Amazon.com on AWS

Building on AWS, Amazon Customer Fulfillment reduced unplanned equipment downtime hours by 69 percent, helping the company meet its goal of fulfilling customer orders on time. Amazon Customer Fulfillment has fulfillment centers worldwide where employees pick, pack, and ship customer orders. Because of the global scale of the company, Amazon Customer Fulfillment wanted to implement a predictive maintenance program as part of its broad maintenance strategy to make it more affordable and efficient. Using Amazon Monitron, Amazon Customer Fulfillment can be more effective with technician time, avoiding costly unplanned equipment downtime with predictive maintenance.

The Amazon customer packing experience team partnered with AWS to build a machine learning (ML) solution to make more sustainable packaging decisions, while keeping the customer experience bar high. Amazon is the co-founder and was first signatory of The Climate Pledge, which calls on signatories to commit to achieving the goals of the Paris Agreement 10 years early — reaching net zero carbon by 2040. Since 2015, the company has reduced the weight of its outbound packaging by 33 percent, eliminating 915,000 tons of packaging material worldwide, or the equivalent of over 1.6 billion shipping boxes. With less packaging used throughout the supply chain, volume per shipment is reduced and transportation becomes more efficient. The cumulative impact across Amazon’s network is a dramatic reduction in carbon emissions.

Amazon Fulfillment Technologies migrated from a legacy custom solution for identifying misplaced inventory to Amazon SageMaker, reducing AWS infrastructure costs by a projected 40 percent per month and simplifying its architecture.

Amazon migrated its Item Master Service from Oracle to Amazon DynamoDB to improve availability and scalability, reduce the operational burden, and improve utilization. Amazon is the world’s leading online retailer and provides a wide range of cloud services through its Amazon Web Services (AWS) division. The company used AWS Database Migration Service to migrate 150 TB of data in just two months with zero downtime, and now relies on Amazon DynamoDB to process more than five billion catalog updates every day without significant manual effort.

Amazon migrated its analytics system from Oracle to AWS to enable greater agility, reduce operational cost and effort, and support growing performance needs. Amazon is the world’s leading online retailer and provides a wide range of cloud services through its Amazon Web Services (AWS) division. The company built a high-performance analytics infrastructure in the cloud using Amazon S3, Amazon Redshift, Amazon EMR, and a range of other AWS services.

Amazon migrated its Inventory Management Services from Oracle Database to Amazon Aurora to improve availability and scalability and reduce its operational burden. Amazon is the world’s leading online retailer and provides a wide range of cloud services through its Amazon Web Services (AWS) division. The company moved 700 Oracle instances to Amazon Aurora with PostgreSQL compatibility, achieving greater throughput, scalability, and resiliency.

Amazon migrated 5,000 databases from Oracle to AWS, cutting its annual database operating costs by more than half and reducing the latency of most critical services by 40 percent. Amazon is the world’s leading online retailer and provides a wide range of cloud services through its Amazon Web Services (AWS) division. For databases supporting critical services and requiring high availability, Amazon is using Amazon DynamoDB and Amazon Aurora; for noncritical services, Amazon RDS for PostgreSQL and MySQL; and for inexpensive, long-term storage of relational and non-relational data, Amazon S3.

Amazon migrated its transactional subledger from Oracle to AWS to maintain its core mission of delivering timely, complete, and accurate financial reports as transaction volumes continue to grow. Amazon migrated critical databases to Amazon DynamoDB and used a lift-and-shift approach to migrate relational databases to Amazon RDS.

Amazon migrated its Item Master Service from Oracle to Amazon DynamoDB to improve availability and scalability, reduce operational burden, and improve utilization. Amazon used AWS Database Migration Service to migrate 150 TB of data in just two months with zero downtime, and now relies on Amazon DynamoDB to process more than five billion catalog updates every day without significant manual effort.

The Amazon.com Transaction Risk Management Services (TRMS) team migrated 40 TB of data from on-premises Oracle databases to AWS in just six months with only one hour of downtime. TRMS runs the Buyer Fraud Service, which uses machine-learning algorithms to predict and prevent fraudulent transactions on Amazon.com. The TRMS team used a migration stack that included AWS Database Migration Service (DMS) to migrate to a new relational database solution based on PostgreSQL-compatible Amazon Aurora.

Amazon Advertising Engineering and Development (AED) increased throughput and avoided a three-year project to rebuild performance monitors and management tools by shifting data held in Oracle databases to Amazon RDS. AED builds, manages, and scales the technologies that undergird Amazon's programmatic advertising offerings. The new solution uses PostgreSQL relational databases that are fully managed by Amazon RDS, with Amazon S3 for storing backups.

Amazon.com switched from tape backup to Amazon S3 for backing up the majority of its databases. This strategy reduced complexity and capital expenditures, provided faster backup and restore performance, eliminated tape-capacity planning for backup and archive, and freed administrative staff for higher value operations. By switching from tape backup to Amazon S3, Amazon.com realized a 12x performance improvement and reduced restore time from 15 hours to 2.5 hours in select scenarios.

When customers across the globe place orders on Amazon.com, those orders are processed through many different backend systems. One of those key systems is Herd, a workflow-orchestration engine developed by the Amazon eCommerce Foundation Team. Herd controls the business logic for processing all Amazon.com customer orders worldwide, orchestrating more than 1,300 workflows for everything from order processing to fulfillment-center operations to coordinating parts of the Amazon Alexa backend. A mission-critical system used by more than 300 Amazon engineering teams, Herd executes more than 4 billion workflows on peak days.

When Amazon Prime Video won the rights to stream NFL Thursday Night Football games in more than 200 countries, it knew it needed to provide the best possible experience for millions of fans watching their favorite teams. “With live sporting events, reliability and low latency are absolutely critical because every lost second negatively impacts viewers,” explains BA Winston, the global head of digital video playback and delivery for Amazon Video. “If there are any interruptions or buffering, the fans won’t watch.”

When customers across the globe place orders on Amazon.com, those orders are processed through many different backend systems. One of those key systems is Herd, a workflow-orchestration engine developed by the Amazon eCommerce Foundation Team. Herd controls the business logic for processing all Amazon.com customer orders worldwide, orchestrating more than 1,300 workflows for everything from order processing to fulfillment-center operations to coordinating parts of the Amazon Alexa backend. A mission-critical system used by more than 300 Amazon engineering teams, Herd executes more than 4 billion workflows on peak days.