February Mini Book Reviews
Over the past month, I've read four books that I wanted to review for you: Learning Perl Objects, References & Modules, Text Processing in Python, Core PHP Programming, 3rd Ed. and MySQL 2nd Ed.. All of them are good books for the audience each is trying to hit. Take a look below for some more information.
I tend to review books that are interesting to me (no surprise there), but if you'd like me to review a book, let me know at pate-reviews@eylerfamily.org, and I'll do my best to put it in my reading pile.
In addition to a short review of each book, I'm also rating them on a scale of 1 to 10. Tens represent something pretty close to life changing, so don't look for them too often.
Many years ago, I found a copy of Randal Schwartz's Learning Perl, which I really loved. I've suggested it to a number of Perl neophytes since then. I even bought a copy for my daughter last year so she could get a feel for what Perl can do. I'd always wanted a follow up that was more approachable, and now Randal and Tom have provided exactly the book I was looking for.
Learning Perl Objects, References & Modules is a fairly small book, 179 pages discounting exercise answers and appendix, but it packs a solid amount of information into that small space. The book is laid out to support a Perl training class, and permission (and advice) is given for instructors who want to use it as a text book. It works perfectly well for self-guided learning though, so don't worry about picking it up for yourself.
My only nit was that some of the OO related bits are familiar to avid readers of Randal's work. Other than that, I really enjoyed the book. The chapter on Essential Testing probably is my favorite, although a number of good things are scattered throughout. I'm giving this book a solid 9 stars--and I'm giving a copy of it to my daughter as soon as she finishes Learning Perl.
Upon picking up and leafing through Text Processing in Python, I immediately took a liking to it. David skips over a lot of the extraneous clutter that tends to fill books. His preface (chapter 0) has a meatiness missing from most others. While not huge, 416 pages in all, the book presents a fairly dense chunk of information.
Although David manages to convey both the philosophy of text processing and the use of Python quite well, it sometimes felt as though I was reading a Python reference with some of text munging information tossed in. Another downside to David's presentation is his typography is quite different from what you'll see in most other books. The book is easily readable though, so neither of these should present a big problem.
Although the book is quite good overall, I think I learned the most from Chapter 4, "Parsers and State Machines". I'll keep this book handy for a while, and try to absorb some of its lessons as I go back to it with specific problems to solve. Text Processing in Python gets 8 stars.
-- -pate http://on-ruby.blogspot.com
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Enter to Win an Adafruit Pi Cobbler Breakout Kit for Raspberry Pi

It's Raspberry Pi month at Linux Journal. Each week in May, Adafruit will be giving away a Pi-related prize to a lucky, randomly drawn LJ reader. Winners will be announced weekly.
Fill out the fields below to enter to win this week's prize-- a Pi Cobbler Breakout Kit for Raspberry Pi.
Congratulations to our winners so far:
- 5-8-13, Pi Starter Pack: Jack Davis
- 5-15-13, Pi Model B 512MB RAM: Patrick Dunn
- 5-21-13, Prototyping Pi Plate Kit: Philip Kirby
- Next winner announced on 5-27-13!
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Realizing the promise of Apache® Hadoop® requires the effective deployment of compute, memory, storage and networking to achieve optimal results. With its flexibility and multitude of options, it is easy to over or under provision the server infrastructure, resulting in poor performance and high TCO. Join us for an in depth, technical discussion with industry experts from leading Hadoop and server companies who will provide insights into the key considerations for designing and deploying an optimal Hadoop cluster.
Some of key questions to be discussed are:
- What is the “typical” Hadoop cluster and what should be installed on the different machine types?
- Why should you consider the typical workload patterns when making your hardware decisions?
- Are all microservers created equal for Hadoop deployments?
- How do I plan for expansion if I require more compute, memory, storage or networking?



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