Linux Journal Contents #27, July 1996
Linux Journal Issue #27/July 1996
Features
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IP Masquerading with Linux
by Chris Kostick
How to enable and configure IP masquerading, also known as Network Address Translation, for Linux.
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Understanding Red Hat Run Levels
by Mark F. Komarinski
How to easily add to or modify the existing subsystems of Red Hat distributions of Linux.
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Filters: Doing it Your Way
by Malcolm Murphy
A look at several of the more flexible filters, programs that read some input, perform some operation on it, and write the altered data as output.
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The New Korn Shell
by David G. Korn, Charles J. Northrup, and Jeffery Korn
ksh93, the latest major revision of the Korn Shell language, provides an alternative to Tcl and Perl.
News and Articles
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Samba in the Home and Office
by Peter Kelly
Linux makes a great server for any computer network.
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Maceater A true story; Linux pings connectivity to an office of Apple computers.
by Jonathan Gross
A true story; Linux pings connectivity to an office of Apple computers. i
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Object Databases
by Gregory A. Meinke
Not just for CAD/CAM Anymore
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Serving Two Masters
by Michael K. Johnson
Getting Linux and Windows 95 to coexist
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Basic fvwm Configuration
by John M. Fisk
Tips for using fvwm, an X-Windows manager
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Introducing HyperNews
by David Alan Black
Combining the functions of Usenet and the WWW
Columns
Directories & References
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.
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| Dynamic DNS—an Object Lesson in Problem Solving | May 21, 2013 |
| Using Salt Stack and Vagrant for Drupal Development | May 20, 2013 |
| Making Linux and Android Get Along (It's Not as Hard as It Sounds) | May 16, 2013 |
| Drupal Is a Framework: Why Everyone Needs to Understand This | May 15, 2013 |
| Home, My Backup Data Center | May 13, 2013 |
| Non-Linux FOSS: Seashore | May 10, 2013 |
- Dynamic DNS—an Object Lesson in Problem Solving
- Making Linux and Android Get Along (It's Not as Hard as It Sounds)
- Using Salt Stack and Vagrant for Drupal Development
- New Products
- A Topic for Discussion - Open Source Feature-Richness?
- Drupal Is a Framework: Why Everyone Needs to Understand This
- Validate an E-Mail Address with PHP, the Right Way
- RSS Feeds
- Readers' Choice Awards
- Tech Tip: Really Simple HTTP Server with Python
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!
Free Webinar: Hadoop
How to Build an Optimal Hadoop Cluster to Store and Maintain Unlimited Amounts of Data Using Microservers
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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