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Advanced Analytics With Spark: Patterns For Learning From Data At Scale
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In The Second Edition Of This Practical Book, Four Cloudera Data Scientists Present A Set Of Selfcontained Patterns For Performing Largescale Data Analysis With Spark. The Authors Bring Spark, Statistical Methods, And Realworld Data Sets Together To Teach You How To Approach Analytics Problems By Example. Updated For Spark 2.1, This Edition Acts As An Introduction To These Techniques And Other Best Practices In Spark Programming.Youll Start With An Introduction To Spark And Its Ecosystem, And Then Dive Into Patterns That Apply Common Techniquesincluding Classification, Clustering, Collaborative Filtering, And Anomaly Detectionto Fields Such As Genomics, Security, And Finance.If You Have An Entrylevel Understanding Of Machine Learning And Statistics, And You Program In Java, Python, Or Scala, Youll Find The Books Patterns Useful For Working On Your Own Data Applications.With This Book, You Will: Familiarize Yourself With The Spark Programming Model Become Comfortable Within The Spark Ecosystem Learn General Approaches In Data Science Examine Complete Implementations That Analyze Large Public Data Sets Discover Which Machine Learning Tools Make Sense For Particular Problems Acquire Code That Can Be Adapted To Many Uses
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- Q: What is the page count of this book? A: This book has two hundred seventy-seven pages. It provides extensive patterns for large-scale data analysis using Spark.
- Q: What are the dimensions of this book? A: The book measures seven inches in length, zero point five inches in width, and nine point one inches in height. These dimensions make it portable for readers.
- Q: What type of binding does this book have? A: The book is paperback bound. This makes it flexible and easy to handle while reading.
- Q: Who is the author of this book? A: The author of this book is Sandy Ryza. He is a data scientist with expertise in analytics and Spark.
- Q: What is the main focus of this book? A: The book focuses on large-scale data analysis patterns with Spark. It combines statistical methods and real-world datasets to teach practical approaches.
- Q: Can beginners understand this book? A: Yes, beginners with an entry-level understanding of machine learning and statistics can understand this book. It is designed to be accessible to those who program in Java, Python, or Scala.
- Q: What topics are covered in this book? A: The book covers topics like classification, clustering, collaborative filtering, and anomaly detection. These techniques are applied to fields like genomics, security, and finance.
- Q: How can I apply the patterns from this book? A: You can apply the patterns by adapting the provided code for your own data applications. The book includes complete implementations that analyze large public datasets.
- Q: Is this book suitable for someone interested in data science? A: Yes, this book is suitable for anyone interested in data science. It provides practical examples and methodologies relevant to the field.
- Q: What is the target audience for this book? A: The target audience includes data scientists, analysts, and developers interested in large-scale data analysis. It’s ideal for those familiar with programming.
- Q: Are there any prerequisites for reading this book? A: Yes, a basic understanding of machine learning and statistics is recommended. Familiarity with Java, Python, or Scala will also enhance comprehension.
- Q: How should I care for this book? A: To care for this book, store it in a dry place and avoid exposing it to extreme temperatures. This will help maintain its quality over time.
- Q: Is this book safe for children? A: Yes, the content is educational and safe for children, though it is primarily aimed at teens and adults interested in data science.
- Q: What if I find a damaged copy? A: If you receive a damaged copy, contact the retailer for a return or exchange. Most retailers have a customer service policy for such issues.
- Q: Can I return this book if I don't like it? A: Yes, most retailers allow returns within a specified period. Check the return policy for details on conditions and timeframes.