DataGrip: a great database IDE to work with MongoDB Atlas

Massimiliano Marcon and Maxim Sobolevskiy
June 16, 2020 | Updated: September 9, 2024
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>> 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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Hanabi Technologies Uses MongoDB to Power AI Assistant, Hana

For all the hype surrounding generative AI, cynics tend to view the few real-world implementations as little more than “fancy chatbots.” But for Abhinav Aggarwal, CEO of Hanabi Technologies, the idea of a generative AI-powered bot that is more than just an assistant was intriguing. “I’d been using ChatGPT since it launched,” said Aggarwal. “That got me thinking: How could we make a chatbot that was like a team member?” And with that concept, Hana was born. The problem with bots “Most generative AI chatbots do not act like people; they wait for a command and give a response,” said Aggarwal. “We wanted to create a human-like chatbot that would proactively help people based on what they wanted—automating reminders, for example, or fetching time zones from your calendar to correctly schedule meetings.” Hanabi’s flagship product, Hana, is an AI assistant designed to enhance team collaboration within Google Chat, working in concert with Google Workspace and its suite of products. “Our target customers are smaller companies of between 10 and 50 people. At this size you’re not going to build your own agent from scratch,” he said. Hana integrates with Google APIs to deliver a human-like assistant that chimes in with helpful interventions, such as automatically setting reminders and making sure meetings are booked in the right time zone for each participant. “Hana is designed to bring AI to smaller companies and help them collaborate in a space where they are already working—Google Workspace,” Aggarwal explained. The MongoDB Atlas solution For Hana to act like a member of the team, Hanabi needed to process massive amounts of data to support advanced features like retrieval-augmented generation (RAG) for better information retrieval across Google Docs and many other sources. And with a rapidly growing user base of over 600 organizations and 17,000+ installs, Hanabi also required a secure, scalable, and high-performing data storage solution. MongoDB Atlas provided a flexible document model, built-in vector database, and scalable cloud-based infrastructure, freeing Hanabi engineers to build new features for Hana rather than focusing on rote tasks like data extract, transform, and load processes or manual scaling and provisioning. Now, MongoDB Atlas handles a variety of responsibilities: Scalability and security: MongoDB Atlas’s auto-scaling and automatic backup features have enabled Hanabi to seamlessly grow its user base without the need for manual database management. RAG: MongoDB Atlas plays a critical role in Hana’s RAG functionality. The platform enables Hanabi to split Google Docs into small sections, create embeddings, and store these sections in Atlas’s vector database. Development Processes: According to Aggarwal, MongoDB’s flexibility in managing changing schemas has been essential to the company’s fast-paced development cycle. Data Visualization: Using MongoDB Atlas Charts has enabled Hanabi to create comprehensive dashboards for real-time data visualization. This has helped the team track usage, set reminders, and optimize performance without needing to build a manual dashboard. Impact and results With MongoDB Atlas, Hanabi can successfully scale Hana to meet the demands of its rapidly expanding user base. The integration is also enabling Hana to offer powerful features like automatic interactions with customers, advanced information retrieval from Google Docs, and manually added memory snippets, making it an essential tool for teams around the world. Next steps Hanabi plans to continue integrating more tools into Hana while expanding its reach to personal Gmail users. The company is also rolling out a new automatic-interaction feature, further enhancing Hana’s ability to proactively assist users without direct commands. MongoDB Atlas remains a key component of Hanabi’s stack, alongside Google Kubernetes Engine, NestJS, and LangChain, enabling Hanabi to focus on innovating to improve the customer experience. Tech Stack MongoDB Atlas Google Kubernetes Engine NestJS LangChain Are you building AI apps? Join the MongoDB AI Innovators Program today! Successful participants gain access to free MongoDB Atlas credits, technical enablement, and invaluable connections within the broader AI ecosystem. If your company is interested in being featured, we’d love to hear from you. Connect with us at ai_adopters@mongodb.com.

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