UNIT – I

What is Big Data?

According to Gartner, the definition of Big Data –
“Big data” is high-volume, velocity, and variety information assets that demand cost-effective,
innovative forms of information processing for enhanced insight and decision making.”

This definition clearly answers the “What is Big Data?” question – Big Data refers to complex and
large data sets that have to be processed and analyzed to uncover valuable information that can
benefit businesses and organizations.

However, there are certain basic tenets of Big Data that will make it even simpler to answer what
is Big Data:
 It refers to a massive amount of data that keeps on growing exponentially with time.
 It is so voluminous that it cannot be processed or analyzed using conventional data
processing techniques.
 It includes data mining, data storage, data analysis, data sharing, and data visualization.
 The term is an all-comprehensive one including data, data frameworks, along with the tools
and techniques used to process and analyze the data.

The History of Big Data

Although the concept of big data itself is relatively new, the origins of large data sets go back to
the 1960s and '70s when the world of data was just getting started with the first data centers and
the development of the relational database.

Around 2005, people began to realize just how much data users generated through Facebook,
YouTube, and other online services. Hadoop (an open-source framework created specifically to
store and analyze big data sets) was developed that same year. NoSQL also began to gain
popularity during this time.

The development of open-source frameworks, such as Hadoop (and more recently, Spark) was
essential for the growth of big data because they make big data easier to work with and cheaper to
store. In the years since then, the volume of big data has skyrocketed. Users are still generating
huge amounts of data—but it’s not just humans who are doing it.

With the advent of the Internet of Things (IoT), more objects and devices are connected to the
internet, gathering data on customer usage patterns and product performance. The emergence of
machine learning has produced still more data.

While big data has come far, its usefulness is only just beginning. Cloud computing has expanded
big data possibilities even further. The cloud offers truly elastic scalability, where developers can
simply spin up ad hoc clusters to test a subset of data.

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 Benefits of Big Data and Data Analytics

 Big data makes it possible for you to gain more complete answers because you have more
information.
 More complete answers mean more confidence in the data—which means a completely
different approach to tackling problems.

Types of Big Data

Now that we are on track with what is big data, let’s have a look at the types of big data:

a) Structured
Structured is one of the types of big data and By structured data, we mean data that can be
processed, stored, and retrieved in a fixed format. It refers to highly organized information that
can be readily and seamlessly stored and accessed from a database by simple search engine
algorithms. For instance, the employee table in a company database will be structured as the
employee details, their job positions, their salaries, etc., will be present in an organized manner.

b) Unstructured
Unstructured data refers to the data that lacks any specific form or structure whatsoever. This
makes it very difficult and time-consuming to process and analyze unstructured data. Email is an
example of unstructured data. Structured and unstructured are two important types of big data.

c) Semi-structured
Semi structured is the third type of big data. Semi-structured data pertains to the data containing
both the formats mentioned above, that is, structured and unstructured data. To be precise, it refers
to the data that although has not been classified under a particular repository (database), yet
contains vital information or tags that segregate individual elements within the data. Thus we come
to the end of types of data.

Characteristics of Big Data

Back in 2001, Gartner analyst Doug Laney listed the 3 ‘V’s of Big Data – Variety, Velocity, and
Volume. Let’s discuss the characteristics of big data.
These characteristics, isolated, are enough to know what big data is. Let’s look at them in depth:
a) Variety
Variety of Big Data refers to structured, unstructured, and semi-structured data that is gathered
from multiple sources. While in the past, data could only be collected from spreadsheets and
databases, today data comes in an array of forms such as emails, PDFs, photos, videos, audios, SM
posts, and so much more. Variety is one of the important characteristics of big data.

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 b) Velocity
Velocity essentially refers to the speed at which data is being created in real-time. In a broader
prospect, it comprises the rate of change, linking of incoming data sets at varying speeds, and
activity bursts.
c) Volume
Volume is one of the characteristics of big data. We already know that Big Data indicates huge
‘volumes’ of data that is being generated on a daily basis from various sources like social media
platforms, business processes, machines, networks, human interactions, etc. Such a large amount
of data is stored in data warehouses. Thus comes to the end of characteristics of big data.

Why is Big Data Important?

The importance of big data does not revolve around how much data a company has but how a
company utilizes the collected data. Every company uses data in its own way; the more efficiently
a company uses its data, the more potential it has to grow. The company can take data from any
source and analyze it to find answers which will enable:

1. Cost Savings: Some tools of Big Data like Hadoop and Cloud-Based Analytics can
bring cost advantages to business when large amounts of data are to be stored and these
tools also help in identifying more efficient ways of doing business.
2. Time Reductions: The high speed of tools like Hadoop and in-memory analytics can
easily identify new sources of data which helps businesses analyzing data immediately
and make quick decisions based on the learning.
3. Understand the market conditions: By analyzing big data you can get a better
understanding of current market conditions. For example, by analyzing customers’
purchasing behaviors, a company can find out the products that are sold the most and
produce products according to this trend. By this, it can get ahead of its competitors.
4. Control online reputation: Big data tools can do sentiment analysis. Therefore, you
can get feedback about who is saying what about your company. If you want to monitor
and improve the online presence of your business, then, big data tools can help in all
this.
5. Using Big Data Analytics to Boost Customer Acquisition and Retention
The customer is the most important asset any business depends on. There is no single
business that can claim success without first having to establish a solid customer base.
However, even with a customer base, a business cannot afford to disregard the high
competition it faces. If a business is slow to learn what customers are looking for, then
it is very easy to begin offering poor quality products. In the end, loss of clientele will
result, and this creates an adverse overall effect on business success. The use of big data
allows businesses to observe various customer related patterns and trends. Observing
customer behavior is important to trigger loyalty.
6. Using Big Data Analytics to Solve Advertisers Problem and Offer Marketing
Insights

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 Big data analytics can help change all business operations. This includes the ability to
match customer expectation, changing company’s product line and of course ensuring
that the marketing campaigns are powerful.
7. Big Data Analytics As a Driver of Innovations and Product Development
Another huge advantage of big data is the ability to help companies innovate and
redevelop their products.

Business Intelligence vs Big Data

Although Big Data and Business Intelligence are two technologies used to analyze data to help
companies in the decision-making process, there are differences between both of them. They differ
in the way they work as much as in the type of data they analyze.

Traditional BI methodology is based on the principle of grouping all business data into a central
server. Typically, this data is analyzed in offline mode, after storing the information in an
environment called Data Warehouse. The data is structured in a conventional relational database
with an additional set of indexes and forms of access to the tables (multidimensional cubes).

A Big Data solution differs in many aspects to BI to use. These are the main differences between
Big Data and Business Intelligence:

1. In a Big Data environment, information is stored on a distributed file system, rather than
on a central server. It is a much safer and more flexible space.
2. Big Data solutions carry the processing functions to the data, rather than the data to the
functions. As the analysis is centered on the information, it´s easier to handle larger
amounts of information in a more agile way.
3. Big Data can analyze data in different formats, both structured and unstructured. The
volume of unstructured data (those not stored in a traditional database) is growing at levels
much higher than the structured data. Nevertheless, its analysis carries different challenges.
Big Data solutions solve them by allowing a global analysis of various sources of
information.
4. Data processed by Big Data solutions can be historical or come from real-time sources.
Thus, companies can make decisions that affect their business in an agile and efficient way.
5. Big Data technology uses parallel mass processing (MPP) concepts, which improves the
speed of analysis. With MPP many instructions are executed simultaneously, and since the
various jobs are divided into several parallel execution parts, at the end the overall results
are reunited and presented. This allows you to analyze large volumes of information
quickly.

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 Big Data vs Data Warehouse

Big Data has become the reality of doing business for organizations today. There is a boom in the
amount of structured as well as raw data that floods every organization daily. If this data is
managed well, it can lead to powerful insights and quality decision making.

Big data analytics is the process of examining large data sets containing a variety of data types to
discover some knowledge in databases, to identify interesting patterns and establish relationships
to solve problems, market trends, customer preferences, and other useful information. Companies
and businesses that implement Big Data Analytics often reap several business benefits. Companies
implement Big Data Analytics because they want to make more informed business decisions.

A data warehouse (DW) is a collection of corporate information and data derived from operational
systems and external data sources. A data warehouse is designed to support business decisions by
allowing data consolidation, analysis and reporting at different aggregate levels. Data is populated
into the Data Warehouse through the processes of extraction, transformation and loading (ETL
tools). Data analysis tools, such as business intelligence software, access the data within the
warehouse.

Hadoop Environment Big Data Analytics

Hadoop is changing the perception of handling Big Data especially the unstructured data. Let’s
know how Apache Hadoop software library, which is a framework, plays a vital role in handling
Big Data. Apache Hadoop enables surplus data to be streamlined for any distributed processing
system across clusters of computers using simple programming models. It truly is made to scale
up from single servers to a large number of machines, each and every offering local computation,
and storage space. Instead of depending on hardware to provide high-availability, the library itself
is built to detect and handle breakdowns at the application layer, so providing an extremely
available service along with a cluster of computers, as both versions might be vulnerable to
failures.

Hadoop Community Package Consists of

 File system and OS level abstractions
 A MapReduce engine (either MapReduce or YARN)
 The Hadoop Distributed File System (HDFS)
 Java ARchive (JAR) files
 Scripts needed to start Hadoop
 Source code, documentation and a contribution section

Activities performed on Big Data

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  Store – Big data need to be collected in a seamless repository, and it is not necessary to
store in a single physical database.
 Process – The process becomes more tedious than traditional one in terms of cleansing,
enriching, calculating, transforming, and running algorithms.
 Access – There is no business sense of it at all when the data cannot be searched, retrieved
easily, and can be virtually showcased along the business lines.

Classification of analytics

Descriptive analytics
Descriptive analytics is a statistical method that is used to search and summarize historical data in
order to identify patterns or meaning.

Data aggregation and data mining are two techniques used in descriptive analytics to discover
historical data. Data is first gathered and sorted by data aggregation in order to make the datasets
more manageable by analysts.

Data mining describes the next step of the analysis and involves a search of the data to identify
patterns and meaning. Identified patterns are analyzed to discover the specific ways that learners
interacted with the learning content and within the learning environment.

Advantages:

 Quickly and easily report on the Return on Investment (ROI) by showing how performance
achieved business or target goals.

 Identify gaps and performance issues early - before they become problems.

 Identify specific learners who require additional support, regardless of how many students
or employees there are.

 Identify successful learners in order to offer positive feedback or additional resources.

 Analyze the value and impact of course design and learning resources.

Predictive analytics

Predictive Analytics is a statistical method that utilizes algorithms and machine learning to identify
trends in data and predict future behaviors

The software for predictive analytics has moved beyond the realm of statisticians and is becoming
more affordable and accessible for different markets and industries, including the field of learning
& development.

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 For online learning specifically, predictive analytics is often found incorporated in the Learning
Management System (LMS), but can also be purchased separately as specialized software.

For the learner, predictive forecasting could be as simple as a dashboard located on the main screen
after logging in to access a course. Analyzing data from past and current progress, visual indicators
in the dashboard could be provided to signal whether the employee was on track with training
requirements.

Advantages:

 Personalize the training needs of employees by identifying their gaps, strengths, and
weaknesses; specific learning resources and training can be offered to support individual
needs.

 Retain Talent by tracking and understanding employee career progression and forecasting
what skills and learning resources would best benefit their career paths. Knowing what skills
employees need also benefits the design of future training.

 Support employees who may be falling behind or not reaching their potential by offering
intervention support before their performance puts them at risk.

 Simplified reporting and visuals that keep everyone updated when predictive forecasting
is required.

Prescriptive analytics
Prescriptive analytics is a statistical method used to generate recommendations and make decisions
based on the computational findings of algorithmic models.

Generating automated decisions or recommendations requires specific and unique algorithmic

models and clear direction from those utilizing the analytical technique. A recommendation cannot
be generated without knowing what to look for or what problem is desired to be solved. In this
way, prescriptive analytics begins with a problem.

Example
A Training Manager uses predictive analysis to discover that most learners without a particular
skill will not complete the newly launched course. What could be done? Now prescriptive analytics
can be of assistance on the matter and help determine options for action. Perhaps an algorithm can
detect the learners who require that new course, but lack that particular skill, and send an automated
recommendation that they take an additional training resource to acquire the missing skill.

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 The accuracy of a generated decision or recommendation, however, is only as good as the quality
of data and the algorithmic models developed. What may work for one company’s training needs
may not make sense when put into practice in another company’s training department. Models are
generally recommended to be tailored for each unique situation and need.

Descriptive vs Predictive vs Prescriptive Analytics

Descriptive Analytics is focused solely on historical data.

You can think of Predictive Analytics as then using this historical data to develop statistical models
that will then forecast about future possibilities.

Prescriptive Analytics takes Predictive Analytics a step further and takes the possible forecasted
outcomes and predicts consequences for these outcomes.

What Big Data Analytics Challenges

1. Need For Synchronization Across Disparate Data Sources

As data sets are becoming bigger and more diverse, there is a big challenge to incorporate them
into an analytical platform. If this is overlooked, it will create gaps and lead to wrong messages
and insights.

2. Acute Shortage Of Professionals Who Understand Big Data Analysis

The analysis of data is important to make this voluminous amount of data being produced in every
minute, useful. With the exponential rise of data, a huge demand for big data scientists and Big
Data analysts has been created in the market. It is important for business organizations to hire a
data scientist having skills that are varied as the job of a data scientist is multidisciplinary. Another
major challenge faced by businesses is the shortage of professionals who understand Big Data
analysis. There is a sharp shortage of data scientists in comparison to the massive amount of data
being produced.

3. Getting Meaningful Insights Through The Use Of Big Data Analytics

It is imperative for business organizations to gain important insights from Big Data analytics, and
also it is important that only the relevant department has access to this information. A big challenge
faced by the companies in the Big Data analytics is mending this wide gap in an effective manner.

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 4. Getting Voluminous Data Into The Big Data Platform

It is hardly surprising that data is growing with every passing day. This simply indicates that
business organizations need to handle a large amount of data on daily basis. The amount and
variety of data available these days can overwhelm any data engineer and that is why it is
considered vital to make data accessibility easy and convenient for brand owners and managers.

5. Uncertainty Of Data Management Landscape

With the rise of Big Data, new technologies and companies are being developed every day.
However, a big challenge faced by the companies in the Big Data analytics is to find out which
technology will be best suited to them without the introduction of new problems and potential
risks.

6. Data Storage And Quality

Business organizations are growing at a rapid pace. With the tremendous growth of the companies
and large business organizations, increases the amount of data produced. The storage of this
massive amount of data is becoming a real challenge for everyone. Popular data storage options
like data lakes/ warehouses are commonly used to gather and store large quantities of unstructured
and structured data in its native format. The real problem arises when a data lakes/ warehouse try
to combine unstructured and inconsistent data from diverse sources, it encounters errors. Missing
data, inconsistent data, logic conflicts, and duplicates data all result in data quality challenges.

7. Security And Privacy Of Data

Once business enterprises discover how to use Big Data, it brings them a wide range of possibilities
and opportunities. However, it also involves the potential risks associated with big data when it
comes to the privacy and the security of the data. The Big Data tools used for analysis and storage
utilizes the data disparate sources. This eventually leads to a high risk of exposure of the data,
making it vulnerable. Thus, the rise of voluminous amount of data increases privacy and security
concerns.

Terminologies Used In Big Data Environments

 As-a-service infrastructure
Data-as-a-service, software-as-a-service, platform-as-a-service – all refer to the idea that rather
than selling data, licences to use data, or platforms for running Big Data technology, it can be
provided “as a service”, rather than as a product. This reduces the upfront capital investment

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 necessary for customers to begin putting their data, or platforms, to work for them, as the provider
bears all of the costs of setting up and hosting the infrastructure. As a customer, as-a-service
infrastructure can greatly reduce the initial cost and setup time of getting Big Data initiatives up
and running.

 Data science
Data science is the professional field that deals with turning data into value such as new insights
or predictive models. It brings together expertise from fields including statistics, mathematics,
computer science, communication as well as domain expertise such as business knowledge. Data
scientist has recently been voted the No 1 job in the U.S., based on current demand and salary and
career opportunities.
 Data mining
Data mining is the process of discovering insights from data. In terms of Big Data, because it is so
large, this is generally done by computational methods in an automated way using methods such
as decision trees, clustering analysis and, most recently, machine learning. This can be thought of
as using the brute mathematical power of computers to spot patterns in data which would not be
visible to the human eye due to the complexity of the dataset.
 Hadoop
Hadoop is a framework for Big Data computing which has been released into the public domain
as open source software, and so can freely be used by anyone. It consists of a number of modules
all tailored for a different vital step of the Big Data process – from file storage (Hadoop File System
– HDFS) to database (HBase) to carrying out data operations (Hadoop MapReduce – see below).
It has become so popular due to its power and flexibility that it has developed its own industry of
retailers (selling tailored versions), support service providers and consultants.
 Predictive modelling
At its simplest, this is predicting what will happen next based on data about what has happened
previously. In the Big Data age, because there is more data around than ever before, predictions
are becoming more and more accurate. Predictive modelling is a core component of most Big Data
initiatives, which are formulated to help us choose the course of action which will lead to the most
desirable outcome. The speed of modern computers and the volume of data available means that
predictions can be made based on a huge number of variables, allowing an ever-increasing number
of variables to be assessed for the probability that it will lead to success.
 MapReduce
MapReduce is a computing procedure for working with large datasets, which was devised due to
difficulty of reading and analysing really Big Data using conventional computing methodologies.
As its name suggest, it consists of two procedures – mapping (sorting information into the format
needed for analysis – i.e. sorting a list of people according to their age) and reducing (performing
an operation, such checking the age of everyone in the dataset to see who is over 21).

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