Apache Spark for Data Science Cookbook - Brossura

Chitturi, Padma Priya

Brossura

ISBN 10: 1785880101 ISBN 13: 9781785880100

Casa editrice: Packt Publishing, 2016

Riassunto

Over insightful 90 recipes to get lightning-fast analytics with Apache Spark About This Book * Use Apache Spark for data processing with these hands-on recipes * Implement end-to-end, large-scale data analysis better than ever before * Work with powerful libraries such as MLLib, SciPy, NumPy, and Pandas to gain insights from your data Who This Book Is For This book is for novice and intermediate level data science professionals and data analysts who want to solve data science problems with a distributed computing framework. Basic experience with data science implementation tasks is expected. Data science professionals looking to skill up and gain an edge in the field will find this book helpful. What You Will Learn * Explore the topics of data mining, text mining, Natural Language Processing, information retrieval, and machine learning. * Solve real-world analytical problems with large data sets. * Address data science challenges with analytical tools on a distributed system like Spark (apt for iterative algorithms), which offers in-memory processing and more flexibility for data analysis at scale. * Get hands-on experience with algorithms like Classification, regression, and recommendation on real datasets using Spark MLLib package. * Learn about numerical and scientific computing using NumPy and SciPy on Spark. * Use Predictive Model Markup Language (PMML) in Spark for statistical data mining models. In Detail Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark's selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease. This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark's data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work. Style and approach This book contains a comprehensive range of recipes designed to help you learn the fundamentals and tackle the difficulties of data science. This book outlines practical steps to produce powerful insights into Big Data through a recipe-based approach.

Su questo libro

L'autore

Padma Priya Chitturi is a Senior Research and Development Engineer at Amadeus Software Labs (India) Pvt Ltd and has over 4 years of experience in Big Data processing. Currently, she is working for an Airlines product on a real-time processing platform serving one million user requests/sec at Amadeus. She has worked on realizing large-scale deep networks (Jeffrey dean's work in Google brain) for image classification on the big data platform Spark. She works closely with Big Data technologies such as Spark, Storm Cassandra, and Hadoop. She is an open source contributor to Apache Storm and you can find her name in the Storm community.

She has also authored technical and research articles.

Dettagli dell?edizione

• Editore: Packt Publishing
• Data di pubblicazione: 2016
• ISBN 10: 1785880101
• ISBN 13: 9781785880100
• Rilegatura: Copertina flessibile
• Lingua: Inglese
• Numero di pagine: 392