AWS re:Invent 2025 - Modernize your data warehouse by moving to Amazon Redshift (ANT317)

Modernizing Data Warehouses with Amazon Redshift

Key Trends in Data and Analytics

• Over 80% of data and analytics leaders see generative AI and agentic AI as key strategic initiatives
• However, over 50% still struggle with foundational data issues like data quality, data silos, and data fragmentation
• Leaders are focused on:
  • Curating high-quality, unified data
  • Breaking down data silos
  • Ensuring data security and governance
  • Delivering analytics capabilities at scale and cost-effectively

How Amazon Redshift Addresses These Needs

• Redshift provides a modern, cloud-based data warehouse architecture:
  • Separation of storage and compute for flexibility and cost-optimization
  • Lakehouse capabilities to handle structured and unstructured data
  • Multi-cluster architecture for scalability, reliability, and workload isolation
• Redshift continuously adds new capabilities to simplify data warehousing and analytics:
  • Performance enhancements across on-demand and provisioned compute
  • Expanded support for real-time data integration from diverse sources
  • Tight integration with AI/ML and generative AI tools like Redshift MCP Server

Customer Success: Charter Communications

• Migrated 600TB of data from on-premises to Redshift
• Achieved 35% cost reduction and 18% SLA improvement
• Simplified architecture, eliminated data duplication, and improved disaster recovery
• Gained elasticity and scalability to handle spikes in demand (e.g. new Netflix show launches)

Simplifying ETL with Zero ETL Integration

• Demonstrated how Redshift's Zero ETL Integration can replicate data from sources like Salesforce, DynamoDB, and PostgreSQL directly into Redshift
• Eliminates the need for complex ETL pipelines, allowing data to be made available to analysts faster

Empowering Analysts with Redshift MCP Server

• Redshift MCP Server integrates large language models (LLMs) to allow analysts to query data using natural language
• LLMs can automatically identify the right tools and sequence to execute to answer the analyst's question
• Enables self-service analytics without relying on IT teams for data access and queries

Roche's Data Warehouse Modernization Journey

• Roche faced challenges with data silos, slow time-to-insight, and inability to scale innovations globally
• Adopted a people-process-technology framework to modernize their data architecture:
  • Reorganized teams into specialized roles (data engineers, analytics engineers, data analysts)
  • Implemented DevOps, agile, and transparency practices
  • Migrated to a cloud-based, Redshift-centric architecture
• Key benefits:
  • Operate at massive scale (300+ data sources, 3M Redshift queries/day)
  • Achieve faster time-to-insight (new solutions in days, not months)
  • Deeper customer understanding by connecting disparate data sources

Getting Started with Redshift

• AWS provides a variety of tools and resources to simplify the migration and modernization journey:
  • Database migration and schema conversion tools
  • Professional services and partner support for scoping and executing projects
  • Migration acceleration programs to offset migration costs
• Additional Redshift learning resources:
  • Redshift website, blogs, tutorials, and customer success stories
  • Redshift LinkedIn group for updates and community engagement
  • Books and training materials

Key Takeaways

• Strong data foundations are critical to unlocking the value of generative AI and agentic AI
• Redshift provides an enterprise-grade, cloud-native data warehouse platform to modernize analytics
• Redshift's capabilities around data integration, self-service analytics, and AI/ML integration enable faster time-to-insight and better business outcomes
• Successful data warehouse modernization requires a holistic approach addressing people, processes, and technology
• AWS provides comprehensive tools and resources to support the Redshift migration and modernization journey