What is the “Big Data”?

“What is the Big Data?” When we ask it will come across many definitions, many many data” and many things. I thought I’d better write a little bit about it.


Note: I usually will use some abbreviated words below:

  • Machine Learning — ML

Lets talk about the BD:

“What is the BD?” When we ask to Google then we will come across many definitions, many “many data” and many things.

I agree with a friend’s comment and share the same quote with you:

“I’ve been dealing with BD for years, but I still don’t understand what happened.”

According to the general opinion, BD is perceived in two different ways. The first is the direction that technical people look at, and the second is the direction of the popular culture or marketing world.

First, technically: BD is a problem.

  • What kind of problem is it?

Data that cannot be processed with traditional methods is called BD.

  • So if this is a problem, what is the solution to it?

The solution to this problem is that multiple computers come together and act like one. In other words, multiple computers act as a single computer (server) to do a job.

This co-operation is carried out in physical and software terms. Physically, more than one computer comes together to form a “cluster”. The “map reduce” programming model in terms of software realizes the work of the cluster acting together, which is formed by the physical combination of computers. And so this problem is solved.

  • So what has solved this problem for us?

It opened new horizons in the field of data analytics, and the performance of ML algorithms increased with the increase of computing power. With the use of larger amounts and types of data, a very important resource / tool has been provided for the process of extracting useful information from data.

The second is the way it is perceived by the marketing world: The combination of data from different types and different sources to make mysterious analyzes and solutions to save the world.

  • Both ways of perception are true. Except for thinking that solutions to save the world come with BD. The solutions that will save this world are all about the performance of a data analyst or data scientist with a lean analytical perspective. When a company brings together how many Zeta Byte data from 10 different data sources, the big problems of our companies are not solved. This shows that the structural part of BD has been removed and the way has been opened for the transition to BD analytics circuit.

BD; data richness and computational performance. But, as always, one thing that should not be forgotten is that the subject comes to the processes of extracting useful information from data.

One day we will not be discussing the existence of basic needs such as electricity and water, and we will not be discussing the size of the data and the difficulties of processing this data, in this case the issue will come back to the issue of extracting useful, action-based information from the data.

  • So what is our final perspective and interpretation about this BD?

As a result, BD is a great blessing for the future! BD is an enormous wealth for data-oriented employees, the ground for new discoveries and a vision that changes our perspective on the world from the data perspective. And the importance of this will become even better understood in the not too distant future. Maybe it’s understood who knows …

This is all I have written about the “What is the Big Data?”. If you want to know more about DS and related others, you can check out my other serial articles. Sample:
Roadmap to Become a “Data Scientist”

You can reach me from my Linkedin account for all your questions and requests.

Hope to meet you in other series articles and articles…🖖🏼

1. https://restart-project.eu/big-data-matters/
2. https://www.tcgdigital.com/big-data-advanced-analytics/
3. https://www.smartdatacollective.com/big-data-20-free-big-data-sources-everyone-should-know/
3. https://www.veribilimiokulu.com/blog/buyuk-veri-nedir/
4. https://www.scnsoft.com/blog/iot-big-data-nature

Experienced Ph.D. with a demonstrated history of working in the higher education industry. Skilled in Data Science, AI, Deep Learning, Big Data, & Mathematics.