Import and export your data with MongoDB Compass

How can you import data to MongoDB using Compass?

In Compass, it has always been quite easy to import data – from JSON and CSV files – into a collection. With this new release, we’ve made it even easier and more powerful.

When you import JSON files, in addition to JSON lines files with Extended JSON, we now support importing from files containing JSON arrays, which is closer to how developers think, is a standard format for REST API responses, and is consistent with the functionality of mongoimport.

When it comes to importing CSV files, we’ve given you more control over what is being imported. You are now able to configure the separator that is used in the CSV file you are trying to import and you can also choose what fields should be imported. Additionally, for each field you can specify a data type; when something other than “String” is selected, the values will be converted automatically during the import operation without requiring additional batch operations after the import is completed.

Import Data to Collection

How can you export data to MongoDB using Compass?

When your data is stored in MongoDB, you can query and aggregate it in many different ways to extract insights, and you can visualize your insights with MongoDB Charts. Sometimes, however, you may want to export your data (or a subset of it) to use it in other tools.

In Compass, exporting data is simple: just select a collection, optionally filter the data with a query and use the export functionality to save it as JSON or CSV.

Export Collection

Compass 1.21 contains also a number of under the hood improvements (connectivity, performance, security, etc.) and bug fixes that should improve your experience with Compass.

What else would you like to see in Compass to work more productively with MongoDB? Submit ideas and suggestions in our feedback portal!

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Welcome to the New MongoDB Cloud Navigation

Take a look at the redesigned MongoDB Cloud navigation for Atlas, Stitch, and Charts and how it helps users find what they need quickly and easily.

April 23, 2020
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Customer Service Expert Wati.io Scales Up on MongoDB

Wati.io is a software-as-a-service (SaaS) platform that empowers businesses to develop conversation-driven strategies to boost growth. Founded by CEO Ken Yeung in 2019, Wati started as a chatbot solution for large enterprises, such as banks and insurance companies. However, over time, Yeung and his team noticed a growing need among small and medium-sized businesses (SMBs) to manage customer conversations more effectively. To address this need, Wati used MongoDB Atlas and built a solution based on the WhatsApp Business API. It enables businesses to manage and personalize conversations with customers, automate responses, improve commerce functions, and enhance customer engagement. Speaking at MongoDB.local Hong Kong in September 2024, Yeung said, “The current solutions on the market today are not good enough. Especially for SMBs [that] don’t have the same level of resources as enterprises to deal with the number of conversations and messages that need to be handled every day.” Supporting scale: From MongoDB Community Edition to MongoDB Atlas “From the beginning, we relied on MongoDB to handle high volumes of messaging data and enable businesses to manage and scale their customer interactions efficiently,” said Yeung. Wati originally used MongoDB Community Edition , as the company saw the benefits of a NoSQL model from the beginning. As the company grew, it realized it needed a scalable infrastructure, so Wati transitioned to MongoDB Atlas. “When we started reaching the 2 billion record threshold, we started having some issues. Our system slowed down, and we were not able to scale it,” said Yeung. Atlas has now become an essential part of Wati’s infrastructure, helping the company store and process millions of messages each month for over 10,000 customers in 165 countries. “Transitioning to a new platform—MongoDB Atlas—seamlessly was critical because our messaging system needs to be on 24/7,” said Yeung. Wati collaborated closely with the MongoDB Professional Services and MongoDB Support teams, and in a few months it was able to rearchitect the deployment and data model for future growth and demand. The work included optimizing Wati’s database by breaking it down into clusters. Wati then focused on extracting connections, such as conversations, and dividing and categorizing data within the clusters—for example, qualifying data as cold or hot based on the read and write frequencies. This architecture underpins the platform’s core features, including automated customer engagement, lead qualification, and sales management. Deepening search capabilities with MongoDB Atlas Search For Wati’s customers, the ability to search through conversation histories and company documents to retrieve valuable information is a key function. This often requires searching through millions of records to rapidly find answers so that they can respond to customers in real-time. By using MongoDB Atlas Search , Wati improved its search capabilities, ultimately helping its business customers perform more advanced analytics and improve their customer service agents’ efficiency and customer reporting. “[MongoDB] Atlas Search is really helpful because we don’t have to do a lot of technical integration, and minimal programming is required,” said Yeung. Looking ahead: Using AI and integrating more channels Wati expects to continue collaborating with MongoDB to add more features to its platform and keep innovating at speed. The company is currently exploring to build more AI capabilities of Wati KnowBot , as well as how it can expand its integration with other conversation platforms and channels such as Instagram and Facebook. To learn more about MongoDB Atlas, visit our product page . To get started with MongoDB Atlas Search, visit the Atlas Search product page .

November 25, 2024