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Edmunds Serverless Case Study

Seeing is believing, especially when you are looking for your next car. That's why Edmunds—which helps more than 20 million web and mobile users each month browse automobile dealer inventory, read vehicle reviews, and consume other automobile-related content—considers its library of vehicle photos uploaded by auto dealers to be one of its most important assets.

"The mission of Edmunds is to make car buying easy," says Nitin Mahajan, the executive director for service engineering at Edmunds. He explains that the company wants its website and mobile apps to be the best place for consumers to complete the key steps in vehicle purchasing: identifying the right vehicle, evaluating trade-ins, analyzing price and financing offers, and finalizing the purchase. "But if vehicle photos don't look right or take too long to load, customers will disengage and move on,” Mahajan says.

Edmunds was planning a key update with even better image quality and faster load times on the company's website and mobile apps. Mahajan and his team had just a few weeks to figure out how to process the company's library of 50 million vehicle images into several new aspect ratios and resolutions, which would result in more than half a billion new images.

The company's existing image-handling solution, based on Cloudera MapReduce clusters, wasn't the right tool for the job, according to Mahajan. "Development time would have taken too long, and achieving sufficient scale and processing power for this project would have required us to manage new clusters and incur new monthly costs of at least $10,000.”

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