Updated Guide to Learning Big Data from scratch

This time I come to talk about Big Data analysis, I will try to explain it in a fun and enjoyable way, taking into account that data analysis is a very complicated, arid and technical subject. A very different subject from the one I talked about last week, which was related to Digital Marketing in MusicBut no less interesting!

Throughout the last era, we have been going through different information storage formats. From bits to bytes, from small data to medium data, and from magnetic tapes to MP3 and MP4 formats, for example.

How to Learn Big Data from Zero and What Big Data Analysis

Throughout the text I will detail what Big Data is and how to learn it from scratch.

We’ll also talk about analytics, artificial intelligence, and I’ll explain the dimensions and scope that this data analytics tool reaches.

Without a doubt, we are in the information age, and if companies know how to make a funnel analyzing all the data that are on the web, about users and potential customers, will exponentially increase their profits.

Table of Contents

What is Big Data and how to learn it from scratch?

There are many definitions for Big Data analysis, but they all come to say the same thing after all. In my opinion, one specific definition could be this.

All these data represent trends, political movements, or purchasing channels chosen by people, that is, whoever knows how to manage and interpret these data, will be many steps ahead of the competition that remains on the sidelines. Hence the importance that is being given in recent years to Learning Big Data.

Big Data Solutions Data Variables:

The variables of Big Data Solutions are many and I summarize them in this list of 5 factors:


As for the volume, there is nothing written about the minimum amount of data storage required to be Big Data, medium un Data, or small data. But what is true is that we are talking about amounts of data that cannot be stored on an ordinary computer. They usually need a lot of terabytes or even petabytes.


The data we work with in Big Data are mostly in real time, with speeds higher than one data per second. The more extreme side can be seen in stock markets such as the New York Stock Exchange or Madrid. Where transactions take less than a nano second.


There are several forms of data storage, or also called database. These tables are used to organize and segment the extracted data. Examples of these tables are: customer tables, order tables, or text and number tables.


As in the case of statistics, in order for the Big Data studies to be representative, it is necessary to choose real data that are in line with reality. This is where we have a problem. Normally, Big Data data collection is usually automatic, so you will need to adjust the collection schedule so that it is a clear projection of the reality.


The real value of Big Data is to know how to convert all the extracted data into real information through the analysis. The data itself is just data. This is essential for companies as it is the differential factor.

The Main Applications of Analytics:

Big Data Analysis is useful for so many fields that many times our mind cannot even imagine them.

Throughout this section we will explore some of the most important ones for Learning Big Data from Zero:

Sales and Marketing

Nowadays, thanks to Big Data data analysis, we find that prices vary depending on the items we want to buy. Referral systems that increasingly know their own customers better and customise their offers to suit individual consumer tastes.

Marketing and sales

Previously, customer information was managed through long spreadsheets, but today’s vision is more holistic. Merges the information from the data generated by the communication channels and those that stand out the most in social networks.

The 2 applications of marketing analytics are:

  • On the web with real-time advertising auctions.
  • And the physical world thanks to some devices called beacons located in different spaces.


All the data collected through Big Data analysis is the basis of the optimization of operations and business processes that will allow you to optimize the stage of production processes.

Customer Experience

The customer’s experience in the analysis is playing one of the most important roles. Now we understand beyond the individual purchase, it is not the same to see a customer making an individual purchase of 100 €, as it is to have the customer’s previous statistics indicating that this person will generate an estimated purchase projection of 20,000 € in 15 years.

Financial risk

Nowadays, companies have many more factors to rely on when granting or not granting a bank loan. Although a priori it may only seem like a benefit for financial institutions, if we analyze it more thoroughly, we will realize that it will allow the people of the street, in the future, to know whether or not they will be able to face the loan they are applying for.

Safety and security

Analytics are also used to predict and analyze possible crime by geographic area, or to profile the timing of suspicious activity on the Internet, such as drug sales, or any other possible crime on the Internet.


Thanks to technologies such as bracelets, or other meters, it is easier to get our physical health, and that of people with various diseases, right.

It has also been shown to be helpful for early diagnosis and for monitoring degenerative diseases.

These analyses have systems for the collection, transformation and aggregation of patient data. They are sent to hospitals, clinics and clinics almost immediately. This improves medical decision making, as well as the speed of reaction to possible incidents.

The Big Data Visualization Success Stories:

That’s right, enough with the theory. These are some of the most notable success stories of large and medium-sized enterprises.


Amazon has been one of the first companies to understand the importance of data analysis. It has an enormous amount of data collected through its website, one of the most important websites for buying and selling in the world. They contain data on browsing habits, shopping interests or even patterns of behavior.

One of the most interesting cases in amazon is the implementation of a small robotic vehicle that they use to lift the shelves and get closer to the people who are preparing packages, thus reducing the work time and optimizing the transit processes to reduce the packing times.

Press and Reporters

Thanks to the Internet and data analysis, so-called”Open Data” has emerged. This makes it possible to access the news that is happening around the world in real time. Today, if a catastrophe like 9/11 were to happen, there would be a reporter who would be broadcasting in real time what is happening. That’s why this Post about Learning Big Data from Zero is so important for new journalists and reporters.

This, in addition to improving information, is essential in order to act as quickly as possible in the event of disasters of this calibre.

Today, many of the videos we see in the newspapers are not recorded by professional reporters, but by people on the street. What does this mean?

That we are in the Information Age, anyone with a 2.0 device can report on whatever they want in real time. Now you are closer to having the knowledge required after reading how to Learn Big Data from Zero

What have we learned?

Finally, and taking into account the difficulty of all the concepts that comprise the Big Data. I will not delay any longer and outline the key concepts:

I will summarize the 5 most common mistakes we make when talking about this analysis:

Here is a small summary of the most common mistakes we often make when thinking of Big Data.

Thinking Big Data Analysis is just Data

We believe that Big Data only means that we have more data, and therefore know more about the customer. But the truth is that the volume of data, as I explained earlier, is only part of one of the dimensions, which means that this is only a partial view of what it means. This point is very important when it comes to understanding and learning the Big Data from scratch.

Big Data really is a paradigm shift. In addition to the volume and speed, the coordinates, and especially the type of data we handle, enter it.

Traditional databases are sufficient for Big Data Analysis

The truth is that we tend to think that the databases collected years ago are useful for us to analyze variables of today. But the truth is that data and trends change so rapidly that these data are unlikely to be representative of reality.

Big Data Analytics allows us to predict the Future

It is true that Big Data analytics tries to predict the future, but not a single future. There are multiple possibilities, so that the analytical generates many scenarios of certain situations that may or may not occur, this means that it is not an objective science, but tries to cover all subjective possibilities, and choose the best option.

Although it is for my taste the best tool to make decisions. It is not a crystal ball that has a 100% success rate, so you have to take into account that it has a small error rate.

Artificial intelligence is not Big Data.

The truth is that artificial intelligence is made up of trillions of data that come from analyses like Big Data (Learning Big Data from Zero). This is why, in some ways, artificial intelligence applications without Big Data would not have enough information to learn how to do all the processes in which they are immersed today.

Artificial intelligence is a matter of the future

Today we are being able to invent an increasingly virtual reality. The truth is that it is scary…. and in my opinion we must be very aware of where we can and cannot go. There are tasks performed by man, which must continue to be performed by man. That is to say, these technological advances must serve to make people’s lives easier, but never replace them.

The 5 keys that we must not forget to learn the Bid Data Analysis from Zero:

To make it clear that we have learned in this short article, I will detail the most important keys of Big Data Solutions.

Go step by step

To be clear that to create a database and then analyze it is better to go step by step. The data collected over the years are more representative in the long term than those collected in the short term. Don’t try to go faster than you can, and when it comes to analyzing, put yourself in the hands of professional analysts.

Distribute Big Data Data

Store the data in a way that makes it easy for you to retrieve it and know where it is. Many slow computers can be faster than the single most powerful computer on the market.

The Art of Asking

For a good Big Data strategy it is important to know the questions we want to answer. So that the analyst can then predict the data that will be given in the future.

The questions will have to allow us to understand what happens, why it happens, and what will happen in the future.

Keeping Artificial Intelligence in mind

The revolution of artificial intelligence will have a lot of weight in the labor market in the years to come.

Automation of the World

This world is on its way to becoming automated. It is in our hands to do so consciously and with the future in our hands. Artificial intelligence is reaching domains beyond logic or physics. There is time to develop themes and automate jobs. What is clear is that the world of the future is getting closer and closer. That’s why it’s so important to learn Big Data from scratch now that we’re on time.


Well, we have reached the end of the article“How to Learn Big Data from Zero”.

I hope it has at least given you an overview of what Big Data means and how important it is today in today’s business and global marketplace.

I will end with my conclusion about Big Data analysis and how to learn it from scratch:

I hope you liked this article on how to learn Big Data from scratch, and now you are closer to knowing how to explain its meaning when you have a few bars with your friends.

Hacker. Flying high with my personal best. Marketing Specialist. Providing Social Media Content and Strategies in the Digital Marketing world.

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