{"id":319,"date":"2017-12-29T14:32:10","date_gmt":"2017-12-29T14:32:10","guid":{"rendered":"https:\/\/bootstrap-it.com\/blog\/?p=319"},"modified":"2017-12-29T14:32:10","modified_gmt":"2017-12-29T14:32:10","slug":"beating-the-technology-learning-curve-monster","status":"publish","type":"post","link":"https:\/\/bootstrap-it.com\/blog\/?p=319","title":{"rendered":"Beating the technology learning curve monster"},"content":{"rendered":"
<\/a><\/a><\/a><\/a><\/a><\/div>

Doing what I do for a living, which these days mostly involves creating technology books and courseware, I\u2019m constantly learning new technologies. In a way, my new tech adventures are not much different than the ones most IT pros face, except that mine probably hit more often. Because there\u2019s so much depending on my understanding the new platform or process\u200a\u2014\u200aand so many other newer platforms and processes waiting for my attention once I\u2019m done with this one\u200a\u2014\u200amy primary goal is to get in and out as fast as possible.<\/p>\n

Trying to organize the layers of complexity and design metaphors inherent in a technology while struggling to figure out if it\u2019ll even do exactly what I want can sometimes be downright intimidating. Without a good plan of attack, I\u2019m dead in the water.<\/p>\n

Before I share a couple of the tools that I\u2019ve used successfully in my own learning, it\u2019s worth discussing a real-world (relatively) new technology.<\/p>\n

Last April, the operations management monitoring company ScienceLogic\u00a0conducted a survey<\/a>\u00a0of more than a thousand enterprise and IT professionals, seeking their thoughts on cloud adoption. While it\u2019s clear that more and more of the IT workload is shifting to cloud providers (with Amazon Web Services leading the way by every metric), there\u2019s deep and widespread concern over what the impact of the change might be.<\/p>\n

31% of respondents felt they lacked the skills to confidently lead a cloud deployment, 50% claimed they lacked the tools to properly manage infrastructure in the cloud, and 28% worried that the shift to the cloud could endanger their current jobs.<\/p>\n

Sound familiar? The faster and more disruptive the change, the more we all worry about how\u200a\u2014\u200aor if\u200a\u2014\u200awe\u2019ll keep up. And if you think Joe IT Professional stays up nights wondering how he\u2019ll figure it all out, show some sympathy for his manager who\u2019s responsible for dragging an entire department into the cloud.<\/p>\n

AWS in particular is on my mind right now because my Manning book \u201cLearn Amazon Web Services in a Month of Lunches<\/a>\u201d just came out. AWS was a technology I first struggled to understand more than a decade ago when I needed to build my own public-facing web conferencing system (using BigBlueButton). It was a long journey between that first EC2 server and the comfort level and deep experience I reached before attempting the book, but the things I got right\u200a\u2014\u200aand wrong\u200a\u2014\u200awhile taking those first steps went a long way to shape how I now teach IT topics.<\/p>\n

Sequential vs scan-and-run<\/h4>\n

How<\/em>\u00a0you\u2019ll learn a skill partly depends on what you\u2019re planning to learn. If you\u2019re looking for an introduction that will take you from zero to functional on a full-stack, multi-tier environment like AWS, then you might be safer going sequential. Starting at the beginning can help you avoid missing critical details\u200a\u2014\u200alike the way billing or security work on AWS. Trust me: if you don\u2019t like the idea of surprise four-digit monthly service charges or compromised infrastructure, then you don\u2019t want to skip the billing and security basics.<\/p>\n

But if it\u2019s a standalone software package (like a\u00a0virtualization technology<\/a>\u00a0or a new IDE) that you can safely test a few times in your own network before actually deploying for real, then fasterer is gooderer. For this kind of project I will often fire up a clean Linux container using LXC (which, for exploring new software, I find far preferable to Docker) or, when the application I\u2019m working with requires host kernel access (like SELinux), a\u00a0Linux virtual machine on Virtual Box<\/a>.<\/p>\n

By \u201cscan-and-run\u201d I mean carefully crafting a search string in DuckDuckGo.com (or one of those other search engines whose names escape me at the moment), quickly picking out the information you want from the results, and trying it on your disposable virtual server. Didn\u2019t work? Congrats. You\u2019ve just learned something you didn\u2019t yet know.<\/p>\n

Just make sure to properly document both your failures and successes so you don\u2019t have to walk around the same park over and over again.<\/p>\n

The command\u00a0line<\/h4>\n

Given a choice, I generally prefer working from the Bash command line over GUI consoles. It\u2019s not because I\u2019m a command line snob (although I am), but I find the unambiguous and trackable nature of a CLI works well with iterative experiments. In plain language, that means it\u2019s easier to retrace my steps to figure out exactly what worked and what didn\u2019t. And don\u2019t forget that Bash error messages can easily be recycled into terrific internet searches.<\/p>\n

Another CLI advantage: the predictable patterns of a well-designed shell environment can make it even easier to anticipate functionality than a well-designed web GUI. Let me illustrate that using the AWS CLI. Once it\u2019s installed and properly configured, even running `aws` without any arguments will get you something useful. Notice how the output guides you to add `help` to any partially completed command to return contextual assistance.<\/p>\n

$ aws
\nusage: aws [options] <command> <subcommand> [<subcommand>\u00a0\u2026] [parameters]
\nTo see help text, you can run:<\/p><\/blockquote>\n

aws help
\naws <command> help
\naws <command> <subcommand> help
\naws: error: too few arguments<\/p><\/blockquote>\n

It\u2019s true that there\u2019s plenty of help available through the GUI: all AWS console pages have links to extensive documentation resources. But that would be AWS documentation which, while well written and carefully maintained, is usually very, very wordy and sometimes a bit confusing. The inline CLI docs are much more focused and get you in and out more quickly.<\/p>\n

Just being familiar with how to access this kind of information can make you much faster and more effective\u2026even through the early learning stages. And the basic structure is available far beyond just AWS.<\/p>\n

\n
\n
<\/canvas><\/div>\n<\/div>
Manning\u2019s\u00a0Learn Amazon Web Services in a Month of\u00a0Lunches<\/a><\/figcaption><\/figure>\n

On the other hand, as I wrote in my book, I feel that the AWS browser console is actually a better place to first come to grips with the workings of Amazon\u2019s cloud. That\u2019s because high-level structure plays such a large role in understanding the way the dozens and dozens of AWS services work together, and the website does such a good job visualizing it. But maybe that\u2019s just me.<\/p>\n

\n
\n
<\/canvas><\/div>\n<\/div>
Official AWS documentation\u200a\u2014\u200acomplete with helpful illustrations<\/figcaption><\/figure>\n

Do it\u00a0yourself<\/h4>\n

More than anything else, your learning will be the most effective if you roll up your sleeves and try it out. Not only should you duplicate the documentation examples, but change the parameters around to see what breaks. And then plan and execute your own projects based on the technologies you\u2019re learning. Applying your knowledge to the real world is critical.<\/p>\n","protected":false},"excerpt":{"rendered":"

Doing what I do for a living, which these days mostly involves creating technology books and courseware, I\u2019m constantly learning new technologies. 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