Title
Data Analytics With Hadoop: An Introduction For Data Scientists
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Ready To Use Statistical And Machinelearning Techniques Across Large Data Sets? This Practical Guide Shows You Why The Hadoop Ecosystem Is Perfect For The Job. Instead Of Deployment, Operations, Or Software Development Usually Associated With Distributed Computing, You??Ll Focus On Particular Analyses You Can Build, The Data Warehousing Techniques That Hadoop Provides, And Higher Order Data Workflows This Framework Can Produce.Data Scientists And Analysts Will Learn How To Perform A Wide Range Of Techniques, From Writing Mapreduce And Spark Applications With Python To Using Advanced Modeling And Data Management With Spark Mllib, Hive, And Hbase. You??Ll Also Learn About The Analytical Processes And Data Systems Available To Build And Empower Data Products That Can Handle??And Actually Require??Huge Amounts Of Data. Understand Core Concepts Behind Hadoop And Cluster Computing Use Design Patterns And Parallel Analytical Algorithms To Create Distributed Data Analysis Jobs Learn About Data Management, Mining, And Warehousing In A Distributed Context Using Apache Hive And Hbase Use Sqoop And Apache Flume To Ingest Data From Relational Databases Program Complex Hadoop And Spark Applications With Apache Pig And Spark Dataframes Perform Machine Learning Techniques Such As Classification, Clustering, And Collaborative Filtering With Spark??S Mllib
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This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.
- Q: What is the page count of this book? A: The book has two hundred eighty-six pages. This length provides a comprehensive overview of data analytics techniques using Hadoop.
- Q: What is the binding type of this book? A: This book is paperback bound. Paperback binding makes it lightweight and portable for readers.
- Q: What are the dimensions of this book? A: The book measures six point eight one inches in length, zero point six seven inches in width, and nine point two one inches in height. These dimensions make it easy to handle and store.
- Q: How can I use the techniques in this book? A: You can apply the techniques by following the practical guide to implement statistical and machine-learning analyses. The book includes hands-on examples for using Hadoop effectively.
- Q: Is this book suitable for beginners? A: Yes, this book is suitable for beginners. It introduces core concepts of data analytics and Hadoop, making it accessible for those new to the field.
- Q: What type of audience is this book intended for? A: The book is intended for data scientists and analysts. It provides insights into using Hadoop for large data sets and advanced analytics.
- Q: How do I keep this book in good condition? A: Store the book in a cool, dry place away from direct sunlight. This helps to preserve the binding and pages over time.
- Q: Are there any special care instructions for this book? A: No special care instructions are needed. Just avoid exposure to moisture and excessive heat.
- Q: What should I do if the book arrives damaged? A: Contact the seller for a return or replacement. Most sellers will have a policy in place for damaged items upon arrival.
- Q: What is the return policy for this book? A: The return policy may vary by seller. Typically, you can return it within a specified period if you're not satisfied.
- Q: Can I use this book for professional development? A: Yes, this book is a great resource for professional development. It covers advanced techniques that can enhance your skills in data analytics.
- Q: Is this book relevant for data management professionals? A: Yes, it is relevant for data management professionals. The book discusses data warehousing techniques and the Hadoop ecosystem.
- Q: Does this book cover practical applications of Hadoop? A: Yes, the book covers practical applications of Hadoop. It includes examples of writing MapReduce and Spark applications.
- Q: What kind of analyses can I learn from this book? A: You can learn various analyses, including classification, clustering, and collaborative filtering techniques. The book provides a comprehensive guide to these methods.
- Q: Is there a focus on machine learning in this book? A: Yes, there is significant focus on machine learning techniques. The book covers the use of Spark’s MLlib for implementing machine learning models.
- Q: Can I find information about data workflows in this book? A: Yes, the book discusses higher order data workflows. It explains how to leverage the Hadoop framework for efficient analytics.