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Case Study

How Spotify Serves Personalized Music Recommendations

Music for everyone. That’s Spotify’s promise. And it lives up to it by serving over 140 million music lovers across the world.  Every day it renders personalized music recommendations to hundreds of millions of happy customers.

“Spotify is a global business with a global user base. Being able to provide a great audio experience to our users is a key priority for us,” said Niklas Gustavsson, chief architect at Spotify. 

To be able to do that, Spotify needed a system that would scale and enable personalization and provide a seamless customer experience. That’s why it moved its infrastructure to Google Cloud and Cloud Bigtable. This allows Spotify to deliver recommendations at scale, roll out experiments quickly, and ingest terabytes every day via Cloud Dataflow. 

“With Cloud Bigtable clusters in Asia, Europe, and the United States, we’re able to get low-latency data access all over the world, enabling Spotify to provide a seamless experience for our users. We’re also pleased with the continuous collaboration and deep engagement with Google’s product teams to accelerate both our businesses, ” says Gustavsson.

In this video, Peter Sobot, Senior Engineer, Personalization, Spotify talks about how Cloud Bigtable is helping Cloud Bigtable personalize recommendations, including tips about how to design a good schema, how to avoid latency when ingesting new data, and effective caching strategies to scale to tens of millions of data points per second.

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Cloud Firestore is a NoSQL document database that simplifies storing, syncing, and querying data for your mobile and web apps at global scale. Cloud Firestore is a fast, fully managed, serverless, cloud-native NoSQL document database that simplifies storing, syncing, and querying data for your mobile, web, and IoT apps at

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