We have been working hard at Databricks to make our product more user-friendly for developers. Recently, we have added two new features that...

We introduced DataFrames in Apache Spark 1.3 to make Apache Spark much easier to use. Inspired by data frames in R and Python...

With an ever-growing community, Apache Spark has had it’s 1.1 release . MLlib has had its fair share of contributions and now supports...

One of our philosophies in Apache Spark is to provide rich and friendly built-in libraries so that users can easily assemble data pipelines. With Spark, and MLlib in particular, quickly gaining traction among data scientists and machine learning practitioners, we’re observing a growing demand for data analysis support outside of model fitting. To address this need, we have started to add scalable implementations of common statistical functions to facilitate v

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