🚀 From Legacy Systems to the Cloud: My Real-Time Data Migration Experience & the Power of AI Today ☁️
A few years ago, I led a team of data analysts and scientists in one of the most challenging cloud and data migration projects of my career. We migrated 100 years' worth of data from disparate systems—mainframes, multiple database platforms, and data warehouses—to AWS Cloud.
📌 Key Challenges We Faced
🔹 Data inconsistencies across various legacy systems
🔹 Massive data cleansing & consolidation efforts
🔹 Complex ETL processes consuming time & resources
🔹 Cross-functional collaboration between business, product, data engineering, and analytics teams
🔹 Months of effort to ensure a smooth, error-free migration
It was a roller coaster journey, demanding deep technical expertise and collaboration across multiple teams. If AWS Generative AI had been available back then, it could have saved months of effort, automating schema conversion, data mapping, and reducing manual fixes.
Fast forward to today:
🚀 We now have AWS Gen AI-powered services like Bedrock, Q Developer, and DMS Schema Conversion, making database migrations faster and more efficient than ever before. These tools take on the heavy lifting—accelerating schema conversion, automating code transformation, and reducing human intervention in repetitive migration tasks.
🔗 Want to learn more? Check out these resources:
🎬 AWS re:Invent talk on Gen AI for Database Migrations
📖 AWS DMS Schema Conversion Blog
If you’re working on a data migration or cloud modernization project, let's connect and discuss how AI can streamline your journey! 🚀
#AWS #DataMigration #CloudComputing #GenerativeAI #DatabaseMigration #ETL #AIforData #AWSBedrock #QDeveloper
https://drive.google.com/drive/folders/1sGopOguhzob2N-628ZIfRPZsVAey1ptM?usp=drive_link