Power of Eloquence

When saying “Hello World!” isn’t enough anymore

Most Common and Useful Design Patterns You Should Be Aware of as a Javascript Developer

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If you’re anything like me, after you spent considerable time chopping up codes for all types of applications, be it web or mobile app, you’ve already come across with code that shares some similar patterns as the codebase itself has grown over some significant portion of the time.

From these observations, we programmers developed our conversations on design patterns in making scalable software solutions.

In particular with JS, with the influx of JS libraries, frameworks, tools etc, we can build our applications to solve some particular problems in so many different ways. But, no matter how much tooling JS developers are going to be choosing, there’s no better substitute for incorporating useful patterns in your code design where you see fit.

Using Built-in JSON Query Tools of Relational Databases

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I’ve been doing relational databases for a long time now, especially when you’re dealing with making data-rich web applications.

In fact, every full stack developer will tell and share you their stories and trivialities of working with database intimately every day.

So what better way to work with them is to know plenty of SQL statements such as SELECT, GROUP BY, FROM, WHERE etc, which is paramount without question.

Knowing such basic skills allows you to work with disparate industry-standard relational database technologies such as MySQL, MS SQL, Oracle DB, Postgres, and many more.

But what I discovered, recently of late, there’s a new tool that has been slowly introduced to these relational database technologies crowd all the while.

For the first time, you can now create and generate JSONified results from SQL statements.

Dependency Injection - What Are They and Why Should We Care (or Not) for Software Architecture Design

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As great software engineers we’re aspired to be every day in our daily work, I’ve always used to think how we can build amazing products for our clients to use so it helps to achieve their major goals tenfold. Especially when you have the scalability forethought in mind.

Built to scale as they say in the world of startups and venture capital funding.

That product can be anything from a simple portfolio website for an artists/singer, a basic space invaders game for kids to play online, to building high-grade commercial e-commerce system for thousands, if not millions of online customers to interact and use worldwide, or perhaps build the next Facebook-scaled size social media platform!

These atypical software products we’re so used to building can vary in size. A product can do one or several simple things. Or a product that makes up so many moving parts that are, rightfully so, considered as components that do very complex jobs on its own. Thus the same product is a behemoth size project so you got think how a lone developer is going to meander through the layers of architecture ensuring that all of these components can work with each other in which they primarily function or not.

Thus it brings to my attention on this very important subject matter - using dependency injection as one of your core software design principles.

Git Commands for Maintaining Health Status of Your Code Repository System

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At some point, when working with your feature branches, you need to do some bit of ‘trimming’ with your stale branches that you no longer need to keep.

These stale branches include:

  • branches have been merged to the master branch, both locally and remotely.
  • branches that were no longer required as actual features by the client.
  • branches that were built for prototype or demo purposes.

Know Your Algorithms and Data Structures - in Any Language - Part 3

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First of all - Happy New Year of 2019 to all! 👑🎉🎊

To kick off the year with a big bang, let’s start with today’s post.

From my previous post little over ago or so, I discussed the importance of knowing data structures such as arrays, stacks, queues etc every good software developer/engineer must grasp. In this post, I will be covering topics on the other not-so-common data structures that we normally (or always, should I say) use when implementing our algorithms. They are follows:

  1. Trees
  2. Graphs
  3. Heaps
  4. Trie