For programmers, data engineers, data scientists and anyone who programs on data

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Now that we have some tools — principles and concepts — at hand to work with, it is time to begin by applying to concrete examples. It is worth noting that the purpose of these examples is only to illustrate the programming point and not their particular business content or meaning.

Note: Examples in this section are chosen from SQL because they better illustrate the idea of structural code complexity. But, it is not about showing SQL coding techniques etc. The purpose is to present general programming ideas, not tied to any one language.

In the examples we start with…


For programmers, data engineers, data scientists and anyone who programs on data

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Separation of Concerns: a Fundamental Principle

In the last part, the concept of complexity was introduced and explored to some extent. Now the question is how to deal with it. The well-known rule for this is: break it into simpler, smaller elements where each element is simple enough to be comprehended and understood easily. We all have heard this time and time again, and this is in fact the principle on which complexity is solved; this is called analysis and is the essence of problem-solving. But, how actually this is done, is not shown as many times. Anyways, for our purposes — programming and design —…


For programmers, data engineers, data scientists and anyone who programs on data

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Part I: Conceptual Framework

In this part some of the most important concepts of programming and design will be introduced which will lay a foundation and a starting point for the subject. In subsequent parts, as the topic will require more concepts and principles will be introduced.

I think instead of opening a new subject by giving a bunch of definitions, it is more useful to develop it, going through the thought process together with the reader or audience (In general one has to be very careful with definitions, for they can be useful when used properly but in many other cases they can…


For programmers, data engineers, data scientists and anyone who programs on data

Photo by Pixabay from Pexels

What is The Problem?

According to the “Data Never Sleeps 5.0” report in 2017, “90% of all data today was created in the past 2 years,” and this growth trend obviously continues at an exponential rate. As the volume is increasing, the importance of data for the functioning of organizations and business increase as well. Business decision-making and operation has become more and more data-driven. With this development, the number of people who work with data, one way or another, has grown dramatically. New disciplines such as Data Science, Machine Learning, Artificial Intelligence, applied statistics and mathematics and many more have emerged that bulk…

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