The Complete Overview of Big Data

Data is one of the prime factors of any business purpose. Business Enterprises are data-driven and without data, no one can have a competitive advantage. It has different definitions wherein the huge amount of data can be considered as Big Data. It is the most widely used technology these days in almost every business vertical.

Big Data Definition

For you to understand Big Data, it is important that you first understand what data is.

Data can be defined as figures or facts that can be stored in or can be used by a computer.

Now, what is Big Data?

Big Data is a term that is used for denoting a collection of datasets that is large and complex, making it very difficult to process using legacy data processing applications.

So, legacy or traditional systems cannot process a large amount of data in one go. But, how will you classify the data that is problematic and hard to process? This Big data tutorial will give you in-depth knowledge about what is Big Data and Hadoop?

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Types of Big Data

Big Data is essentially classified into three types:

• Structured Data
• Unstructured Data
• Semi-structured Data

The above three types of Big Data are technically applicable at all levels of analytics. It is critical to understand the source of raw data and its treatment before analysis while working with large volumes of big data. Because there is so much data, extraction of information needs to be done efficiently to get the most out of the data. The ETL process for each data structure varies.

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Structured Data

Structured data is highly organized and thus, is the easiest to work with. Its dimensions are defined by set parameters. Every piece of information is grouped into rows and columns like spreadsheets. Structured data has quantitative data such as age, contact, address, billing, expenses, debit or credit card numbers, etc.

Due to structured data’s quantitative nature, it is easy for programs to sort through and collect data. It requires little to no preparation to process structured data. The data only needs to be cleaned and pared down to the relevant points. The data does not need to be converted or interpreted too deeply to perform a proper inquiry.

Structured data follow road maps to specific data points or schemas for outlining the location of each datum and its meaning.

The streamlined process of merging enterprise data with relational data is one of the perks of structured data. Due to the pertinent data dimensions being defined and being in a uniform format, very little preparation is required to have all sources be compatible.

The ETL process, for structured data, stores the finished product in a data warehouse. The initial data is harvested for a specific analytics purpose, and for this, the databases are highly structured and filtered. However, there is only a limited amount of structured data available, and it falls under a slim minority of all existing data. Consensus says that structured data makes up only 20 percent or less of all data.

Unstructured Data

Not all data is structured and well-sorted with instructions on how to use it. All unorganized data is known as unstructured data.

Almost everything generated by a computer is unstructured data. The time and effort required to make unstructured data readable can be cumbersome. To yield real value from data, datasets need to be interpretable. But the process to make that happen can be much more rewarding.

The challenging part about unstructured data analysis is teaching an application to understand the information it’s extracting. Oftentimes, translation into structured form is required, which is not easy and varies with different formats and end goals. Some methods to achieve the translation are by using text parsing, NLP, and developing content hierarchies through taxonomy. Complex algorithms are involved to blend the processes of scanning, interpreting, and contextualizing.

Unlike structured data, which is stored in data warehouses, unstructured data is stored in data lakes. Data lakes preserve the raw format of data as well as all of its information. Data lakes make data more malleable, unlike data warehouses where data is limited to its defined schema.

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Semi-structured Data

Semi-structured data falls somewhere between structured data and unstructured data. It mostly translates to unstructured data that has metadata attached to it. Semi-structured data can be inherited such as location, time,  email address, or device ID stamp. It can even be a semantic tag attached to the data later.

Consider the example of an email. The time an email was sent, the email addresses of the sender and the recipient, the IP address of the device that the email was sent from, and other relevant information are linked to the content of the email. While the actual content itself is not structured, these components enable the data to be grouped in a structured manner.

Using the right datasets can make semi-structured data into a significant asset. It can aid machine learning and AI training by associating patterns with metadata.

Semi-structured data’s no set schema can be a benefit as well as a challenge. It can be a challenge to put in all that effort to tell an application the meaning of each data point. But at the same time, there are no limits in structured data ETL in terms of definition.

Subtypes of Data

Apart from the three above-mentioned types, there are subtypes of data that are not formally considered Big Data but are somewhat pertinent to analytics. Most times, it is the origin of data such as social media, machine (operational logging), event-triggered, or geospatial. It can also involve access levels—open (open source),  linked (web data transmitted via APIs and other connection methods), or dark or lost (siloed within systems for the inaccessibility to outsiders such as CCTV systems).

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Characteristics of Big Data

Big Data has the following distinct characteristics:

1. Volume: This refers to tremendously large data. As you can see from the image, the volume of data is rising exponentially. In 2016, the data created was only 8 ZB; it is expected that, by 2020, the data would rise to 40 ZB, which is extremely large.

2. Variety: A reason for this rapid growth of data volume is that data is coming from different sources in various formats. We have already discussed how data is categorized into different types. Let us take another glimpse at it with more examples.

a) Structured Data: Here, data is present in a structured schema along with all the required columns. It is in a structured or tabular format. Data that is stored in a relational database management system is an example of structured data. For example, in the below-given employee table, which is present in a database, the data is in a structured format.

b) Semi-structured Data: In this form of data, the schema is not properly defined, i.e., both forms of data are present. So, semi-structured data has a structured form but it is not defined; for example, JSON, XML, CSV, TSV, and email. The web application data that is unstructured contains transaction history files, log files, etc. Online Transaction Processing (OLTP) systems are built to work with structured data, and this data is stored in relations, i.e., tables.

c) Unstructured Data: This data format includes all unstructured files such as video files, log files, audio files, and image files. Any data that has an unfamiliar model or structure is categorized as unstructured data. Since its size is large, unstructured data possesses various challenges in terms of processing for deriving value out of it. An example of this is a complex data source that contains a blend of text files, videos, and images. Several organizations have a lot of data available with them but they don’t know how to derive value out of it since the data is in its raw form.

d) Quasi-structured Data: This data format consists of textual data with inconsistent data formats that can be formatted with effort, time, and with the help of several tools. For example, web server logs, i.e., a log file that is automatically created and maintained by a server that contains a list of activities.

3. Velocity: The speed of data accumulation also plays a role in determining whether the data is big data or normal data.

As can be seen in the image below, mainframes were initially used when fewer people were using computers. As computers evolved, the client/server model came into existence. Later, web applications came into the picture and their popularity extended to more and more devices such as mobiles, which led to the creation of a lot of data!

4. Value: How will the extraction of data work? Here, our fourth V comes in; it deals with a mechanism to bring out the correct meaning of data. First of all, you need to mine data, i.e., the process to turn raw data into useful data. Then, an analysis is done on the data that you have cleaned or retrieved from the raw data. Then, you need to make sure whatever analysis you have done benefits your business, such as in finding out insights, results, etc., in a way that was not possible earlier.

You need to make sure to clean up whatever raw data you are given for deriving business insights. After you have cleaned the data, a challenge pops up, i.e., during the process of dumping a large amount of data, some packages might be lost.
So, to resolve this issue, our next V comes into the picture.

5. Veracity: Since packages get lost during execution, we need to start again from the stage of mining raw data to convert it into valuable data. And this process goes on. There will also be uncertainties and inconsistencies in the data that can be overcome by veracity. Veracity means the trustworthiness and quality of data. The veracity of data must be maintained. For example, think about Facebook posts, hashtags, abbreviations, images, videos, etc., which make the posts unreliable and hamper the quality of their content. Collecting loads and loads of data is of no use if the quality and trustworthiness of the data are not up to the mark.

Now that you have a clear idea of what Big Data is, let us check out the major sectors using Big Data on an everyday basis.

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Major Sectors Using Big Data Every Day

The applications of big data provided solutions to every sector like Banking, Government, Education, and healthcare, etc.

Banking

Since there is a massive amount of data that is gushing in from innumerable sources, banks need to find uncommon and unconventional ways to manage big data. It’s also essential to examine customer requirements, render services according to their specifications, and reduce risks while sustaining regulatory compliance. Financial institutions have to deal with Big Data Analytics to solve this problem.

• NYSE (New York Stock Exchange): NYSE generates about one terabyte of new trade data every single day. So imagine, if one terabyte of data is generated every day, in a whole year how much data there would be to process. This is what Big Data is used for.

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Government

Government agencies utilize Big Data and have devised a lot of running agencies, managing utilities, dealing with traffic jams, or limiting the effects of crime. However, apart from its benefits in Big Data, the government also addresses the concerns of transparency and privacy.

• Aadhar Card: The Indian government has a record of all 1.21 billion citizens. This huge data is stored and analyzed to find out several things, such as the number of youth in the country. According to which several schemes are made to target the maximum population. All this big data can’t be stored in some traditional database, so it is left for storing and analyzing using several Big Data Analytics tools.

Education

Education concerning Big Data produces a vital impact on students, school systems, and curriculums. By interpreting big data, people can ensure students’ growth, identify at-risk students, and achieve an improvised system for the evaluation and assistance of principals and teachers.

• Example: The education sector holds a lot of information concerning curriculum, students, and faculty. The information is analyzed to get insights that can enhance the operational adequacy of the educational organization. Collecting and analyzing information about a student such as attendance, test scores, grades, and other issues take up a lot of data. So, big data approaches a progressive framework wherein this data can be stored and analyzed making it easier for the institutes to work with.

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Big Data in Healthcare

When it comes to what Big Data is in Healthcare, we can see that it is being used enormously. It includes collecting data, analyzing it, leveraging it for customers. Also, patients’ clinical data is too complex to be solved or understood by traditional systems. Since big data is processed by Machine Learning algorithms and Data Scientists, tackling such huge data becomes manageable.

• Example: Nowadays, doctors rely mostly on patients’ clinical records, which means that a lot of data needs to be gathered, that too for different patients. It is not possible for old or traditional data storage methods to store this data. Since there is a large amount of data coming from different sources, in various formats, the need to handle this large amount of data is increased, and that is why the Big Data approach is needed.

E-commerce

Maintaining customer relationships is the most important in the e-commerce industry. E-commerce websites have different marketing ideas to retail their merchandise to their customers, manage transactions, and implement better tactics of using innovative ideas with Big Data to improve businesses.

• Flipkart: Flipkart is a huge e-commerce website dealing with lots of traffic daily. But, when there is a pre-announced sale on Flipkart, traffic grows exponentially that crashes the website. So, to handle this kind of traffic and data, Flipkart uses Big Data. Big Data can help in organizing and analyzing the data for further use.

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Social Media

Social media in the current scenario is considered the largest data generator. The stats have shown that around 500+ terabytes of new data get generated into the databases of social media every day, particularly in the case of Facebook. The data generated mainly consist of videos, photos, message exchanges, etc. A single activity on any social media site generates a lot of data which is again stored and gets processed whenever required. Since the data stored is in terabytes, it would take a lot of time for processing if it is done by our legacy systems. Big Data is a solution to this problem.

Let’s now continue our tutorial by checking out why Big Data is important to us that we are so concerned about.

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Why is Big Data so important?

Although big data may not immediately kill your business, neglecting it for a long period won’t be a solution. The impact of big data on your business should be measured to make it easy to determine a return on investment. Hence, big data is a problem worth looking into.

Whenever you visit a website, you might have noticed that on the right panel or top panel or somewhere on the screen, you will find a recommendation field which is an advertisement that is related to your preferences. How does the advertisement company know that you would be interested in it?
Well, everything you surf on the Internet is stored and all this data is analyzed properly so that whatever you are surfing for or you’re interested in comes up. You will be interested in that particular advertisement and you will be surfing further. But, mind you! The amount of data generated from a single user is so huge that it is considered big data.

The following image shows two advertisements popping up.

Have you ever noticed when you go to YouTube, YouTube knows what kind of videos you would like to watch and what you must be looking for next? Similarly, Amazon shows you the type of products you must be looking to buy. Even if you would have searched for a pair of earphones, you will keep on getting the recommendation of earphones, again‌ ‌and‌ ‌again, that too on different websites.
How does this happen?

It happens because of Big Data Analytics.

What is Big Data Analytics?

Big Data Analytics examines large and different types of data to uncover hidden patterns, insights, and correlations. Big Data Analytics is helping large companies facilitate their growth and development. And it majorly includes applying various data mining algorithms on a certain dataset.

How is Big Data Analytics used today?

Big Data Analytics is used in several industries to allow organizations and companies to make better decisions, as well as verify and disprove existing theories or models. The focus of Data Analytics lies in inference, which is the process of deriving conclusions that are solely based on what the researcher already knows.

Let us now see a few of the Big Data Analytics tools.

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Tools for Big Data Analytics

• Apache Hadoop
  Big Data Hadoop is a framework that allows you to store big data in a distributed environment for parallel processing.
• Apache Pig
  Apache Pig is a platform that is used for analyzing large datasets by representing them as data flows. Pig is designed to provide an abstraction over MapReduce which reduces the complexities of writing a MapReduce program.
• Apache HBase
  Apache HBase is a multidimensional, distributed, open-source, and NoSQL database written in Java. It runs on top of HDFS providing Bigtable-like capabilities for Hadoop.
• Apache Spark
  Apache Spark is an open-source general-purpose cluster-computing framework. It provides an interface for programming all clusters with implicit data parallelism and fault tolerance.
• Talend
  Talend is an open-source data integration platform. It provides many services for enterprise application integration, data integration, data management, cloud storage, data quality, and Big Data.
• Splunk
  Splunk is an American company that produces software for monitoring, searching, and analyzing machine-generated data using a Web-style interface.
• Apache Hive
  Apache Hive is a data warehouse system developed on top of Hadoop and is used for interpreting structured and semi-structured data.
• Kafka
  Apache Kafka is a distributed messaging system that was initially developed at LinkedIn and later became part of the Apache project. Kafka is agile, fast, scalable, and distributed by design.

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Benefits of Big Data Analytics

Big Data Analytics is indeed a revolution in the field of Information Technology. The use of Data Analytics by various companies is increasing every year. Their primary focus of them is on their customers. Hence, the field is flourishing in Business-to-Consumer (B2C) applications.
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Why Apache Hadoop?

Most database management systems are not up to the mark for operating at such lofty levels of Big Data requirements either due to the sheer technical inefficiency or the insurmountable financial challenges posed. When the type of data is unstructured, the volume of data is huge, and the results needed are at uncompromisable speeds, then the only platform that can effectively stand up to the challenge is Apache Hadoop.

Hadoop owes its runaway success to a processing framework, MapReduce, that is central to its existence. MapReduce technology lets ordinary programmers contribute their part where large datasets are divided and are independently processed in parallel. These coders need not know the nuances of high-performance computing. With MapReduce, they can work efficiently without having to worry about intra-cluster complexities, monitoring of tasks, node failure management, and so on. We shall be learning about MapReduce in the following section of this Hadoop tutorial.

Now in this tutorial, let’s understand how Walmart used Big Data to increase its sales.

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How did Big Data help in driving Walmart’s Performance?

Walmart is the biggest retailer in the world with the most revenue. Consisting of two million employees and 20,000 stores, Walmart is building its private cloud to incorporate 2.5 petabytes of data every hour.

Walmart has been collecting data on products that have the maximum sales in a particular season or because of some specific reason. For example, if people are buying candies during the Halloween season, along with costumes, you’d see a lot of candies and costumes all around Walmart only during the Halloween season. This it does base on the Big Data Analytics it had made for the previous years’ Halloween seasons.

Again, when in 2012, Hurricane Sandy hit the US it was analyzed by Walmart, from the data it had collected and analyzed from such previous instances, that people generally buy emergency equipment and strawberry pop-tarts when a warning for an approaching hurricane is declared. So, this time too, Walmart quickly filled its racks with the emergency equipment people would require during the hurricane in the red alert areas. This made the selling of these products very quick and Walmart made a lot of profit.

With this, our second section of the Hadoop tutorial comes to an end. In this section, we learned about Big Data, Big Data Analytics, Big Data technologies, Big Data tools, and so on. In the next session on this Hadoop tutorial, we will be learning about Hadoop Architecture in detail.

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