Name: Einführung in die Datenmodellierung
Start: 2025-11-17T09:59:00Z
End: 2025-11-17T09:59:54.000Z
Location: BrightTALK

Watch our experts explore how MongoDB can help you modernize faster through best practices, technical deep dives, and customer success stories.

The data that powers modern applications comes in all shapes and sizes. When traditional relational databases struggle with large volumes of unstructured data, incomplete JSON implementations, and clunky horizontal scaling, innovation suffers. True innovation requires a modern, AI-ready database.

In generative AI applications, your database directly impacts response latency, application performance, and output accuracy. This session compares two solutions for vector data storage, indexing, and retrieval: MongoDB Atlas and Elasticsearch. We break down how each handles semantic search, provide performance insights, and walk through best practices for implementing vector search in MongoDB Atlas.

Whether you're building AI chatbots, recommendation engines, or agentic systems, understanding how your database powers LLM-enabled applications is essential. Watch now to gain practical knowledge and working code examples that will help you design and optimize AI-powered solutions with MongoDB Atlas.

AI Database Comparison: MongoDB Atlas vs. Elasticsearch

Entfessel das wahre Potenzial Deiner Daten mit der MongoDB Query API und Aggregation Pipeline. Mit zunehmender Komplexität Deiner Daten werden diese hochpräzisen Tools für das intelligente Abrufen, Verarbeiten und Analysieren umfangreicher JSON-Dokumente unverzichtbar.

Einführung in Abfragen und Aggregation

What will banks of the future look like?
How should AI be orchestrated with banks’ existing technology stacks?
How can fraud management be bolstered with an AI-powered banking layer?

Artificial intelligence (AI)-powered deep fake tools have exploded in development and utilisation in the last few years. Around the world, banks are losing billions every year as a result of these increasingly sophisticated threats. To address this challenge – while at the same time automating operations, unearthing notable transaction correlations, and proactively blocking suspicious activity – banks of the future are reimagining their technology stacks and placing an AI at the heart of their architectures.

Modern, AI-driven banking architectures often have on the front-end an engagement layer, incorporating capabilities for multi-modal conversational experiences with intelligent products and services. To achieve effective digital engagement, agentic AI is key – serving to streamline the interface with customers, better understand their financial needs, and deliver tailored offerings.

Supporting the middle and back-end elements is an AI-powered decision-making layer. With specialised, smart agents in narrow domains, AI promises to raze historic inefficiencies around fraud management – and deliver enhanced detection, advanced transaction monitoring, and behavioural risk scoring. Underpinning it all is the core infrastructure. This supports enterprise data, storage and processing – all of which can be orchestrated with the AI layers, to enable high levels of automation, explainability, traceability, and transparency.

By making these systemic changes today, banks can prepare for tomorrow – and bolster their security in the process.

Watch this Finextra webinar, hosted in association with MongoDB, to hear our panel of experts discuss how AI, at every level of banks’ technology stacks, can supercharge fraud detection.

AI-powered banking stacks: The secret to fraud management

Learn more about open finance innovation with MongoDB: https://mdb.link/GKBLBmJUTc4-blog

Open finance is transforming banking, but data integration, security, and compliance make it challenging. This video explores how MongoDB solves these issues with seamless API integration, flexible data handling, and enterprise-grade security—plus, a live demo of Leafy Bank’s open finance solution.

Visit Mongodb.com → https://www.mongodb.com
Read the MongoDB Blog → https://www.mongodb.com/blog

Embracing Open Finance Innovation with MongoDB

Voyage AI, a leader in embedding and reranking models that make AI-powered search and retrieval more accurate, has now joined MongoDB. In our recent webinar, MongoDB’s Frank Liu, Staff Product Manager and Richmond Alake, Staff Developer Advocate break down the powerful new AI capabilities that are now easily accessible to developers everywhere. 

Watch it on-demand now for a deep dive into reranking and vector embeddings for building AI-powered user experiences followed by a technical question-and-answer period. We hope to see you at our next webinar!

Scaling Vector Database Operations with MongoDB and Voyage AI

Visit our solution library to learn more about leveraging generative AI: https://trymongodb.com/3ZZjFoT

Watch MongoDB's Industry Solutions Specialist Sebastian Rojas demonstrate how to revolutionize business loan risk assessments using MongoDB and Generative AI. Discover how to combine AI technology with powerful database solutions for faster, more accurate loan decisions. See a live demo showcasing RAG (Retrieval-Augmented Generation) and geospatial queries in action! 

Want to learn more about AI solutions? Download our free AI e-book here: https://trymongodb.com/3ZDucov

Enhance Business Loan Risk Assessments: Leverage Generative AI for Smarter Decision-Making

Learn more about data transformation and earn a free Skills Badge: https://mdb.link/GL7V1TCPG1U-skill

In this MongoDB video, we'll explore the powerful capabilities of MongoDB's aggregation framework, which is essential for transforming, combining, and filtering large datasets. You'll learn about various aggregation stages, their importance, and best practices for optimizing your data operations, all aimed at enhancing your real-time insights and analytics.

00:00:00 Introduction to MongoDB's Aggregation Framework
00:01:38 Common Aggregation Stages and Best Practices
00:04:27 The Query Optimization Phase
00:04:51 Building and Using Aggregation Pipelines
00:05:55 Conclusion

Visit Mongodb.com → https://www.mongodb.com
Read the MongoDB Blog → https://www.mongodb.com/blog

Intro to MongoDB Aggregation Framework | Fundamentals of Data Transformation

In this video, we explore differences between relational databases and the document model in MongoDB, focusing on adapting your existing data modeling skills to MongoDB's flexible document model. Using a bookstore example, we cover key concepts like polymorphism, embedding, and array usage, demonstrating how MongoDB simplifies schema design and optimizes queries. 

This guide is ideal for developers familiar with SQL who want to harness MongoDB's power and flexibility for modern applications. Join us to learn how MongoDB transforms your approach to data management.

Practice what you learned with a hands-on lab and earn a free digital Skill Badge here: 
https://learn.mongodb.com/learn/course/relational-to-document-model-on-demand-marketing-youtube/

MongoDB Data Modeling and Schema Fundamentals | From Relational to Document Model

A frequently asked question in the MongoDB community is “I’m designing an application to do X, how do I model the data?” In this video, we will examine the most important considerations when making decisions about your data model, so that you can create the model that best fits your application.

You will learn:

The differences between data modeling in MongoDB vs. relational databases
The key considerations for data modeling with MongoDB
How to further enhance the power of the document model with schema design patterns

Intro to Data Modeling Docs → https://www.mongodb.com/docs/manual/data-modeling/
Data Modeling Explained → https://www.mongodb.com/resources/basics/databases/data-modeling

Intro to Data Modeling

Wealth management is entering a new era. Financial institutions are under pressure to deliver more personalized, intelligent, and real-time insights to clients. This webinar will showcase how to build next-generation, AI-driven portfolio management solutions powered by MongoDB Atlas and Agentic AI.

You’ll learn how Agentic systems, underpinned by embeddings, LLMs, Retrieval-Augmented Generation (RAG), vector search, and AI frameworks such as LangGraph, are transforming portfolio management by delivering sophisticated investment insights that empower portfolio managers with more intelligent decision making about risks and assets reallocation. This solution covers stocks and crypto assets;  macroeconomic indicators; sentiment analysis for news and social media; the role of MongoDB is enabling agent memory and an example of MCP implementation.

What you’ll take away:
- How Agentic AI with the power of MongoDB is revolutionizing wealth portfolio management.
- How MongoDB AI-ready architecture is accelerating real-time decisions and faster time to market of Agentic AI wealth management solutions.
- How domain specific Voyage AI embedding finance model is enhancing the quality and accuracy of AI outcome.
- Review the architecture and technical deep dive.
- A live demo walkthrough.

Join us to see how MongoDB + Agentic AI can help your organization modernize wealth management and stay ahead of the curve.

Transforming Wealth Management with MongoDB + Agentic AI

Sei dabei, wenn Solutions Architect Jannes Buchholz MongoDB entmystifiziert und die unvergleichlichen Vorteile des Dokumentdatenmodells präsentiert. Diese Sitzung ist Dein Schnelldurchgang zum Einstieg in MongoDB Atlas.

Einführung in MongoDB

Lift-and-shift isn’t enough. MongoDB’s Vinod Bagal breaks down how to modernize your data for AI — and why waiting could cost you your competitive edge.

From Lift-and-Shift to AI-Ready Data

Learn how leading enterprises are building intelligent, AI-driven applications using MongoDB Atlas and Microsoft Azure by watching our on-demand webinar. In this session, MongoDB and Microsoft experts explore how Atlas on Azure powers the next generation of AI with the use of RAG, machine learning, and agentic systems.

Topics covered include:

- Building powerful agentic systems with Azure AI Foundry and MongoDB’s native support for vector search, hybrid search, and real-time reasoning 
- Optimizing storage and compute with Atlas Vector Search Nodes for gen AI-specific workloads 
- Deploying securely with deep Azure integration and industry-leading encryption capabilities

Build Real AI Success: From Vectors to Value with MongoDB Atlas on Azure

In the rapidly changing landscape of modern industries, businesses need agile and robust data solutions to stay ahead. In this webinar, we explore MongoDB's flexible and powerful architecture, data model, and out-of-the-box capabilities that are essential for driving innovation at speed and scale. Presenters from MongoDB and trusted partner, Iron Mountain, explain how MongoDB empowers businesses to adapt and thrive in dynamic environments:

- Understand the architectural differences between MongoDB and traditional relational databases.
- Discover how MongoDB’s document data model enhances development speed and flexibility.
- Explore real-world use cases showcasing MongoDB's effectiveness in supporting diverse applications, from transactions to AI.

How Iron Mountain Redefined Data Strategy for Modern Business Needs

Senior Developer Advocate Jesse Hall will showcase the unique advantages of the document data model and show you how to deploy an Atlas cluster. Jesse will present a demo of how to get started with MongoDB Atlas and walk you through how to:

- Create your first cluster, add an IP address and set up a database user
- Load sample data using a Next.js e-commerce demo app
- Connect to the database

Intro to MongoDB

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.

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

Join TDWI VP of research Fern Halper and experts from MongoDB as they discuss how to modernize applications effectively by infusing AI into the process. This webinar will highlight how AI can accelerate code analysis, generate documentation, improve testing, and enhance overall modernization strategies while ensuring reliability and trust. Participants will learn how AI augments—not replaces—engineering best practices. 

Topics include:

- Common misconceptions and challenges in legacy application modernization
- How AI can assist with code transformation, testing, and documentation
- MongoDB’s modular, slice-based modernization methodology using dependency graphs and AI
- Best practices for building stakeholder trust through pilots and transparent modernization processes

Modernizing Applications with AI: Strategies for Smarter Transformation

MongoDB Webinars (German)

Hast Du Dich jemals gefragt, wie Du Deine Daten perfekt für eine Anwendung gestalten kannst?

In diesem Webinar wird diese häufig gestellte Frage direkt angegangen. Wir werden die entscheidenden Faktoren für eine effektive Datenmodellierung untersuchen, um sicherzustellen, dass Deine Anwendung ihre maximale Leistung erbringt.

Du erfährst:
- Die wichtigsten Unterschiede in der Datenmodellierung zwischen MongoDB und Relational Database Management System (RDBMS).
- Wesentliche Schritte zur Modellierung von Daten speziell mit MongoDB.
- Wie Du Dein Dokumentmodell mithilfe leistungsstarker Schema-Design-Muster optimieren kannst.

Einführung in die Datenmodellierung

Data modeling

Database Management

Data Best Practices

MongoDB

document model

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Einführung in die Datenmodellierung

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MongoDB