Resources

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 Node functionality. With the recent 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.

Build with MongoDB AI Applications Program: A Generative AI-Powered Predictive Maintenance Solution with MongoDB Atlas, Langchain and Cohere

Artificial intelligence (AI) is revolutionizing industries worldwide, and manufacturing is no exception. With the emergence of generative AI, a new era of predictive maintenance is emerging, where machine failures are anticipated, repair plans are generated, and personalized repair guidance is provided to achieve maintenance excellence. For manufacturers seeking maintenance excellence, a unified data store and a developer data platform are key enablers. These tools provide the foundation for integrating AI applications that can analyze sensor data, predict failures, and optimize maintenance schedules. MongoDB Atlas is the only multi-cloud developer data platform available that is designed to streamline and speed up developers' data handling. In this webinar, we’ll explore how you can build an end to end predictive maintenance solution using MongoDB Atlas, Langchain and Cohere. We will also show an Agentic AI demo where multiple AI agents work together in a predictive maintenance scenario. Agenda: The significance of an predictive maintenance and how generative AI plays a role in maintenance excellence programs in Industry 4.0 architectures Introduction to MongoDB Atlas Vector Search and MongoDB AI Applications Program End to End predictive maintenance solution architecture Agentic AI for predictive maintenance Live Demos Best practices for scaling and sustaining your generative AI applications Whether you work in OT, IT, as a developer, architect, or data scientist, this webinar promises valuable insights and actionable advice for elevating your industry 4.0 projects.

Future of LLM Applications with LangChain and MongoDB

In today's rapidly evolving AI landscape, retrieval-augmented generation (RAG) is becoming essential for enterprises that want to build scalable, reliable AI solutions. This insightful webinar explores the future of large language model (LLM) applications. We explore effective strategies for implementing AI agents and RAG using LangChain and MongoDB and gain practical solutions to overcome common challenges in developing LLM applications. LangChain provides a powerful framework to build, run, and manage LLM applications, while MongoDB Atlas’ vector database unifies operational, metadata and vector embeddings to build intelligent solutions. The combination of these powerful tools enable enterprises to securely launch gen AI applications to production. Prakul Agarwal, Sr. Product Manager at MongoDB, and Erick Friis, Founding Engineer at LangChain, share insights into the deep integration between MongoDB Atlas and LangChain to help you: Develop powerful AI agents. Conduct RAG and address ingestion challenges. Build Agentic RAG using MongoDB as a checkpointer. Progress from simple to advanced retrieval. Don’t miss this opportunity to take your gen AI applications to the next level.

RAG Beyond the Chatbot: Unlocking Enterprise Value

Watch Sam Charrington, founder of TWIML and host of the TWIML AI Podcast, Elio Naciso, CEO at Scalestack, and Richmond Alake, Staff Developer Advocate, AI/ML at MongoDB, for an insightful discussion on the evolving landscape of RAG in the enterprise. This session will unveil RAG's full potential beyond chatbots, equipping technology leaders with essential concepts and a strategic roadmap to implement high-value generative AI applications while navigating common pitfalls. You'll gain practical insights into: Demystifying RAG core concepts and components. Identifying and prioritizing high-value RAG opportunities within your organization. Key implementation challenges and strategies to overcome them in enterprise environments. The future of RAG and its potential to drive innovation and competitive advantage in your industry.

AI-Powered Personalization to Drive Next-Generation Media Channels

Media producers face an ever-growing level of competition. To keep users engaged, it is crucial to quickly and intelligently learn from their interactions and content consumption, deriving insights into their preferences and what content they are likely to pay for. In this webinar, we will explore how MongoDB Atlas Vector Search can be positioned at the architectural core of a modern, AI-supported media platform. View this webinar to learn about: Easy creation of intelligent content recommendations Generation of comprehensive content summaries The automatic extraction of SEO keywords from text Enhancing content visibility and search engine ranking Through examples using Python and LangChain, you'll discover how to seamlessly integrate these powerful features into your applications, helping you stay ahead of the competition.

Haunted by SQL? How MongoDB Can Save Your Database Nightmares

For decades, traditional relational databases (RDBMS) have been the foundation of data storage, born in an era when minimising storage costs was paramount. But today’s challenges are different. While storage costs have plummeted, the complexity of working with data remains a critical barrier. Developers need to move fast, innovate, and support a wide range of use cases — relational, time series, graph, document, search, and even vector search — without the inefficiencies of data duplication and silos. In this session, we’ll explore how MongoDB Atlas can help you escape the limitations of traditional SQL databases, reduce complexity, and unlock the full potential of your data to build cutting-edge applications.

What’s New in MongoDB for .NET Developers

Join Developer Advocate Luce Carter as she explores MongoDB's latest advancements for .NET and C#. In this session, you'll discover: How the C# driver lets you connect to MongoDB. How the MongoDB provider for Entity Framework Core lets developers access the full spectrum of MongoDB’s capabilities within the familiar EF environment. How to use MongoDB with Microsoft Semantic Kernel and .NET Aspire to create modern AI-powered and cloud-native apps.

Tricks to Treat Your Database: Spooky MongoDB Schema Anti-Patterns and How to Avoid Them

Designing the right database schema is an essential skill for any MongoDB developer. You've probably heard the best practices many times before, but with Halloween just around the corner, let's take a more sinister route. Instead of focusing on the "right way" to do it, we'll confront the spooky anti-patterns lurking in your database schema. We'll learn how to identify these problems and, more importantly, how to exorcise them from your schema once and for all. In this session, we'll dive into six common MongoDB schema design anti-patterns, all revolving around document sizing, and learn how to overcome them.

The Cloud, AI and Financial Services: How they can play together

Financial Services companies have always been at the forefront of technology and the story is no different concerning two of the latest tech trends: the move to the cloud and artificial intelligence. Financial services companies are anxious to put both to good use in their never-ending search for a competitive edge. MongoDB and PeerIslands are intimately involved in helping financial institutions meet their goals. Watch this webinar to hear experts from both companies discuss topics including: The drivers for continued digitization and how best to contain costs along the way How to take advantage of the cost and scale benefits of the cloud, while maintaining security The role of modern databases in promoting data analytics and real-time data access How AI is accelerating the move to modernization, playing a role in everything dealing with legacy code to implementing new features Maintaining a focus on business value amid all the technology buzz You will hear from: Wei You Pan: Director, Financial Services Industry Solutions at MongoDB Rajesh Rajagoppalan: President & CTO at PeerIslands Paul Desmond: Content Strategist at CIO Marketing Services

Atlas 101 eWorkshop (English)

Intro to Data Modeling for Financial Services and Insurance (APAC timezone)

With its document-oriented data model, real-time analytics capabilities, and seamless integration with other cutting-edge technologies, MongoDB has become the go-to choice for forward-thinking financial and insurance institutions. Since the relational schema and MongoDB schema are different, a question that keeps getting asked is: “I’m designing an application to do X, how do I model the data?” How you model your data early on affects the speed of development throughout your application lifecycle and can have ramifications on performance far down the line. In this webinar, we will examine the most important considerations for financial applications when making decisions about the data model that best fits your application. This session is designed to introduce developers, technologists, and technical decision-makers to the NoSQL mindset for financial organizations and to break down NoSQL data modeling for real-world workloads. We'll cover: The differences between data modeling in MongoDB vs. relational databases How to further enhance the power of the document model with schema design patterns applied to financial use cases

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

In this webinar, we reveal results from our benchmark tests between these two different database systems. The webinar was presented by MongoDB Field CTO, Rick Houlihan, who has decades of experience working with RDBMS, PostgreSQL, and document databases like MongoDB. Watch now and find out why it’s essential to consider database platforms specifically designed to handle large, complex data structures to effectively support modern AI applications.