Our backend infrastructure at Tinder relies on Redis-based caching to fulfill the requests generated by more than 2 billion uses of the Swipe® feature per day and hosts more than 30 billion matches to 190 countries globally. Most of our data operations are reads, which motivates the general data flow architecture of our backend microservices.
Source: medium.com
At LinkedIn, we like to say that experimentation is in our blood because no production release at the company happens without experimentation; by “experimentation,” we typically mean “A/B testing.” The company relies on employees to make decisions by analyzing data. Experimentation is a data-driven foundation of the decision-making process, which helps with measuring the precise impact of every change and release, and evaluating whether expectations meet reality.
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In this blog post we’ll show you how we migrated a critical Postgres database with 18Tb of data from Amazon RDS (Relational Database Service) to Amazon Aurora, with minimal downtime. To do so, we’ll discuss our experience at Codacy.
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