Data Analytics With Hadoop by Benjamin Bengfort & Jenny Kim

This book is a comprehensive guide for data scientists and analysts looking to leverage the Hadoop ecosystem for large-scale data analysis. It delves into various techniques, from writing MapReduce and Spark applications with Python to advanced modeling and data management with Spark MLlib, Hive, and HBase. The book emphasizes the analytical processes and data systems that can handle and require huge amounts of data, making it an essential resource for those looking to build and empower data products. /n/n The book covers a wide range of topics, including data management, mining, and warehousing in a distributed context, as well as data ingestion from relational databases using Sqoop and Apache Flume. It also provides insights into programming complex Hadoop and Spark applications with Apache Pig and Spark DataFrames. Additionally, it explores machine learning techniques such as classification, clustering, and collaborative filtering with Spark’s MLlib.

Reviews

Goodreads · A Goodreads User · 2022-05-19

brilliant 4.00

The book provides a comprehensive introduction to data analytics with Hadoop, covering a range of techniques from MapReduce to Spark applications. The practical approach makes it a valuable resource for data scientists and analysts.

This book is a fantastic resource for anyone looking to get started with data analytics using Hadoop. The authors do a great job of breaking down complex concepts into understandable pieces, making it accessible even for those who are new to the field. The practical examples and hands-on exercises are particularly useful, allowing readers to apply what they've learned immediately. The book covers a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced statistical and machine-learning techniques. It's clear that the authors have a deep understanding of the subject matter and are able to convey that knowledge effectively. However, some readers might find the pace a bit slow if they already have a background in data analytics. Overall, it's a well-rounded introduction that provides a solid foundation for further exploration.

Quick quotes

A fantastic resource for anyone looking to get started with data analytics using Hadoop.

The practical examples and hands-on exercises are particularly useful.

The book covers a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced statistical and machine-learning techniques.

Indigo · An Indigo User · 2022-05-19

good 3.00

The book provides a good overview of big data concepts but requires time for debugging. It's not as comprehensive as expected.

This book offers a solid introduction to big data concepts and the Hadoop ecosystem. The authors provide a good overview of the tools and techniques available, making it a useful resource for those new to the field. However, readers might find that it requires a bit more time and effort to debug and implement the examples provided. The book is not as comprehensive as some might expect, and it lacks depth in certain areas. Despite these shortcomings, it's still a valuable resource for anyone looking to get started with data analytics using Hadoop. The practical examples and hands-on exercises are particularly helpful, allowing readers to apply what they've learned in a real-world context. Overall, it's a good starting point, but more experienced data analysts might find it a bit basic.

Quick quotes

The book provides a good overview of big data concepts and the Hadoop ecosystem.

It's not as comprehensive as some might expect, and it lacks depth in certain areas.

The practical examples and hands-on exercises are particularly helpful.

LoveReading · 2021-04-06

great 4.50

This practical guide is ideal for those ready to use statistical and machine-learning techniques across large data sets. The Hadoop ecosystem is highlighted as a perfect tool for the job.

If you're looking to dive into data analytics with Hadoop, this book is a great place to start. It's packed with practical advice and real-world examples that make the concepts come to life. The authors do an excellent job of explaining why the Hadoop ecosystem is so well-suited for large-scale data analysis. They cover a variety of techniques, including statistical analysis and machine learning, and provide clear, step-by-step instructions for implementing them. The book is well-organized and easy to follow, making it accessible to both beginners and more experienced data analysts. The only downside is that it might be a bit too technical for complete beginners, but for anyone with some background in data science, it's an invaluable resource.

Quick quotes

This practical guide is ideal for those ready to use statistical and machine-learning techniques across large data sets.

The Hadoop ecosystem is highlighted as a perfect tool for the job.

The authors do an excellent job of explaining why the Hadoop ecosystem is so well-suited for large-scale data analysis.

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