In October 2019 Amazon's consumer business finished moving about 7,500 Oracle databases and 75 PB of data to DynamoDB, Aurora, RDS and Redshift, with little or no downtime. It cut costs by more than 60% and consumer-facing latency by 40%. How the work was split, why each workload went where it did, and what applies to a company with ten databases.
Moved about 75 PB from nearly 7,500 Oracle databases, across more than 100 teams including Alexa, Prime, Prime Video, Kindle, Audible, Twitch and Zappos, finishing in October 2019.
Chose a target per workload instead of one standard: DynamoDB and ElastiCache for low-latency, high-scale key-value work; Aurora and RDS for transactional relational work; Redshift for analytics.
Covered every proprietary system, including purchasing, catalog, fulfillment, accounting and video streaming, with little or no downtime.
Retrained Oracle DBAs as migration specialists and cloud advisors rather than replacing them.
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The biggest decision was not to replace Oracle with one thing. Workloads that were key-value in disguise went to DynamoDB and got faster; genuinely relational ones went to Aurora and RDS. A like-for-like database swap would have kept the old shape.
People were part of the plan. Retraining the DBAs kept the knowledge of what each database actually did, which is the part no tool converts.
For a company with a handful of Oracle databases, the same logic applies at smaller scale: classify each by access pattern, move the simple ones first, and leave the hardest PL/SQL estate on RDS for Oracle or Oracle Database@AWS if conversion does not pay.
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