Customer 360 Analytics using Amazon Redshift Serverless Multi-Warehouse
Organizations struggle with siloed data spread across multiple Amazon Redshift warehouses, making analytics complex and inefficient. Amazon Redshift data sharing allows you to share data within and across organizations, AWS regions, and even 3rd party providers, without moving or copying the data. Read from and write to the same Redshift databases using multiple data warehouses and extend the ease of use, performance, and cost benefits that Amazon Redshift offers to multi-warehouse, data mesh architectures.
Tony Mullen
Amazon Employee
Published Jun 2, 2025
Last Modified Jun 3, 2025
The show focused on Amazon Redshift serverless multi-warehouse and customer 360 analytics. The hosts Tony Mullen, Saman and Srikant discussed Redshift's capabilities as a cloud data warehouse offering, explaining the differences between provisioned clusters and serverless options. They highlighted how Redshift enables querying data across multiple warehouses and data sources, including S3 data lakes, through features like Redshift Spectrum.
A live demo showcased how to set up data sharing between Redshift clusters, allowing different teams to securely access and query data across warehouses without needing to copy or move it. The presenters explained common use cases like workload isolation and cross-team collaboration enabled by this architecture. They also touched on cost optimization benefits of separating workloads across warehouses.
The hosts discussed Redshift's support for structured, semi-structured and unstructured data, as well as integration with streaming data sources. They briefly demonstrated Amazon Q's generative SQL capabilities for Redshift, though technical issues prevented a full demo. The show concluded with recommendations for customers to start with a proof-of-concept using available utilities and workshops to explore data sharing architectures.
Key highlights:
- Redshift offers provisioned and serverless options for different workload needs
- Data sharing allows querying across warehouses without data movement
- Supports customer 360 use cases by combining data from multiple sources
- Enables workload isolation and cost optimization through multi-warehouse setups
- Integrates with various data types and sources including S3, streaming data
- Amazon Q provides generative AI assistance for SQL queries
- Resources available to help customers start proof-of-concepts for data sharing
Tony Mullen - Senior Database Specialist @AWS
Srikant Das - GTM Specialist SA, Analytics and AI @ AWS
Saman Irfan - Senior GTM Specialist SA, Analytics and AI @ AWS
- Workload isolation, cross group collaboration, deliver data as service, share data between environment
https://aws.amazon.com/blogs/big-data/from-centralized-architecture-to-decentralized-architecture-how-data-sharing-fine-tunes-amazon-redshift-workloads/ - Customer can use test drive utility to perform POC. This is a workshop that they can use to enable them selves on using test drive https://catalog.us-east-1.prod.workshops.aws/workshops/7bf8b12a-0b90-49b0-a6e1-2af3e9fd5ec5/en-US
- Data Sharing Best Practice Blog - https://aws.amazon.com/blogs/big-data/amazon-redshift-data-sharing-best-practices-and-considerations/
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