DevOps Training Classes in Annapolis, Maryland
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8 December, 2025 - 12 December, 2025 - ASP.NET Core MVC (VS2022)
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Blog Entries publications that: entertain, make you think, offer insight
For those newly moving into the realm of programming, the important question to mull over is what programming language or technology to specialize in. On the other hand, those who are already working as a software developer, the constant worry remains whether their current technology would become obsolete very soon.
Both these concerns could be easily addressed by checking the list of programming languages that are highly in demand and formulating the career by modifying your specialization accordingly. The supply for the developers have not met with the demand in these programming languages yet, making them most viable options for career.
Popular Programming Languages Based on TIOBE Index
The top 10 list of programming languages which are highly in demand in 2014 is listed below in the order of popularity. These languages are identified from the TIOBE Programming Community index which consists of 20 top programming languages. TIOBE index is an indicator of the popularity of programming languages and is updated once in every month. This index is calculated using multiple search engines, and the ratings are based on the number of skilled engineers world-wide, courses and third party vendors.
I’ve been a technical recruiter for several years, let’s just say a long time. I’ll never forget how my first deal went bad and the lesson I learned from that experience. I was new to recruiting but had been a very good sales person in my previous position. I was about to place my first contractor on an assignment. I thought everything was fine. I nurtured and guided my candidate through the interview process with constant communication throughout. The candidate was very responsive throughout the process. From my initial contact with him, to the phone interview all went well and now he was completing his onsite interview with the hiring manager.
Shortly thereafter, I received the call from the hiring manager that my candidate was the chosen one for the contract position, I was thrilled. All my hard work had paid off. I was going to be a success at this new game! The entire office was thrilled for me, including my co-workers and my bosses. I made a good win-win deal. It was good pay for my candidate and a good margin for my recruiting firm. Everyone was happy.
I left a voicemail message for my candidate so I could deliver the good news. He had agreed to call me immediately after the interview so I could get his assessment of how well it went. Although, I heard from the hiring manager, there was no word from him. While waiting for his call back, I received a call from a Mercedes dealership to verify his employment for a car he was trying to lease. Technically he wasn’t working for us as he had not signed the contract yet…. nor, had he discussed this topic with me. I told the Mercedes office that I would get back to them. Still not having heard back from the candidate, I left him another message and mentioned the call I just received. Eventually he called back. He wanted more money.
I told him that would be impossible as he and I had previously agreed on his hourly rate and it was fine with him. I asked him what had changed since that agreement. He said he made had made much more money in doing the same thing when he lived in California. I reminded him this is a less costly marketplace than where he was living in California. I told him if he signed the deal I would be able to call the car dealership back and confirm that he was employed with us. He agreed to sign the deal.
The original article was posted by Michael Veksler on Quora
A very well known fact is that code is written once, but it is read many times. This means that a good developer, in any language, writes understandable code. Writing understandable code is not always easy, and takes practice. The difficult part, is that you read what you have just written and it makes perfect sense to you, but a year later you curse the idiot who wrote that code, without realizing it was you.
The best way to learn how to write readable code, is to collaborate with others. Other people will spot badly written code, faster than the author. There are plenty of open source projects, which you can start working on and learn from more experienced programmers.
Readability is a tricky thing, and involves several aspects:
- Never surprise the reader of your code, even if it will be you a year from now. For example, don’t call a function max() when sometimes it returns the minimum().
- Be consistent, and use the same conventions throughout your code. Not only the same naming conventions, and the same indentation, but also the same semantics. If, for example, most of your functions return a negative value for failure and a positive for success, then avoid writing functions that return false on failure.
- Write short functions, so that they fit your screen. I hate strict rules, since there are always exceptions, but from my experience you can almost always write functions short enough to fit your screen. Throughout my carrier I had only a few cases when writing short function was either impossible, or resulted in much worse code.
- Use descriptive names, unless this is one of those standard names, such as i or it in a loop. Don’t make the name too long, on one hand, but don’t make it cryptic on the other.
- Define function names by what they do, not by what they are used for or how they are implemented. If you name functions by what they do, then code will be much more readable, and much more reusable.
- Avoid global state as much as you can. Global variables, and sometimes attributes in an object, are difficult to reason about. It is difficult to understand why such global state changes, when it does, and requires a lot of debugging.
- As Donald Knuth wrote in one of his papers: “Early optimization is the root of all evil”. Meaning, write for readability first, optimize later.
- The opposite of the previous rule: if you have an alternative which has similar readability, but lower complexity, use it. Also, if you have a polynomial alternative to your exponential algorithm (when N > 10), you should use that.
Use standard library whenever it makes your code shorter; don’t implement everything yourself. External libraries are more problematic, and are both good and bad. With external libraries, such as boost, you can save a lot of work. You should really learn boost, with the added benefit that the c++ standard gets more and more form boost. The negative with boost is that it changes over time, and code that works today may break tomorrow. Also, if you try to combine a third-party library, which uses a specific version of boost, it may break with your current version of boost. This does not happen often, but it may.
Don’t blindly use C++ standard library without understanding what it does - learn it. You look at documentation at it tells you that its complexity is O(1), amortized. What does that mean? How does it work? What are benefits and what are the costs? Same with std::vector::push_back(), and with std::map. Knowing the difference between these two maps, you’d know when to use each one of them.std::unordered_map
Never call or new directly, use delete and [cost c++]std::make_shared[/code] instead. Try to implement std::make_unique yourself, in order to understand what they actually do. People do dumb things with these types, since they don’t understand what these pointers are.usique_ptr, shared_ptr, weak_ptr
Every time you look at a new class or function, in boost or in std, ask yourself “why is it done this way and not another?”. It will help you understand trade-offs in software development, and will help you use the right tool for your job. Don’t be afraid to peek into the source of boost and the std, and try to understand how it works. It will not be easy, at first, but you will learn a lot.
Know what complexity is, and how to calculate it. Avoid exponential and cubic complexity, unless you know your N is very low, and will always stay low.
Learn data-structures and algorithms, and know them. Many people think that it is simply a wasted time, since all data-structures are implemented in standard libraries, but this is not as simple as that. By understanding data-structures, you’d find it easier to pick the right library. Also, believe it or now, after 25 years since I learned data-structures, I still use this knowledge. Half a year ago I had to implemented a hash table, since I needed fast serialization capability which the available libraries did not provide. Now I am writing some sort of interval-btree, since using std::map, for the same purpose, turned up to be very very slow, and the performance bottleneck of my code.
Notice that you can’t just find interval-btree on Wikipedia, or stack-overflow. The closest thing you can find is Interval tree, but it has some performance drawbacks. So how can you implement an interval-btree, unless you know what a btree is and what an interval-tree is? I strongly suggest, again, that you learn and remember data-structures.
These are the most important things, which will make you a better programmer. The other things will follow.
Let’s face it, fad or not, companies are starting to ask themselves how they could possibly use machine learning and AI technologies in their organization. Many are being lured by the promise of profits by discovering winning patterns with algorithms that will enable solid predictions… The reality is that most technology and business professionals do not have sufficient understanding of how machine learning works and where it can be applied. For a lot of firms, the focus still tends to be on small-scale changes instead of focusing on what really matters…tackling their approach to machine learning.
In the recent Wall Street Journal article, Machine Learning at Scale Remains Elusive for Many Firms, Steven Norton captures interesting comments from the industry’s data science experts. In the article, he quotes panelists from the MIT Digital Economy Conference in NYC, on businesses current practices with AI and machine learning. All agree on the fact that, for all the talk of Machine Learning and AI’s potential in the enterprise, many firms aren’t yet equipped to take advantage of it fully.
Panelist, Michael Chui, partner at McKinsey Global Institute states that “If a company just mechanically says OK, I’ll automate this little activity here and this little activity there, rather than re-thinking the entire process and how it can be enabled by technology, they usually get very little value out of it. “Few companies have deployed these technologies in a core business process or at scale.”
Panelist, Hilary Mason, general manager at Cloudera Inc., had this to say, “With very few exceptions, every company we work with wants to start with a cost-savings application of automation.” “Most organizations are not set up to do this well.”
Tech Life in Maryland
| Company Name | City | Industry | Secondary Industry |
|---|---|---|---|
| McCormick and Company, Incorporated | Sparks | Wholesale and Distribution | Grocery and Food Wholesalers |
| USEC Inc. | Bethesda | Manufacturing | Manufacturing Other |
| Coventry Health Care, Inc. | Bethesda | Healthcare, Pharmaceuticals and Biotech | Healthcare, Pharmaceuticals, and Biotech Other |
| Host Hotels and Resorts, Inc. | Bethesda | Travel, Recreation and Leisure | Hotels, Motels and Lodging |
| W.R. Grace and Co. | Columbia | Agriculture and Mining | Farming and Ranching |
| Discovery Communications, Inc. | Silver Spring | Media and Entertainment | Radio and Television Broadcasting |
| Legg Mason, Inc. | Baltimore | Financial Services | Financial Services Other |
| Marriott International Inc. | Bethesda | Travel, Recreation and Leisure | Hotels, Motels and Lodging |
| Constellation Energy Resources, LLC | Baltimore | Energy and Utilities | Gas and Electric Utilities |
| Lockheed Martin Corporation | Bethesda | Manufacturing | Aerospace and Defense |
| T. Rowe Price | Baltimore | Financial Services | Investment Banking and Venture Capital |
training details locations, tags and why hsg
The Hartmann Software Group understands these issues and addresses them and others during any training engagement. Although no IT educational institution can guarantee career or application development success, HSG can get you closer to your goals at a far faster rate than self paced learning and, arguably, than the competition. Here are the reasons why we are so successful at teaching:
- Learn from the experts.
- We have provided software development and other IT related training to many major corporations in Maryland since 2002.
- Our educators have years of consulting and training experience; moreover, we require each trainer to have cross-discipline expertise i.e. be Java and .NET experts so that you get a broad understanding of how industry wide experts work and think.
- Discover tips and tricks about DevOps programming
- Get your questions answered by easy to follow, organized DevOps experts
- Get up to speed with vital DevOps programming tools
- Save on travel expenses by learning right from your desk or home office. Enroll in an online instructor led class. Nearly all of our classes are offered in this way.
- Prepare to hit the ground running for a new job or a new position
- See the big picture and have the instructor fill in the gaps
- We teach with sophisticated learning tools and provide excellent supporting course material
- Books and course material are provided in advance
- Get a book of your choice from the HSG Store as a gift from us when you register for a class
- Gain a lot of practical skills in a short amount of time
- We teach what we know…software
- We care…





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