MongoDB Partners With Codecademy on New “Learn MongoDB” Course

MongoDB
September 7, 2022 | Updated: March 14, 2025

MongoDB is pleased to announce the release of the new "Learn MongoDB'' course, created together with Codecademy. Hosted on the Codecademy platform, the course teaches students the basics of MongoDB and how to perform CRUD operations, query and analyze data, and create and use indexes.

With interactive tutorials and quizzes throughout, developers in the “Learn MongoDB” course can educate themselves on the breadth of MongoDB’s modern database and learn best practices for building applications on top of MongoDB. And by completing additional coursework on the programming language of their choice on Codecademy, early-career developers can learn how to code across the full application stack. This is a free, self-paced course that takes approximately eight hours to complete.

For developers building new applications, identifying the right solution for their data layer is critical. But equally important is learning how to use their solution of choice for maximum reliability and scalability. In addition to documentation and how-to guides, educational courses like “Learn MongoDB” are invaluable for helping developers harness new solutions to their full potential. MongoDB offers MongoDB University, which helps developers advance their careers with MongoDB courses and certifications; we also partner with leading third-party providers of developer educational experiences like Codecademy.

If you’re a developer new to working with MongoDB, Codecademy’s “Learn MongoDB” course is a great way to get started. Sign up today!

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Free your data with the MongoDB Relational Migrator

Nothing is more frustrating than data that is just out of reach. Imagine wanting to combine customer behavior data from your CRM and usage data from your legacy product to trigger tailored promotions in your new mobile app, but not being able to locate the required data in the sea of tables in your relational database. As MongoDB CTO Mark Porter explains in his MongoDB World keynote, the data that can make a difference might be locked up “somewhere that you can’t use.” Relying on his own hard-earned experience with data, Porter adds that this information can be trapped “in a schema with hundreds or thousands of tables that have built up over decades.” “Schema is a huge part of this problem,” MongoDB product manager Tom Hollander explains during a presentation on MongoDB Relational Migrator at MongoDB World 2022. “So we’ve spent a lot of time building out the tools to enable you to map your tabular relational schema into a document schema and make use of the full power of the MongoDB document model.” To see MongoDB Relational Migrator in action, check out this introduction and demo from MongoDB World 2022, featuring MongoDB product manager Tom Hollander. What is MongoDB Relational Migrator? MongoDB Relational Migrator streamlines migrations from legacy data infrastructure to MongoDB by helping developers analyze relational database schemas, convert them into MongoDB schemas, and then migrate data from the source database to MongoDB. Currently, Relational Migrator is compatible with four of the most common relational databases: Oracle, SQL Server, MySQL, and PostgreSQL. Migrator not only moves data from your relational database to MongoDB, but it also transforms it according to your new schema. As Hollander and MongoDB product marketing director Eric Holzhauer point out , developers often use a mix of software and tools (e.g., extract-transform-load pipelines, change data capture (CDC), message queues, and streaming) to execute migrations, which can be complicated, risky, and error-prone. Relational Migrator provides a single tool that can streamline the process while simultaneously ensuring that your data lands in an organized, logical manner. By simplifying schema translations — one of the most complex, difficult parts of any relational migration — Relational Migrator grants developers and other technical teams a greater degree of control over (and increased visibility into) their new MongoDB schema. The result is to make data more accessible for analysis and decision making. “Now I can get at the data in my program without going through a translation layer,” Porter explains. A visual representation of how Migrator maps relational schema to document schema. Migration mode: Snapshot or ongoing? Migrator provides two modes of data transfer: a one-time snapshot or a continuous sync (which will be available later this year). To help decide which mode you should use, consider whether you can move over to MongoDB and immediately decommission your previous database or whether you need to keep your existing relational database up and running. Organizations may wish to keep their relational database for various reasons, such as testing the effectiveness of your proposed document schema, running out the contract or licensing agreement to avoid expensive fees, or keeping old databases available for audits. In this situation, you can keep your relational database running so that Relational Migrator will continue to push data from your source to your new MongoDB clusters. The limits of Relational Migrator As Hollander points out, Relational Migrator is only a tool — one intended to facilitate schema mapping, providing many abilities and options for effective schema design. “It’s not a silver bullet that will immediately modernize your application portfolio,” Hollander says. “It’s not going to do everything for you. You still have to do the planning.” Furthermore, because database schema is a tricky topic even for seasoned experts, Hollander recommends that developers would benefit from working with architects, consultants, and partners — especially if they’re not familiar with MongoDB or schema design best practices. Relational Migrator does not yet support continuous replication, which would enable your relational database and MongoDB clusters to coexist for an extended period of time. However, Hollander says that work on this feature is ongoing and it will be available in the future, along with additional capabilities like schema recommendations, an integration for the MongoDB Atlas modern database, and more. MongoDB Relational Migrator is currently in private preview, for use on non-production workloads with assistance from our Product and Field Engineering teams. To learn more, get in touch with your MongoDB rep or contact us via our Migrator page to discuss your workload and next steps.

September 6, 2022
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The Future of AI Software Development is Agentic

Today in New York, our flagship MongoDB.local event is bringing together thousands of developers and tech leaders to discuss the future of building with MongoDB. Among the many exciting innovations and product announcements shared during the event, one theme has stood out: empowering developers to reliably build with AI and create AI solutions at scale on MongoDB. This post will explore how these advancements are set to accelerate developer productivity in the AI era. Ship faster with the MongoDB MCP Server Software development is rapidly evolving with AI tools powered by large language models (LLMs). From AI-driven editors like VS Code with GitHub Copilot and Windsurf, to terminal-based coding agents like Claude Code, these tools are transforming how developers work. While these tools bring tremendous productivity gains already, coding agents are still limited by the context they have. Since databases hold the core of most application-related data, access to configuration details, schemas, and sample data from databases is essential for generating accurate code and optimized queries. With Anthropic’s introduction of the Model Context Protocol (MCP) in November 2024, a new way emerged to connect AI agents with data sources and services. Database connection and interaction quickly became one of the most popular use cases for MCP in agentic coding. Today, we’re excited to announce the general availability (GA) of the MongoDB MCP Server, giving AI assistants and agents access to the context they need to explore, manage, and generate better code with MongoDB. Building on our public preview used by thousands of developers, the GA release introduces key capabilities to strengthen production readiness: Enterprise-grade authentication (OIDC, LDAP, Kerberos) and proxy connectivity. Self-hosted remote deployment support, enabling shared deployments across teams, streamlined setup, and centralized configuration. Note that we recommend following security best practices , such as implementing authentication for remote deployments. Accessible as a bundle with the MongoDB for VS Code extension , it delivers a complete experience: visually explore your database with the extension or interact with the same connection through your AI assistant, all without switching context. Figure 1. Overview of the MongoDB MCP Server. Meeting developers where they are with n8n and CrewAI integrations AI is transforming how developers build with MongoDB, not just in coding workflows, but also in creating AI applications and agents. From retrieval-augmented generation (RAG) to powering agent memory, these systems demand a database that can handle diverse data types—such as unstructured text (e.g., messages, code, documents), vectors, and graphs—all while supporting comprehensive retrieval mechanisms at scale like vector and hybrid search. MongoDB delivers this in a single, unified platform: the flexible document model supports the varied data agents need to store, while advanced, natively integrated search capabilities eliminate the need for separate vector databases. With Voyage AI by MongoDB providing state-of-the-art embedding models and rerankers, developers get a complete foundation for building intelligent agents without added infrastructure complexity. As part of our commitment to making MongoDB as easy to use as possible, we’re excited to announce new integrations with n8n and CrewAI . n8n has emerged as one of the most popular platforms for building AI solutions, thanks to its visual interface and out-of-the-box components that make it simple and accessible to create reliable AI workflows. This integration adds official support for MongoDB Atlas Vector Search , enabling developers to build RAG and agentic RAG systems through a flexible, visual interface. It also introduces an agent chat memory node for n8n agents, allowing conversations to persist by storing message history in MongoDB. Figure 2. Example workflow with n8n and MongoDB powering an AI agent. Meanwhile, CrewAI—a fast-growing open-source framework for building and orchestrating AI agents—makes multi-agent collaboration more accessible to developers. As AI agents take on increasingly complex and productive workflows such as online research, report writing, and enterprise document analysis, multiple specialized agents need to interact and delegate tasks with each other effectively. CrewAI provides an easy and approachable way to build such multi-agent systems. Our official integration adds support for MongoDB Atlas Vector Search , empowering developers to build agents that leverage RAG at scale. Learn how to implement agentic RAG with MongoDB Atlas and CrewAI. The future is agentic AI is fundamentally reshaping the entire software development lifecycle, including for developers building with MongoDB. New technology like the MongoDB MCP Server is paving the way for database-aware agentic coding, representing the future of software development. At the same time, we’re committed to meeting developers where they are: integrating our capabilities into their favorite frameworks and tools so they can benefit from MongoDB’s reliability and scalability to build AI apps and agents with ease. Start building your applications with the MongoDB MCP Server today by following the Get Started guide . Visit the AI Learning Hub to learn more about building AI applications with MongoDB.

September 17, 2025