Kick off MongoDB Day with insightful talks and hands-on exercises designed to boost your technical skills. Learn the principles of schema design, dive into coding web services with MongoDB, and explore critical features like indexes, locking, and transactions. Build a real-time reporting service using the Aggregation Framework and apply these concepts during live demonstrations and Q&A with MongoDB experts.
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Don't miss your chance to sign up for one-on-one Design Review Sessions with MongoDB specialists to receive personalized feedback on your projects.
Scroll to peruse the full agenda, search for topics of interest.Â
Please note agenda is subject to change without any prior notice.
Content will be delivered in English.
Creating a schema for a relational database is a straightforward process, but designing a schema for a MongoDB application may seem a little more challenging. However, it doesn't have to be if you follow the main principles that MongoDB has identified for its users. This talk covers these data modeling principles and provides modeling tips to help you define the most effective document schema for your workload.
Exercise: The exercise introduces a minimalist browser-based development environment that lets you instantly code and test web services and access a MongoDB Driver to let you interact with the database. We start with basics of the environment and then go on to cover connecting to MongoDB, receiving input via HTTP and inserting it into MongoDB, Retrieving that data by various attributes and performing updates on it.
This is a beginner session that covers some critical underlying things that developers often never fully understand like extended JSON and BSON types. It also talks about the different ways to interact with MongoDB and some of the pros and cons of each.
This session is a high level overview of the Atlas Developer Data Platform. MongoDB Atlas is far more than just a database, in this session we tell the story of the growing set of business requirements for a large organization with both public and partner interfaces. We talk about the features of the developer data platform in context, how and when to use them including using Generative and Inferential AI - this session is more about understanding at a high level what the MongoDB developer data platform provides so you can make an informed decision about what to use, we talk about both function and non-functional requirements and capabilities.
Exercise: In the exercise, discover how the same API allows you to not only interact with MongoDB as a database but also Atlas Search for text searching, Vector Searching for concepts as well as reporting using Atlas SQL
- Build RESTful services to support text search with relevance scoring.
- Work with an AI LLM and combine it with vector search in a RAG architecture to sell a travel booking service.
- Report on data in MongoDB the old fashioned way using SQL.
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Many MongoDB developers learn CRUD - how to store data, how to retrieve and perform simple updates to it then never go on to learn the next set of critical features. In this session, we go beyond basic CRUD and talk about working with arrays, expression-based queries, projections and updates as well as discussing all important schema enforcement.
Exercise: In this session you can try out the examples shown and explore these key next-level concepts and how they interact with your data.
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As well as storing and retrieving data MongoDB can perform computation on data inside the database this is faster as it's close to the data and avoids network performance and costs - In this session, we teach you to use MongoDB's aggregation framework to summarize and analyze data in the database and to return just the results.
Exercise: In this workshop, you will be given a specification for a new Management reporting service and compete to be the first to implement a service that provides the required reporting data for management to drive their dashboards
Register for a 50 minute whiteboard session with a MongoDB expert where individual Morgan Stanley development team can explore how their own workloads might be supported by a NoSQL backend. You will dive deep into your project and design a data model in real time. Teams can expect to leave these sessions with a documented data model and enough knowledge to iterate and optimize the design on their own.
