Available On-Demand

Building and deploying GenAI applications can be challenging, especially when data is spread across different, disconnected systems. When teams face issues with slow batch processing or fragmented data, it can hold up AI projects. A more efficient approach can help.

In this demo, you’ll learn how to:

Build a real-time knowledge base for RAG architecture on a data streaming platform
Use Flink SQL for vector embedding, transforming data into AI-ready formats directly within your data pipeline
Configure and launch a fully managed sink connector to update MongoDB Atlas Vector Search

Plus, explore how Confluent’s ecosystem of 120+ pre-built connectors helps you access and integrate your enterprise data, speeding up the development of GenAI applications. This connector portfolio makes it easy to connect diverse data systems to a data streaming platform, allowing for real-time updates to your vector database for RAG implementations.

Mac KhajuriaSenior Product Marketing Manager, Confluent

Mac is a senior product marketing manager at Confluent responsible for go-to-market for Kafka connectors. Prior to Confluent, he led a team of solution architects at Google designing marketing infrastructure for retail customers.

Erick LeeSenior Product Manager, Confluent

Erick Lee is a Product Manager at Confluent focused on Connect. Prior to product management, he worked as a Solutions Engineer partnering with Digital Native customers on building out their data streaming platforms. He started his career at Oracle within the analytics and data warehouse space. Outside of work you'll often time find him rock climbing and enjoying the outdoors.

How to Build GenAI apps Faster with Confluent’s Connectors

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Available On-Demand