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Apache Spark 1.1: Bringing Hadoop Input/Output Formats to PySpark

September 17, 2014 by Nick Pentreath and Kan Zhang in Engineering Blog
This is a guest post by Nick Pentreath of Graphflow and Kan Zhang of IBM , who contributed Python input/output format support to Apache Spark 1.1. Two powerful features of Apache Spark include its native APIs provided in Scala, Java and Python, and its compatibility with any Hadoop-based input or output source. This language support means that users can quickly become proficient in the use of Spark even without experience in Scala, and furthermore can leverag

Announcing Apache Spark 1.1

September 11, 2014 by Patrick Wendell in Engineering Blog
Today we’re thrilled to announce the release of Apache Spark 1.1! Apache Spark 1.1 introduces many new features along with scale and stability improvements. This post will introduce some key features of Apache Spark 1.1 and provide context on the priorities of Spark for this and the next release.

Statistics Functionality in Apache Spark 1.1

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

Mining Ecommerce Graph Data with Apache Spark at Alibaba Taobao

August 14, 2014 by Andy Huang and Wei Wu in Engineering Blog
This is a guest blog post from our friends at Alibaba Taobao. Alibaba Taobao operates one of the world’s largest e-commerce platforms. We collect hundreds of petabytes of data on this platform and use Apache Spark to analyze these enormous amounts of data. Alibaba Taobao probably runs some of the largest Spark jobs in the world. For example, some Spark jobs run for weeks to perform feature extraction on petabytes of image data. In this blog post, we share our

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