A Guide to Big Data for Novices That Includes Everything You Should Know

A Guide to Big Data for Novices
A Guide to Big Data for Novices

Ciftcikitap.com – A Guide to Big Data for Novices, Big Data is a term that has been brought up in nearly every discussion pertaining to technological advancements, the Internet of Things (IoT), and the investigation of data science. Having said that, there is still a degree of ambiguity about the precise meaning of this phrase. In this Big Data lesson, our primary goal is to provide you with all of the information that you require before beginning work with Big Data.

To put it another way, big data refers to the collection, analysis, and processing of massive amounts of diverse data that originate from a variety of sources. These big datasets have the potential to reveal insights into human behavior, as well as to inform corporate practices, strategy, product creation, artificial intelligence, and other areas of study. In this lesson on Big Data, we will discuss some of the most important ideas and terms associated with the aforementioned jargon.

We have high hopes that by the time you reach the end of this lesson, you will have enough information to begin your adventure into the world of big data. However, before we go on to that part of our Big Data tutorial, let’s have a look at the differences between small data and Big Data.

When compared to smaller datasets, big data is much easier to comprehend in terms of its whole breadth. The term “small data” refers to information that is manageable by a single machine or that may be analyzed using more conventional techniques. Both the origin of this data and its effects are on a more localized scale. For instance, production logs can be utilized to generate weekly performance reports on the productivity of a manufacturing line. Alternatively, survey results can be used in a marketing report about brand perception. Both of these examples are examples of how data can be used.

The only thing we need to do to comprehend the clear distinction between the two kinds of data is look at some statistics: by the year 2020, every individual on earth will generate 1.7MB of data every second, and this data will come from more than 50 billion gadgets that are connected to the internet. It is possible to leverage such a massive volume of data, which comes from almost as many sources, to guide business decisions across entire industries, to restructure e-commerce sites, and even to revolutionize the way health care is delivered.

Now that you have a general understanding of what “Big Data” is, it’s time to take this “Big Data” tutorial one step further and discuss the fundamental ideas behind the field.

Big Data Features and Qualities

How can heterogeneous data be processed on such a huge scale when standard methods of analytics are guaranteed to fail? One of the most critical issues that big data scientists have faced is addressing this issue. Doug Laney, a senior analyst at Gartner, provided a presentation that outlined the three primary ideas that constitute “big data” in order to simplify the solution.


When it comes to big data platforms, this is the most important factor that sets them apart. There is a digital imprint left by each one of us, and the number of data sets that can be compiled from each of our many electronic gadgets is mind-boggling. Consider the social networking website Facebook as an illustration: as of the year 2016, there were 2.6 trillion posts on the website. Twitter currently processes 500 million tweets every single day. When you consider all of the different digital devices to which a person has access, it is simple to comprehend how each individual on the planet generates an average of 0.77 gigabytes (GB) of data each and every day.


Ninety percent of the data that is currently available was only produced in the last two years. Every single day, 2.5 quintillion bytes of data are produced, and it is expected that this data will be analyzed in real time (or near real time), in order to develop insights that will not become obsolete in a world that is always evolving. Because of this, big data analysts have moved away from the traditional batch-oriented methodology and have shifted their focus to real-time analysis. This is done to ensure that the information they generate is pertinent to the circumstances that are currently occurring.


The fact that big data systems deal with unique datasets, which originate from a wide variety of sources and are processed using a wide variety of methodologies, is a significant part of the reason why these systems are so relevant to organizations and communities. Data can come from a variety of sources, including feeds from social media platforms, actual devices like Fitbits, home security systems, GPS systems in automobiles, and more. The data itself is extremely varied; it may consist of rich media (pictures, movies, and audio files), structured logs, or unstructured data. All of these formats are possible. Big data’s unique selling proposition is that it brings together all of this information, irrespective of where it came from, to produce an exhaustive data set for each individual user.

Since 2001, “The Three Vs” have been utilized to differentiate big data; however, the most recent narratives support the addition of “veracity, visualization, variability, and value” to this list, which broadens the scope of big data research even more.

That concludes our discussion on the features of Big Data; in the next part of this tutorial on Big Data, let’s move on to the topic of making this data usable and gaining insights from it.

How to make sense of massive data?

One of the most appealing aspects of big data is the diversity of conclusions that may be formed from it. Because many of the insights, trends, and patterns are frequently obscure, accomplishing this goal typically cannot be accomplished through the use of conventional procedures. In addition, the tools used for analyzing small amounts of data do not lend themselves well to the vast volume and variety of material that can be generated using big data methods.

In order to address these obstacles, numerous new technologies have been developed, with Apache Hadoop emerging as the most prominent of these. Clustered computing is utilized by these technologies in order to ingest data into a data system, compute and analyze the data, and visualize the data streams.

Big Data has firmly established itself in every conceivable industry, and it would be irresponsible of me not to discuss the marvels that are being accomplished by Big Data.

Utilizations and Applications of Big Data

On a more personal level, big data is being used to maximize individual health, which is a step in the direction of personal development. Armbands and smartwatches collect data on the user’s sleep cycle, calorie consumption, exercise levels, and other factors in order to produce insights on improving the user’s health. These insights are then communicated to the individual user in a manner that is tailored to their specific needs.

Advertising: Marketing companies are using a variety of data points, such as global positioning systems (GPS), traffic patterns, eye-movement tracking, and other similar technologies, to determine what advertisements people are more interested in, and to formulate a marketing strategy that is more accurate as a result. This is a departure from the conventional marketing technique, in which pricing was determined based on the number of times an advertisement was viewed.

Supply chain optimization: Big data is playing a big role in delivery route optimization, which is a huge concern for companies like Amazon and eBay. This is the process by which live traffic data, driver behavior, and other factors are tracked using radio frequency identifiers and GPS systems in order to determine the best route to take, taking into account the time of day and the season.

Forecasting the weather: applications installed on mobile phones are being used to solicit input from the general public in real time regarding weather patterns. These apps are able to create accurate real-time data for prediction models by utilizing a combination of ambient thermometers, barometers, and hygrometers. This can significantly increase the accuracy of weather forecasting systems.

The development of smart city infrastructure includes cities testing out big data analysis technologies as part of the process of building smart city infrastructure. Drought-ridden The state of California was able to reduce its overall water consumption by 80 percent by tracking the water use of individual consumers using big data analytics. The monitoring of traffic signals all throughout the city has resulted in a 16% decrease in the amount of traffic congestion in Los Angeles.

Big Data is expanding at an exponential rate and is gaining a stronger foothold in every industry as each year goes by. We sincerely hope that this Big Data tutorial was able to assist you in comprehending the fervor that surrounds the term “Big Data.” There are a multitude of Big Data tutorials, courses, and certifications available that will get you started off on the right foot if you are interested in delving further.