Harshida Patel

In this session, we will introduce AWS Analytics Model Context Protocol (MCP) Servers, including the Data Processing MCP Server and Amazon Redshift MCP Server, which enable agentic workflows across AWS Glue, Amazon EMR, Amazon Athena, and Amazon Redshift.

There’s never been a better time to work with data in the cloud. Rapid adoption of new data architecture patterns and creative breakthrough technologies such as generative AI are rapidly moving data and analytics to the cloud.

Amazon Redshift continuous innovations provide cloud data warehousing capabilities that deliver price-performance leadership and ease of use with scale. Learn how Amazon Redshift features, built on the reliability and performance this service is known for today, can help you empower developers with automated capabilities, reduce time to business insights, or integrate across data types, AWS,...

As data and demand for analytics have grown, Amazon Redshift has added innovative new features that allow you to scale with more data and more users while making it easier to manage and bring down costs. In this session, take an in-depth look at the best practices for data warehousing with Amazon Redshift.

Loading data into your data warehouse quickly and reliably is a big pain point for many organizations. In this session, we show you how to generate the schema for your semi-structured data; create ETL code to transform, flatten, and enrich the data; and load it into Amazon Redshift on a recurring basis.

In this session, we take an in-depth look at best practices for data warehousing with Amazon Redshift. We show you the best way to analyze all your data, inside and outside your data warehouse, without moving the data, which helps you gain deeper insights for running your business.