On October 15, 2019, Amazon Chief AWS Evangelist Jeff Barr announced via blog post that it had finished migrating its internal consumer databases from Oracle database software to AWS. The project was motivated by the realization that Amazon’s team was spending too much time managing and scaling legacy Oracle databases, rather than focusing on higher-value differentiated work. More than 100 teams participated in the migration effort, including the following brands:

• Alexa
• Amazon Prime
• Amazon Prime Video
• Amazon Fresh
• Kindle
• Amazon Music
• Audible
• Shopbop
• Twitch
• Zappos

Other internal teams were involved in the project, including:

• AdTech
• Amazon Fulfillment Technology
• Consumer Payments
• Customer Returns
• Catalog System
• Delivery Experience
• Digital Devices
• External Payments
• Finance
• InfoSec
• Marketplace
• Ordering
• Retail Systems

Amazon illustrated the database migration as follows: [caption id="attachment_98187" align="aligncenter" width="700"] Source: Company reports [/caption]   The teams migrated 75 petabytes (75 x 10¹⁵ bytes) of internal data stored in nearly 7,500 Oracle databases to multiple AWS database services including Amazon DynamoDB, Amazon Aurora, Amazon Relational Database Service (RDS), and Amazon Redshift. The company reported the following benefits:

• Cost Reduction: Reduced database costs by over 60%, on top of the heavily discounted rate.
• Performance Improvements: Latency of consumer-facing applications was reduced by 40%.
• Administrative Overhead: The switch to managed services reduced database admin overhead by 70%.

Benefits from the migration of selected functions include: Advertising: Doubled database fleet size (and throughput) within the timeframe of minutes to accommodate peak traffic. Buyer Fraud: Moved 40 TB of data and realized the same or better performance at half the cost. Financial Ledger: Moved 120 TB of data, reduced latency by 40%, cut costs by 70% and cut overhead by 70%. Wallet: Moved more than 10 billion records to DynamoDB, reducing latency by 50% and operational costs by 90% in the process.