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Introducing Predictive Optimization: Faster Queries, Cheaper Storage, No Sweat

Predictive Optimization intelligently optimizes your Lakehouse table data layouts for peak performance and cost-efficiency - without you needing to lift a finger.

Scalable Collaborative Filtering with Apache Spark MLlib

July 23, 2014 by Burak Yavuz and Reynold Xin in Engineering Blog
Recommendation systems are among the most popular applications of machine learning. The idea is to predict whether a customer would like a certain item: a product, a movie, or a song. Scale is a key concern for recommendation systems, since computational complexity increases with the size of a company's customer base. In this blog post, we discuss how Apache Spark MLlib enables building recommendation models from billions of records in just a few lines of Pyt