Big Data Analytics & Big Data Management: 10 things you absolutely need to know

Knowing what goes around the mendo of Big Data is important simply because everything in our contemporary world is connected and generates data;
just think of our smartphone with all its applications from the less heavy ones to those like Facebook that exchange significant amounts of data, etc.
Big Data Analytics because data have an effect on impact in everyday life and in the IoT (Internet of Thinks) the Internet of Things; they help to track patterns and make decisions, they help to understand phenomena (KPIs or metrics) and thus to know what to do.

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Big Data Management because all this amount of data needs to be managed, stored, and maintained for what it is needed and in the time it is needed, just in order to be able to make queries that are increasingly frequent and increasingly related to artificial intelligence algorithms.

If we go back and reread a 2012 post from


, it already contained a prediction calculated precisely for 2020 namely that each of us, regardless of men, women or children, would generate 5,200 gb of data, considering that the bulk of it does not pass through our hands but is generated by the very systems that talk to each other.

The above numbers are truly frightening but the vast majority of them, while generated, are not used but remain silent even though they could be useful.

2012 Big Data Study infographic 600

Big Data Analytics & Big Data Management: examples of how much data is generated

The amount of data generated daily is so great that the resources to be able to handle all this large amount of information must be above imaginable…

All of these statistics taken from the


(if you visit it you will see that they will have already changed a great deal) give an idea of how much data can be produced and what level resources need to be fielded for data analysis alone.

Now we give you an “essay” of the statistics between two dates just to realize how much Big Data related to stocks (in this case Big Data Analytics) grows by leaps and bounds the first as of March 31, 2020 and the second, as of today’s day of writing, i.e., October 05, 2020 (and this affects Big Data Management).

big data
big data 05102020

TOTAL INTERNET USERS 4,517,295,074 are the world’s internet users as of March 31, 2020 out of a total population of 7,530,000,000 people

big data: internet users

1,760,112,297 the number of websites on the web

234,691,422,222 emails sent today alone

6,367,266,286 searches made within the Google search engine

6,089,703 posts written in today alone

6,495,686,845 videos viewed on Youtube today

694,298,082 tweets made by users in today alone

130,661,933 posts written on social Tumblr

76,418,304 photos uploaded to Instagram today

2,462,581,363 Facebook users active to date

807,074,031 the users of Google+

284,244,253 active users on Pinterest

358,637,469 Twitter users to date

353,156,804 chats conducted on Skype

136,016 sites hacked in today’s day

3,845,503 smartphones sold today

620,949 computers sold today

6,996,126,755 the GB of traffic moved today

380,066 tablets sold today

3,724,132 the MWh of Electricity consumed and

3,039,645 the tons of CO2 emissions generated by the internet network

All these statistics taken from the INTERNETLIVESTATS.COM website (if you visit it you will see that they will have already changed a great deal) give an idea of how much data can be produced and what level resources need to be fielded for data analysis alone.

The more we go on over the years, the more the amount of data being collected at any level grows, in order to study behaviors, analyze sales, study the effects of decisions, etc.

Data, and today therefore Big Data, is fundamental to understanding phenomena; data is an essential resource for economic growth, competitiveness, innovation, job creation and the advancement of society at large.

Let us see how the


has structured this infographic that makes us realize how many aspects can be touched by Big Data processing:

big data infografica

Big Data Analytics & Big Data Management: the potential for processing

Decoding the human genome in 2003 took 10 years, with today’s computing capabilities it takes 1 week while in the future it may take only a few hours.

This is all because we have highways (broadband) available that allow the exchange of large amounts of data while, thanks to collection via the cloud we can dispose of it even remotely and, as a result of the high performance of current computers we can generate reports, statistical models and whatever else very naturally.

Big Data Analytics & Big Data Management: real-world applications

In every aspect of our lives, data and the knowledge of it, can positively affect improvements that enable us and will enable us to increase our everyday lives 360 degrees by enhancing every aspect of them.

In travel, they can help govern smart traffic lights and traffic flows, they can improve understanding and diagnostics of health-related problems by improving our average life both in terms of quality with less illness and in terms of actual lengthening of life lived.

In agro-livestock supply chains they can help make increasingly efficient uses of natural resources, while in industrial production they help implement efficiency and productivity.

In our home they can help manage smart home systems.

Big Data Analytics & Big Data Management and new economy

The whole world revolving around big data processing and artificial intelligence systems has benefited over the past 20 years from huge investments made by major market players.

The more years go by, the more big data companies gain in importance and are in turn acquired by larger companies since, in the future, among the elements that cannot really be done without are:

Storage and cloud computing in general

The processing and management of big data

Communication infrastructure

Artificial intelligence systems

Crowdstrike and Elastic reached large valuations at the time of the IPO ($7 billion and $5 billion, respectively).

Other IPOs included PagerDuty ($1.8 billion), Anaplan ($1.8 billion) and Domo ($500 million).

There have been major acquisitions, such as Qualtrics (acquired by SAP for $8 billion), Medidata (acquired post-IPO by Dassault for $5.8 billion), Hortonworks (merged with Cloudera adding $5.2 billion in value), Imperva (acquired by Thoma Bravo for $2.1 billion), AppNexus (acquired by AT&T for $2 billion), Cylance (acquired by BlackBerry for $1.4 billion), Datorama (acquired by Salesforce for $800 million), Treasure Data (acquired by Arm for $600 million), Attunity (acquired post-IPO by Qlik for $560 million), Dynamic Yield (acquired by McDonalds for $300 million), and the list is still long.

Even at the startup level, investments are getting bigger and bigger as more and more companies are in the market…our Big Data Innovation Group is proof of that.

On the website of


Matt Turck an explanatory table of all the groupings of companies operating in this world is updated annually and we publish: Big_Data_Landscape_Final

2019 Matt Turck Big Data Landscape Final Fullsize

Big Data Analytics & Big Data Management: according to IBM

Several major players in this field, give almost unambiguous definitions to Big Data but characterize aspects of it in different ways:

According to one of the leading players in the big data world, IBM perlappunto, such big data sources are characterized by 4 Vs:

Volumes of data and thus breadth of quantity, Variety or variety of data and thus types, Veracity or reliability of data, and Velocity i.e., speed for their collection and processing.

In the next table all the essential characteristics according to them:

4 Vs of big data scaled

Big Data Analytics & Big Data Management: sources according to IBM

Le fonti dei big data

Big Data Analytics & Big Data Management: their evolutions

We have seen so far how our daily interactions with Facebook, Whatsapp, email, web browsing, shopping, etc., involve the collection of data from a myriad of parties that, beyond the proper treatment of the same according to the regulations in force in various countries for the treatment of sensitive data (GDPR on privacy), is stored by an equal myriad of sources.

Just think of Google’s Universal Analytics or Facebook Insights alone, which represent impressive amounts of Big Data to analyze and correlate with each other.

Today’s computing capabilities have made it possible to relate almost inexhaustible data sources in order to structure or otherwise relate them after rationalizing them.

Big Data Analytics & Big Data Management: types of Big Data

Structured: data format organized with a fixed schema, example RDBMS

Semi-structured: partially organized data that does not have a fixed format, example type XML, JSON, etc.

Unstructured: unorganized data with an unknown pattern such as audio files, video files, etc.

Big Data Analytics & Big Data Management: value in the present and future

As it is increasingly said … the future is in the data and, more importantly, the correlation between them.

Increasing computational capabilities will provide the ability to relate data sets and trace patterns.

Increasing storage capabilities will allow us to extend research over longer periods by providing greater reliability to the models themselves.


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