Resources

MongoDB Observability: Diagnostics and Debugging

During the session, Product Marketing Manager Sabina Friden and Staff Product Manager Frank Sun will walk you through MongoDB Atlas’s robust diagnostic and debugging capabilities. We’ll highlight how enterprise teams can leverage these tools to stay ahead of performance issues and ensure seamless operations. You’ll also hear real-life use cases showing how customers in industries like e-commerce and financial services use MongoDB’s observability to meet their operational goals. Key takeaways: How MongoDB Atlas integrates with third-party observability tools Best practices for monitoring, diagnosing, and debugging database environments Real-world use cases demonstrating observability in action

Atlas 201 eWorkshop (English)

In this exclusive workshop you will get a full overview on how to migrate from a relational database to MongoDB followed by hands-on exercises and direct access to our expert panel. During this time, you will learn how to: Migrate from a relational databases to MongoDB How to build design patterns using MongoDB How to create aggregation pipelines How to use Atlas Vector Search and AI for context aware search

Atlas Search eWorkshop (English)

During this workshop, you will be able to learn more about features of the MongoDB's Developer Data Platform, including a follow-along, hands-on project to implement an AI application. We will cover various functionalities of MongoDB, as well as useful LLM techniques. In particular: Leverage Atlas to build a semantic search using LLMs for pre-summarization and information extraction Learn how to get started developing your first semantic search app Atlas with a free cluster Use automatic client side encryption to store user secrets securely (we're going to access users Google Drive Docs!) Learn about the pre-summarization technique, an alternative to chunking techniques Store LLM interactions and keep track of model usage and costs using Atlas Charts Use langchain to compare costs & performance of various models, featuring open-source models, OpenAI, Anthropic, Cohere & Mistral. Implement access control for vector search Build a basic RAG pipeline to search across stored facts

Building Agentic RAG Applications with MongoDB and LlamaIndex

Agentic RAG is the next evolution of retrieval-augmented generation (RAG) applications. They enable your AI-powered systems — fueled by personalized information based on user context and preferences — to provide more relevant, accurate answers for an enhanced user experience. Whether you're already building RAG applications or just getting started, this webinar will help you take the next step by adding agentic capabilities using MongoDB and LlamaIndex to your gen AI strategy. We’ll show you how to go from basic to agentic RAG applications, and as an added bonus, we’ll show you how to use hybrid search to optimize the application for relevance. Join us for this webinar, where Anaiya Raisinghani, Developer Advocate at MongoDB, and Laurie Voss, VP of Developer Relations at LlamaIndex, will explore the deep integration between MongoDB Atlas and LLamaIndex and provide valuable insights that will help you: Progress from basic to advanced retrieval. Optimize for relevance using hybrid search. Build Agentic RAG apps using MongoDB and LlamaIndex. Don't miss your chance to dive deep into the future of RAG applications and see how MongoDB and LlamaIndex can empower you to build next-gen solutions.

PostgreSQL 與 MongoDB：哪個更適合 GenAI？

Ask Me Anything: Mastering Data Modeling with MongoDB

During the webinar, our experts shared valuable insights on: Optimizing schema design for better application performance. Key differences between relational databases and MongoDB’s document model. Best practices for data modeling and real-world design patterns. A live data modeling design review and audience Q&A.

Comparing PostgreSQL vs. MongoDB: Which Is Better for AI Workloads?

Reaping the Benefits of Hyper-Personalization with AI and Application Modernization

The AI revolution in financial services is here. Both traditional AI and generative AI offer a wide range of use cases for financial institutions — from enhanced fraud prevention to streamlined operations. However, one of the most significant opportunities lies in the impact these technologies can have on customer experience. In a time when customer expectations are higher than ever, AI technologies enable banks to provide tailored, hyper-personalized experiences to their customers. This improves customer satisfaction, extends the customer lifecycle, and increases overall revenue. Yet, achieving the front-office benefits of hyper-personalization isn’t possible without addressing the back office. How can financial institutions reduce their reliance on legacy systems to fully leverage the potential of AI? Application modernization is critical for becoming agile enough to deliver tailored customer experiences and adapt to an increasingly dynamic market. So, how can banks modernize effectively without disrupting operations? Watch this on-demand webinar, hosted by Finextra in collaboration with MongoDB and Microsoft, and hear from a panel of industry experts. They’ll discuss how financial institutions can deliver hyper-personalized services by leveraging AI and implementing smart application modernization strategies.

Atlas 101 eWorkshop (English)

Building Advanced Gen AI Apps with gravity9 and MongoDB

An AI solution is only as good as its implementation. But many organizations — while seeing vast potential for gen AI in their business — are encumbered by legacy technology and may lack the expertise to plan and execute a successful implementation. That’s why we’re delighted to include gravity9 among our AI implementation partners. In this exclusive webinar we dive into the cutting-edge world of generative AI and discover how MongoDB Atlas Vector Search, in partnership with gravity9, can help you achieve the potential offered by gen AI while assuring a solid design and removing implementation risk. You’ll learn: The capabilities and limitations of gen AI and large language models. How retrieval-augmented generation (RAG) works and its role in gen AI apps. How to evaluate and optimize RAG solutions. The role of MongoDB Atlas Vector Search, its features and benefits. How other companies are applying gen AI You’ll also see a live demonstration of building advanced generative AI apps with gravity9 and MongoDB Atlas Vector Search. Speakers: Loghman Zadeh, gravity9 AI Director Richmond Alake, MongoDB Staff Developer Advocate, AI/ML Eric Allen, gravity9 Partner

Atlas Vector Search Webinar (English)

Unlock the Power of Search Nodes: Performance at Scale

In this webinar, Seth Payne, Staff Product Manager at MongoDB, provides a guided walkthrough of Atlas Search Nodes functionality. With the introduction of Atlas Search Nodes, you can now achieve better performance at scale, workload isolation, higher availability, and optimized resource usage by decoupling search usage from database needs. The webinar includes: A walkthrough of Atlas Search Nodes. An overview of the benefits compared to coupled configurations. How to migrate and get started with Search Nodes. Highlights of a few success stories.