---
product_id: 388784976
title: "Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark Paperback – Big Book, 13 June 2017"
brand: "russell jurney"
price: "€ 112.15"
currency: EUR
in_stock: true
reviews_count: 5
url: https://www.desertcart.sk/products/388784976-agile-data-science-2-0-building-full-stack-data-analytics
store_origin: SK
region: Slovakia
---

# Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark Paperback – Big Book, 13 June 2017

**Brand:** russell jurney
**Price:** € 112.15
**Availability:** ✅ In Stock

## Quick Answers

- **What is this?** Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark Paperback – Big Book, 13 June 2017 by russell jurney
- **How much does it cost?** € 112.15 with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [www.desertcart.sk](https://www.desertcart.sk/products/388784976-agile-data-science-2-0-building-full-stack-data-analytics)

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- russell jurney enthusiasts

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## Description

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## Customer Reviews

### ⭐⭐⭐⭐⭐ 







  
  
    clear, useful, motivational
  

*by J***E on Reviewed in the United States on 21 February 2018*

As a R language programmer, I'd like a book so clear, concise, to the task and motivational as Russell's but for R . Worth every dollar

### ⭐⭐⭐ 







  
  
    Only Buy for the Architecture Explanation and not to Follow Along
  

*by T***R on Reviewed in the United States on 9 September 2020*

I saw the good and bad reviews on this book, but considering my day to day job has me setting up similar infrastructure, I wanted to see how a pro does it. The good thing about this book is that Russell Jurney does seem to have a great end to end product solution that is similar to what we push into production. Customer Front End -> Message Broker -> Big Data ETL -> Front End for Product Managers with Insights. The only issue is that the code used to follow along is so patchy and broken (not due to the fault of the author, but due to different technologies upgrading) that actually following along with code becomes impossible.Russel gives two options to follow along in the book, AWS (which costs at MINIMUM $0.50/hour) or a Virtual Machine (which becomes difficult to manage and takes up MINIMUM 50GB on your laptop). AWS seems to be the most stable so far, but comes at a cost, literally. I don't really want to be paying to have a machine running when I am trying to figure out what went wrong or debugging. You can shut the machine off and save the state, but you are still charged for the full hour, so might as well use it. After two days I have a $5 bill and I am barely through chapter 3, and there are still out of date package issues in python (i.e. `pymongo_spark` library doesn't work because the last time it was updated was 2015) . I estimate by the time I done with the book the charges will be over $100.The VM option seems to be more stable as long as you change some of the versions in the VagrantFile bash file provided on the books github (i.e. libsvm needs to be changed from 1.0.0 to 1.1) but then this causes compatability issues which then need to be resolved by installing Vagrant plugins. Once you get past all of that, the NAT from your VM to the host machine was improperly configured so you can't forward any of the ports to do the exercises in the book.The third option (not in the book) was a Dockerfile I found on the book's github. I was like "Thank You! A modern free solution" but the file doesn't work and gets stuck when installing one of the many libraries, but it won't tell you which one or why, so that option is out...I was debating between actually giving 1 star or 3 stars, but decided for 1. I am assuming I am like most people and review the 5 Stars to see what people loved about it and 1 stars to see what people didn't like about it. I also thought that people who may not have access to some of the production versions of these technologies will be very disappointed if they never get an opportunity to use them.All in All the architecture and explanations are good, not great (get "Designing Data Intensive Applications" for a better overview), but you can use this outline to setup a pipeline if you are willing to spend a lot of money on AWS.

### ⭐⭐⭐⭐ 







  
  
    Good book for thinking about innovating data science in a more agile fashion
  

*by E***Y on Reviewed in the United States on 1 November 2017*

As a erstwhile data scientist, this reviewer doesn't agree entirely with the premise as suggested in the book that "the most effective output of the data science process suitable for effecting change in an organization is the web application. It asserts that application development is a fundamental skill of a data scientist." From my perspective, being a good data scientist means really understanding the data, and figuring out what to highlight. Sometimes an application (web based or other) may be the most effective method, but not always. And occasionally, there may be very important data security issues that need to be addressed if using the web as a medium for data transfer, display or analysis. So, this reader is not sold on the exact idea, but it provides an interesting way to consider data science, and whether there are more and better approaches that can be used. Keep in mind that people think of data as a medium with out bias, and machine learning in a similar vein; however, if data are collected in a biased fashion, or questions or slanted in such a way to capture answers in a desired fashion, the data itself is not bias free, and machine learning turns out those flaws as "facts," so there are many facets that need to be considered when using data science with only an empiric framework for exploratory data analysis.Agile methods are becoming the gold standard of approaching technologic solutions, but, as Jurney points out, may be difficult when approaching data analytics and big data. understanding the nuances and caveats of each dataset make the "agile" approach sometimes a little dangerous and difficult to create as a gold standard for data analytics vs some other methods (such as scrum) that currently exist in a software development environment. In some cases, it's also difficult to weigh the impact of "failed" experiments (data was wrong), as the onus is usually on the analyst to present the "right" answer.There's a lot of good stuff in here, though, as analysts and managers of analysts think about how to transform the work they do. With more and mroe data available, in structured and more unstructured formats, how do you iterate and improve upon the data understanding? Consider the importance of the visuals. Move out of the framework of only relational tables. This book is relatively up-to-date with the most commonly used tools (Spark instead of Mapreduce); examples of how to use Spark in Python (load PySpark), MongoDB, Elasticsearch, etc. There are good code samples and a very well indexed appendix.Overall, some interesting concepts that a data team will enjoy discussing, even if they don't agree with all of Jurney's points of view.

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*Last updated: 2026-03-17*