| Description |
1 online resource (392) |
| Contents |
Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Big Data Analytics with Spark; Introduction; Initializing SparkContext; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Working with Spark's Python and Scala shells; How to do it ... ; How it works ... ; There's more ... ; See also; Building standalone applications; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Working with the Spark programming model; How to do it ... ; How it works ... ; There's more ... ; See also |
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Working with pair RDDsGetting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Persisting RDDs; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Loading and saving data; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Creating broadcast variables and accumulators; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Submitting applications to a cluster; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Working with DataFrames; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also |
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Working with Spark StreamingGetting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Chapter 2: Tricky Statistics with Spark; Introduction; Working with Pandas; Variable identification; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Sampling data; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Summary and descriptive statistics; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Generating frequency tables; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Installing Pandas on Linux |
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Getting readyHow to do it ... ; How it works ... ; There's more ... ; See also; Installing Pandas from source; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Using IPython with PySpark; Getting ready; How to do it ... ; How it work ... ; There's more ... ; See also; Creating Pandas DataFrames over Spark; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Splitting, slicing, sorting, filtering, and grouping DataFrames over Spark; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Implementing co-variance and correlation using Pandas; Getting ready |
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How to do it ... How it works ... ; There's more ... ; See also; Concatenating and merging operations over DataFrames; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Complex operations over DataFrames; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Sparkling Pandas; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Chapter 3: Data Analysis with Spark; Introduction; Univariate analysis; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Bivariate analysis; Getting ready; How to do it ... ; How it works ... ; There's more .. |
| Summary |
Annotation Over insightful 90 recipes to get lightning-fast analytics with Apache SparkAbout 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 dataWho This Book Is ForThis 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 DetailSpark 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 approachThis 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 |
| Notes |
Print version record |
| SUBJECT |
Spark (Electronic resource : Apache Software Foundation) http://id.loc.gov/authorities/names/no2015027445
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Spark (Electronic resource : Apache Software Foundation) fast |
| Subject |
Data mining.
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Information retrieval.
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Big data.
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Data Mining
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Information Storage and Retrieval
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information retrieval.
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Big data
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Data mining
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Information retrieval
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| Form |
Electronic book
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| ISBN |
1785288806 |
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9781785288807 |
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