What is Big Data?
We show you everything you need to know about Big Data
Also known as big data, big data, or large-scale data. It is a term that refers to a large amount of data that exceeds the capacity of traditional software. This, to be captured, administered and processed in a specified time.
In practice, big data generally refers to analyzing user behavior by extracting and storing valuable data and making predictions based on observed patterns.
In reference to the term Big Data
The term big data has been in use since 1990 and some credit John Mashey. To start with, we can classify big data into structured, unstructured and semi-structured data and there are some categories.
Category 1: This data is generated by the people themselves, information that comes from emails, messages by applications, Facebook, WhatsApp, Tweeds or surveys.
Category 2: They are obtained from transactions, here we can mention billing, telephone calls and access to Wi-Fi networks.
Category 3: Here electronic and web marketing this information generates a large amount of data when browsing the internet and on certain sites.
Category 4: This data is obtained through machine-to-machine interactions, data that is collected from meters, temperature, light, height, pressure and sound sensors.
Category 5: These are biometric data and are large amounts of data generated by biometric readers such as retinal scanners, fingerprint scanners, or DNA strand readers.
How does Big Data work?
Through the analysis of large volumes of data, they can provide new insights that open up new opportunities, and at the same time, help the generation of new business models or even improve them.
It helps companies or institutions to make good decisions. But, to clearly understand how it works, it is necessary to know the 3 key actions that are involved in Big data.
Integrate ETL
Big Data couples information from many sites and programs, traditional data integration mechanisms such as ETL (extract, transform and load).
They generally do not meet the desired expectations of this function, for which new strategies and technologies are required to analyze large data sets in capacities ranging from terabytes or even peta byte.
During integration, data is appended for processing to ensure that it is available, appropriately so that analysts can begin to use it.
Manage
Big data requires storage, this implies that, if it is necessary to finalize information from various sources, it would imply having enough space to store all that data.
Perhaps this is the most complex action to understand and to find the right solution for the company, to get an idea, a YouTube video with a quality of 480 pixels uses 4 Megabytes for every 2 minutes.
Using an external hard drive we can save a limited amount of videos, but now companies tend to migrate their data to the storage clouds because they can manage much more information practically and more according to their demands.
Analyze
The best investment for a company when using big data lies in the way in which the information is analyzed and how it will act based on the analysis of the data.
For this analysis to have the best results, there are 2 more companions for big data, machine learning (Machine Learning) and deep learning (Deep Learning).
The first is a subset of artificial intelligence that, through statistical techniques, provides computers with the ability to learn from data and trains the computer to continue developing this ability.
The second works as a technique to implement machine learning.
Big Data information, how is it generated?
Many of our daily activities on the internet generate macro data that are consequently the data sources of big data and are many pieces of information.
GPS devices, facial recognition sensors, or email are just a few examples.
The most common sources of these large amounts of data are usually websites, social networks, biometric devices, transactions, text messages, Telegram, apps etc.
Most people use certain online technologies and services like Hotmail and platforms like Facebook, and these companies allow us to send and exchange data and then use the data we provide for their benefit.
This means that online services, websites, applications, and many other devices are constantly analyzing data to make their services more effective and to develop new products.
They use big data tools and services to analyze and process large amounts of data and improve their products and offerings. But that's not all, someone thought "what if we better use big data so that machines can learn by themselves."
Thus was born machine learning.
What can we do with Big Data?
Nowadays, it is good to take into account the capabilities of big data, as it provides us with a lot of help in the commercial field and in business for those who want to grow their company.
Those small, medium or large companies that are capable of understanding the need to collect information, to work on it, apply artificial intelligence algorithms, machine learning and above all to make decisions that offer a better service or solution to their clients.
These are the companies that will be successful in the future, and we will name a few examples for inspiration.
Netflix
This popular platform was able to take advantage of the great capacity of big data using recommendation systems for movies or series.
They collect the behavior of all its users, review the content they are watching and which ones are related to each other and from here, they ingest a probability that we will see a certain episode.
Netflix uses big data to try to personalize our experience by consuming content that will keep us on the platform the most.
Companies such as Amazon, McDonald's, Spotify, Walmart, Zara, Hopper have learned from this and have been working with Big data for a long time to continue evolving in the market.