Nithyashree Alwarsamy

The enterprise data landscape is scattered. Teams are trapped in disconnected tools, business users wait days for insights, and engineers build fragile one-off integrations. This hands-on workshop shows how to integrate Amazon Quick MCP actions with Snowflake Cortex AI's MCP server to build AI agents that transform how teams interact with enterprise data. Business users can ask questions such as "What are our top-performing products this quarter?" without writing SQL, while IT maintains security and governance controls. Walk out with working code and production-ready patterns that support self-service analytics across your organization.

Organizations have various streaming use cases where they need to ingest unstructured data — such as customer reviews or social media comments in text or JSON format — into a lakehouse. Before storing this data in database tables, they often want to process it in real time using generative AI capabilities, such as fraud analysis, sentiment analysis and summarization. This hands-on lab provides step-by-step guidance on building an end-to-end data pipeline using Amazon Bedrock, Amazon Managed Service for Apache Flink, Amazon Data Firehose and Snowflake.