13 Data, Databases and Engineering

13.1 DevOps for Data Science

by Alex K Gold

In this book, you’ll learn about DevOps conventions, tools, and practices that can be useful to you as a data scientist. You’ll also learn how to work better with the IT/Admin team at your organization, and even how to do a little server administration of your own if you’re pressed into service.

Link: https://do4ds.com/

13.2 Exploring Enterprise Databases with R: A Tidyverse Approach

by John David Smith, Sophie Yang, M. Edward (Ed) Borasky, Jim Tyhurst, Scott Came, Mary Anne Thygesen

Great resource for moving from a standard R developer to incorporating R workflows into enterprise-grade technologies using Docker and Databases.

Link: https://smithjd.github.io/sql-pet/

13.3 R for Data Engineers

by Greg Wilson

Years ago, Patrick Burns wrote The R Inferno, a guide to R for those who think they are in hell. Upon first encountering the language after two decades of using Python, I thought Burns was an optimist—after all, hell has rules.

I have since realized that R does too, and that they are no more confusing or contradictory than those of other programming languages. They only appear so because R draws on a tradition unfamiliar to those of us raised with derivatives of C. Counting from one, copying data rather than modifying it, lazy evaluation: to quote the other bard, these are not mad, just differently sane.

Welcome, then, to a universe where the strange will become familiar, and everything familiar, strange. Welcome, thrice welcome, to R.

Link: https://tidynomicon.github.io/tidynomicon/

13.4 Reproducible Analytical Pipelines (RAP) Companion

by Matthew Gregory, Matthew Upson

Reproducible Analytical Pipelines require a range of tools and techniques to implement that can be a challenge to overcome, and this book address some of the common knowledge gaps and hard-to-Google problems that upcoming RAP-pers face.

Link: https://ukgovdatascience.github.io/rap_companion/

 

Created and maintained by Oscar Baruffa

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