Blueprint for the Cloud: Building a Future-Ready Data Strategy
summary
Claims that cloud migration needs a concrete data strategy not platitudes and shows actionable steps: inventory legacy ETLs and integrations, enforce column‑level lineage and metadata catalogs, apply zero‑trust, encryption and automated compliance, and run monitoring with AI. A concrete case migrates legacy APIs to AWS API gateways, rebuilds ETLs with Lambda Glue Athena and Step Functions, and moves reporting into PowerBI — outcome is no slippage, APIs operate, and pipelines deliver usable analytics.
AI-written summary · about
chapters (12)
- 0:00Introduction and Speaker Background
- 0:28The Importance of Cloud Migration
- 1:37Risks of Cloud Migration Without a Data Strategy
- 2:37Addressing Legacy Technologies
- 4:06Building a Secure and Scalable Environment
- 5:13Key Pillars of Data Strategy
- 5:58Data Quality and Security
- 7:11Integration and AI Enablement
- 7:41Principles for Secure Data Foundations
- 8:56Monitoring and Alert Systems
- 12:08Critical Steps for Cloud Transformation
- 12:45Case Study and Conclusion
related talks
31mIntroduction to Data Visualisation
19mDemystifying Modern Data Pipeline Architecture
36mNew trends in data modernization: Catalysts for AI/ML analytics on AWS
31mFrom Raw Data to Real Impact: Lessons from Real-World Transformations
39mLessons Learned: Insights from 30 Years in Cloud and AI/ML Integration
29mBecoming a data-driven enterprise
01h 00mModernizing Legacy Systems: AI Enhanced Cloud Adoption Frameworks
27mData team strategies: unleashing your team