas cluster;
Deploy some sample datasets;
Download DataGrip and start working with your data immediately.

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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