Libros UCLV { BETA }

Advanced Analytics with Spark

In this practical book, four Cloudera data scientists present a set of self- contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—classification, collaborative filtering, and anomaly detection, among others—to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find these patterns useful for working on your own data applications.

Fast Data Processing with Spark

Apache Spark has captured the imagination of the analytics and big data developers, and rightfully so. In a nutshell, Spark enables distributed computing on a large scale in the lab or in production. Till now, the pipeline collect-store-transform was distinct from the Data Science pipeline reason-model, which was again distinct from the deployment of the analytics and machine learning models. Now, with Spark and technologies, such as Kafka, we can seamlessly span the data management and data science pipelines. We can build data science models on larger datasets, requiring not just sample data. However, whatever models we build can be deployed into production (with added work from engineering on the "ilities", of course). It is our hope that this book would enable an engineer to get familiar with the fundamentals of the Spark platform as well as provide hands-on experience on some of the advanced capabilities.