You must have backgrounds in the programming world while reading this article.
In the field of Big Data, it is not enough to be a programmer only, but there are degrees and degrees in this field, but, we will start with what is simple, to get to the simplified complexity of course, so, let's start with the important programming languages used in the Big Data:
Python: A very powerful and very popular language, Python is a very powerful language that is used in many things, whether programming office applications or programming sites and others, and learning it will benefit you a lot in the field of Big Data, especially since Python has a better future In the field of modern technologies.
SQL: Of course in the Big Data, we are dealing with databases with certainty, and SQL is one of the most famous and most powerful languages for creating and managing databases. It is true that we will deal with huge data that may have little SQL codes, but this does not mean that learning them will not benefit you, but rather It will benefit you very very much.
Scala: It is a programming language dependent on Java of course, the Scala depends on the principle of Scalability as it came in its name, that is, it depends on flexibility and analysis above all, therefore, knowing the Scala or at least taking an idea about it is not a bad idea if you want to access a world The Big Data
There are in fact many and many languages and methodologies that you must learn, besides the three languages, there is also Matlab that you must master also, we do not forget both HiveQL and Pig Latin, moreover, also learn Sas and Julia It will benefit you greatly in this matter.
In the field of Big Data - programmatically and practically - there are, of course, rankings for each of those working in this field, so it is not permissible for all IT technology programming languages to deal with these huge databases, therefore, work in the field of Big Data has been divided into the following areas, And I will try to explain it briefly because of its many terms and the enormity of its concepts:
Data Warehousing: It is the category that collects data with all its guard, but it does the filtering or isolation process only in order to isolate if there is data that does not fit and does not need to be saved, or it may be predatory or harmful data, so do not forget to Everything uploaded to the Internet is stored, and we do not forget about the category of hackers who also raise their harmful applications in the web world, therefore, such files must be filtered and not recorded, because they will not be useful, harmful, and most importantly, they are not confirmed and 100% correct, and it is one of the big data properties (you can refer to the big data properties section).
Data Collection: In this section, we rely on collecting data and saving them in the place designated for them. I mentioned to you previously that data is divided into small parts and each category is stored for example in specific tables and rules. At this stage, data is collected Taken from Data Warehousing, dividing, segmenting, and preserving it, of course filtering unhelpful or incomplete information, and often it can be filtered until only the information for a specific field is isolated, in the case of for example if I have accessed a set of data but I do not need - At that moment - only data related to the field of cars, for example, if the company he worked for is interested in this field.
Data Analysis: There is no point in accumulating data in front of you if you are not able to understand and analyze it and extract the most important part of it which is how to exploit it, therefore, a database analyst is necessary in any organization that uses the Big Data, that I analyze the data in my hands and know how to use it It is necessary if I want to provide my other customers with data stating that the red car brings 55% of visitors to our site unlike the blue car, that I analyze and interpret such data will benefit me very, very much, in fact it is the basis of the Big Data, and I guess What, this stage requires strong skill in the field of database analysis or dealing with databases in general.
Data Transformation: Some of us have passed through a set of methods, approaches and methods for analyzing, modifying and filtering data coming to us, and inferring a summary based on that data through analysis, it is time to implement the necessary changes in order for the company / product development stage to take its approach The private sector based on the analyzes, at this stage, we call it Data Transformation, simply, it is the application of all the data that was inferred on the ground, and the application of a series of analyzes in order to obtain a better return.
Author: Carolyn