What Is Big Data Analytics?
What Is Big Data Analytics?
Big Data Analytics is a complete process of examining large
sets of data through varied tools and processes in order to discover unknown
patterns, hidden correlations, meaningful trends, and other insights for making
data-driven decisions in the pursuit of better results.
Big Data Types
Big Data is primarily measured by the volume of the data.
But along with that, Big Data also includes data that is coming in fast and at
huge varieties. Primarily, there are three types of Big Data, namely:
Structured Data
Unstructured Data
Semi-structured Data
Big Data can be measured in terms of terabytes and more.
Sometimes, Big Data can cross over petabytes. The structured data includes all
the data that can be stored in a tabular column. The unstructured data is the
one that cannot be stored in a spreadsheet; and semi-structured data is
something that does not conform with the model of the structured data. You can
still search semi-structured data just like structured data, but it does not
offer the ease with which you can do it on the structured data.
The structured data can be stored in a tabular column.
Relational databases are examples of structured data. It is easy to make sense
of the relational databases. Most of the modern computers are able to make
sense of structured data.
Unstructured data, on the other hand, is the one which
cannot be fit into tabular databases. Examples of unstructured data include
audio, video, and other sorts of data which comprise such a big chunk of the
Big Data today.
The semi-structured data includes both structured and
unstructured data. This type of data sets include a proper structure, but still
it might not be possible to sort or process that data due to some constraints.
This type of data includes the XML data, JSON files, and others.
Check out this insightful video on Big Data Analytics for
beginners:
Processing Big Data
In order to process Big Data, you need to have cloud and
physical machines as well. Today, due to the advancements in the technology, we
might include Cloud Computing and Artificial Intelligence within the ambit of
Big Data processing. Due to all these advancements, manual inputs can be
reduced and automation can take over.
Data Analytics refers to the set of quantitative and
qualitative approaches to derive valuable insights from data. It involves many
processes that include extracting data, categorizing it in order to analyze
various patterns, relations, and connections, and gathering other such valuable
insights from it.
Today, almost every organization has morphed itself into a
data-driven organization, and this means that they are deploying a data-driven
approach in order to collect more data that is related to the customers,
markets, and business processes. This data is then categorized, stored, and
analyzed to make sense out of it and derive valuable insights from it.
Understanding Big Data Analytics
With Big Data Analytics, you can answer a new range of
diagnostic questions about your business needs. It provides more data and
sophisticated analytics to deliver actionable results to your business teams.
You may start with a general question, one your traditional descriptive
analytics has revealed.
Further, Big Data Analytics lets you explore deeper
diagnostic questions—some of which you might not have even thought of asking—to
reveal a new level of insight and identify steps that have to be taken to
improve business performance. Many definitions on the topic of Big Data focus
on a bottom-up view, using the three Vs of data—volume, variety, and velocity.
Check this Intellipaat R tutorial that helps learn Big Data
Analytics with R!
The term ‘Big Data Analytics’ might look simple, but there
are large number of processes which are comprised in Big Data Analytics. We can
think of Big Data as one which has huge volume, velocity, and variety. Big Data
Analytics tools can make sense of the huge volumes of data and convert it into
valuable business insights.
Though the term ‘Big Data Analytics’ might seem simple, it
is anything but simple. Data Analytics is most complex when it is deployed for
Big Data applications. The three most important attributes of Big Data include
volume, velocity, and variety.
The need for Big Data Analytics comes from the fact that we
are generating data at extremely high speeds and every organization needs to
make sense of this data. As per confirmed sources, by the year 2020, we will be
generating a staggering 1.7 MB of data every second, contributed by every
individual on earth.
All this tells us the importance of Big Data Analytics for
making sense of all the huge volumes of data. Big Data Analytics helps us
organize, transform, and model the data based on the requirements of an
organization and identify patterns and draw conclusions from it.
Watch this insightful video to find out what a Big Data
Analyst does in real life:
The larger the size of the data the bigger the problem. So,
Big Data may be defined as the data where the size of it itself poses the
problem and it needs newer ways of handling the same. The analysis of data that
is at high volume, velocity, and variety means that the traditional methods of
working with the data would not apply here.
Types of Big Data Analytics
Prescriptive Analytics: This is the type of analytics talks
about an analysis, which is based on the rules and recommendations, to
prescribe a certain analytical path for the organization. At the next level,
prescriptive analytics will automate decisions and actions—how can I make it
happen? Building upon the previous analytics, neural networks and heuristics
are applied to the data to recommend the best possible actions that derive
desired outcomes.
Predictive Analytics: This type of analytics ensures that
the path is predicted for the future course of action. Answering the how and
why questions will reveal specific patterns to detect when outcomes are about
to occur. Predictive analytics builds upon the diagnostic analytics to look for
these patterns and see what is going to happen. Machine Learning is also
applied to continuously learn as new patterns emerge.
Descriptive Analytics: In this type of analytics, we work
based on the incoming data. For the mining of this data, we deploy analytics
and come up with a description based on the data. Many organizations have spent
years generating descriptive analytics—answering the ‘what happened’ questions.
This information is valuable, but only provides a high-level, rearview mirror
view of the business performance. In Diagnostic Analytics, most organizations
start to apply Big Data Analytics to answer diagnostic questions—how and why
something happened. Some might also call these behavioral analytics.
Diagnostic Analytics: This is about looking into the past
and determining why a certain thing happened. This type of analytics usually
revolves around working on a dashboard. Diagnostic Analytics with Big Data
helps in two ways: (a) the additional data brought by the digital age
eliminates analytic blind spots, and (b) the how and why questions deliver
insights that pinpoint the actions need to be taken.
Regardless of the type of Big Data Analytics you want to
deploy, algorithms play a key role. Read this insightful blog to find out more.
How Does Big Data Analytics Help Derive Business Insights?
There are various tools in Big Data Analytics that can be
successfully deployed in order to parse data and derive valuable insights out
of it. The computational and data-handling challenges that are faced at scale
mean that the tools need to be specifically able to work with such kinds of
data.
The advent of Big Data changed analytics forever, thanks to
the inability of the traditional data handling tools like relational database
management systems to work with Big Data in its varied forms. Also, data
warehouses could not handle data of extremely big size.
The era of Big Data drastically changed the requirements for
extracting meaning from business data. In the world of relational databases,
administrators easily generated reports on data contents for business use, but
these provided little or no broad business intelligence. For that, they
employed data warehouses, but data warehouses generally cannot handle the scale
of Big Data, cost-effectively.
While data warehouses are certainly a relevant form of Data
Analytics, the term ‘Data Analytics’ is slowly acquiring a specific subtext
related to the challenge of analyzing data of massive volume, variety, and
velocity. Check this informative blog that talks about how Big Data Analytics
is driving the best Formula 1 teams ahead.
Databases for Big Data Analytics
Non-relational Databases
Non-relational databases are used for working with
unstructured data. Here, the data cannot be stored in the regular tabular
column. JSON files and XML are some of the most important unstructured data
types. With JSON, you can write tasks in the application layer and this allows
enhanced cross-platform functionalities.
In-memory Databases
When it comes to Big Data processing engines like Hadoop, the
speed at which the processing happens is extremely low, thanks to the constant
read and write access that is needed with respect to disk storage. But with the
high-speed in-memory processing, you can do read and write at a much higher
pace. This is where the in-memory processing engines like Apache Spark and SAP
HANA come into the picture.
Hadoop Hybrid: Data Storage and Processing
You can think of Hadoop as a hybrid processing engine that
can work for both data storage and processing systems. The storage arm of
Hadoop is the Hadoop Distributed File System, and the processing arm of Hadoop
is MapReduce. Due to the need for hybrid processing engines in today’s
digitally disruptive world, Hadoop is finding increased acceptance. Apache
Hadoop is a hybrid data storage and processing tool that can be harnessed even
by small organizations since it is part of the open-source platform.
Importance of Data Mining
Data mining can be used for reducing costs and increasing
revenues. Data mining is one of the fundamental steps in the Data Analytics
process. It is the step wherein you perform the Extract, Transform, and Load
for getting the right data into data warehouses. It also takes on the task of
storing and managing data based in multidimensional databases. Within data
mining, we have some recent phenomena that are based on contextual analyzing of
big data sets to discover the relationship between separate data items. The
objective is to use a single data set for different purposes by different
users. Finally, data mining is also assigned with the task of presenting the
data which has been analyzed in a simple yet effective way.
Top Tools Used in Big Data Analytics
In this section, we will be familiarizing you with various
aspects of the Big Data Analytics domain. Here, we include a list of analytical
courses that you can take up:
Apache Spark: Spark is a framework for real-time Data
Analytics which is part of the Hadoop ecosystem.
Python: This is one of the most versatile programming
languages that is rapidly being deployed for various applications including
Machine Learning.
SAS: SAS is an advanced analytical tool that is being used
for working with huge volumes of data and deriving valuable insights from it.
Hadoop: It is the most popular Big Data framework that is
being deployed by some of the widest range of organizations from around the
world for making sense of big data.
SQL: This is the structured query language that is used for
working with relational database management systems.
Tableau: This is the most popular Business Intelligence tool
that is deployed for the purpose of data visualization and business analytics.
Splunk: Splunk is the tool of choice for parsing the
machine-generated data and deriving valuable business insights out of it.
R Programming: R is the Number 1 programming language that
is being used by Data Scientists for the purpose of statistical computing and
graphical applications alike.
Watch this insightful video to learn more about the job role
of a Data Analyst:
Major Sectors Using Big Data Analytics
Retail
The retail industry is actively deploying Big Data
Analytics. They are applying the techniques of Data Analytics to understand
what the consumers are buying and offering products and services that are
tailor-made for these customers. Today, it is all about having an omni-channel
experience. Customers might make contact with a brand on one channel, then
finally buy it through another channel, meanwhile going through more
intermediary channels. Retailer will have to keep track of these customer
journeys, and they must deploy their marketing and advertising campaigns based
on that in order to improve the chances of sales and lower costs.
Technology
Technology companies, offering products and services, are
also heavily deploying Big Data Analytics. They are finding out more how the
customers interact with their websites or apps and gather key information.
Based on this, they are able to optimize their sales, customer service, improve
customer satisfaction, and more. This also helps them launch new products and
services since today we are living in a knowledge-intensive economy, and the
enterprises in the technology sector are reaping the benefits of Big Data
Analytics.
Healthcare
Healthcare is another industry that can benefit a lot from
Big Data Analytics tools, techniques, and processes. Healthcare personnel can
diagnose the health of their patients through various tests, run it through
their computers, look for telltale signs of anomalies and maladies, and more.
Big Data Analytics also helps improve patient care and increase the efficiency
of the treatment and medication processes. Some diseases can be diagnosed
before its onset so that the measures can be taken in a preventive manner
rather than a remedial manner.
Manufacturing
Manufacturing is an industrial sector that is involved with
developing physical goods. The life cycle of a manufacturing process can vary
from product to product. The manufacturing systems are involved within the
industry setup and across the manufacturing floor. There are a lot of
technologies that are involved like Internet of Things, Robotics, and others,
but the backbone of each of these is firmly based on Big Data Analytics. Using
Big Data Analytics, manufacturers can improve the yield, reduce the time to
market, enhance the quality, optimize the supply chain and logistics process,
and build prototypes before the launch of products so as to understand all the
implications. Throughout all these steps, Big Data Analytics helps the
manufacturers.
Energy
Most of the oil and gas companies which come under the
energy sector are big users of Big Data Analytics. When it comes to discovering
oil and resources, a lot of Big Data Analytics is deployed. Also, the market is
very volatile for the fossil fuels. So, there is tremendous amounts of Big Data
Analytics that goes into finding out what the price of a barrel of oil will be,
what the output should be, and if an oil well will be profitable or not. Big
Data Analytics is also deployed in finding out the equipment failures, deploy
predictive maintenance, and optimally use the resources in order to reduce the
capital expenditure.[Source]-https://intellipaat.com/blog/big-data-analytics/
Asterix
Solution’s big data course is designed to help applications scale up from single servers to
thousands of machines. With the rate at which memory cost decreased the
processing speed of data never increased and hence loading the large set of
data is still a big headache and here comes Hadoop as the solution for it.
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