What is Big Data Analytics Types, Application and why its Important?

Introduction to Big Data
What is Big Data
Types of data
6 Vs of big data
Challenges in Big data
Big Data Technologies
Hadoop Introduction
Distributed Computing
So why Hadoop?
Challenges of SuperComputing
Hadoop History
Hadoop Framework
How Data Analytics can help small businesses?

Big data – Introduction

Introduction to big data: Big data analytics refers to data sets that are too large and complex to be processed using traditional methods. Big data is often used in fields such as medicine, finance, and marketing. In medicine, big data can be used to track the spread of diseases. In finance, big data can be used to predict market trends. And in marketing, big data can be used to target potential customers. This blog helps you to understand the concepts like what is big data, why big data, Is it worth learning big data technologies and as professionals, we will get paid high and many more.

What is Big data?

As the name implies, big data is data with huge size. We get a large amount of data in different forms from different sources and in huge volume, velocity, variety and etc which can be derived from human or machine sources.

We are talking about data and let us see what are the types of data to understand the logic behind big data, learn more about the Big Data Landscape.

Types of data:

Three types of data can be classified as:

Structured data: Data which is represented in a tabular form. The data can be stored, accessed and processed in the form of fixed format. Ex: databases, tables

Semi structured data: Data which does not have a formal data model Ex: XML files

Unstructured data: data which does not have a pre-defined data model Ex: Text files, web logs.

Let us dig on 6 Vs of big data:

Volume: The amount of data from various sources like in TB, PB, ZB etc. It is a rise of bytes we are nowhere in GBs now.

Velocity: High frequency data like in stocks. The speed at which big data is generated.

Veracity: Refers to the biases, noises and abnormality in data.

Variety: Refers to the different forms of data. Data can come in various forms and shapes, like visuals data like pictures, and videos, log data etc. This can be the biggest problem to handle for most businesses.

Variability: to what extent, and how fast, is the structure of your data changing? And how often does the meaning or shape of your data change?

Value: This describes what value you can get from which data, how big data will get better results from stored data.

Challenges in Big data:

Complex: No proper understanding of the underlying data

Storage: How to accommodate large amounts of data in a single physical machine.

Performance: How to process large amounts of data efficiently and effectively so as to increase the performance.

Big Data Technologies:

Big Data is broad and surrounded by many trends and new technology developments, the top emerging technologies given below are helping users cope with and handle Big Data in a cost-effective manner.

1. Apache Hadoop

2. Apache Spark

3. Apache Hive

There are many other technologies. But we will learn about the above 3 technologies In detail.

If you are new to the world of Big Data, check out this Big Data Analytics course by Great Learning Academy.

Hadoop Introduction:

Hadoop is a distributed parallel processing framework, which facilitates distributed computing

Now to dig more on Hadoop, we need to have understanding on “Distributed Computing”. This will actually give us a root cause of the Hadoop.

Distributed Computing

In simple English, distributed computing is also called parallel processing. Let’s take an example, let’s say we have a task of painting a room in our house, and we will hire a painter to paint and may approximately take 2 hours to paint one surface. Let’s say we have 4 walls and 1 ceiling to be painted and this may take one day(~10 hours) for one man to finish, if he does this non stop.

The same thing to be done by 4 or 5 more people can take half a day to finish the same task. This is the simple real time problem to understand the logic behind distributed computing

Now let’s take an actual data related problem and analyse the same.

Look at how Predictive Analytics is used in the Travel Industry.

We have an input file of lets say 1 GB and we need to calculate the sum of these numbers together and the operation may take 50secs to produce a sum of numbers

Then let’s take the same example by dividing the dataset into 2 parts and give the input to 2 different machines, then the operation may take 25 secs to produce the same sum results.

This is the fundamental idea of parallel processing.

So why Hadoop?

The idea of parallel processing was not something new!

The idea ws existing since long back in the time of Super computers (back in 1970s)

There we used to have army of network engineers and cables required in manufacturing supercomputers and there are still few research organizations which use these kind of infrastructures which is called as “super Computers”

Lets see what were the challenges of SuperComputing.

• A general purpose operating system like framework for parallel computing needs did not exist

• Companies procuring supercomputers were locked to specific vendors for hardware support

• High initial cost of the hardware.

• Develop custom software for individual use cases

• High cost of software maintenance and upgrades which had to be taken care in house the organizations using a supercomputer.

• Not simple to scale horizontally

There should be a better reason always!

HADOOP comes to rescue

• A general purpose operating system like framework for parallel computing needs

• Its free software (open source) with free upgrades

• Has options for upgrading the software and its free !

• Opens up the power of distributed computing to a wider set of audience.

• Mid sized organizations need not be locked to specific vendors for hardware support – Hadoop works on commodity hardware

• The software challenges of the organization having to write proprietary softwares is no longer the case.

Data is everywhere. People upload videos, take pictures, use several apps on their phones, search the web and more. Machines too, are generating and keeping more and more data. Existing tools are incapable of processing such large data sets. Hadoop and large-scale distributed data processing, in general, is rapidly becoming an important skill set for many programmers. Hadoop is an open-source framework for writing and running distributed applications that process large amounts of data. This course introduces Hadoop in terms of distributed systems as well as data processing systems. With this course, get an overview of the MapReduce programming model using a simple word counting mechanism along with existing tools that highlight the challenges around processing data at a large scale. Dig deeper and implement this example using Hadoop to gain a deeper appreciation of its simplicity.

Hadoop History:

The need of the hour was scalable search engine for the growing internet
Internet Archive search director Doug Cutting and University of Washington graduate student Mike Cafarella set out to build a search engine and the project named NUTCH in the year 2001-2002
Google’s distributed file system paper came out in 2003 & first file map-reduce paper came out in 2004
In 2006 Dough Cutting joined YAHOO and created an open source framework called HADOOP (name of his son’s toy elephant) HADOOP traces back its root to NUTCH, Google’s distributed file system and map-reduce processing engine.
It went to become a full fledged Apache project and a stable version of Hadoop was used in Yahoo in the year 2008

Hadoop Framework: Stepping into Hadoop.

Let us look at some Key terms used while discussing Hadoop.

● Commodity hardware: PCs which can be used to make a cluster

● Cluster/grid: Interconnection of systems in a network

● Node: A single instance of a computer

● Distributed System: A system composed of multiple autonomous computers that communicate through a computer network

● ASF: Apache Software Foundation

● HA: High Availability

● Hot stand-by : Uninterrupted failover whereas cold stand-by will be there will be noticeable delay. If the system goes down, you will have to reboot.

How Data Analytics can help small businesses?

There’s a famous quote by Bill Gates, Founder of Microsoft. He says: “If your business is not on the Internet, soon your business will be out of business.” With these words, Bill Gates sums up the importance of every business, regardless of its size and nature, having an online footprint and presence.

Digital Marketing Isn’t Enough

However, in today’s world and especially with the boom in e-commerce during the COVID-19 pandemic, merely having a good website, Facebook page, YouTube channel, or other social media presence isn’t enough. You need to know where your potential market lies, who and how to target as clients and likes and dislikes of the areas where you’re focusing all energies of your online strategies.

Therefore, data analytics is a vital component for every online marketing strategy, including for small businesses.So, learn an introduction to data analytics course for free and upskill.

In this article, let’s explore how data analytics can help small businesses grow. And how to sustain that growth for tangible results.

How Data Analytics Helps Small Businesses

As a small business in India, you’ll surely be aware of the challenges it faces. These include erratic logistics, seasonal demands, and the different ethnic groups in this land and above all, economic swings that affect buying patterns, among others.

Therefore, here’s how data analytics can help.

1. Understanding Demographics

The key to the success of any online business is to understand the demographics of your present or intended market. While a small business could be targeting a specific region, there could be no demand from clients. Therefore, you won’t be able to make much headway in that market.

There are wonderful small business ideas that you can get online. And when you open a small business using any of these superb ideas, data analysis can win you a niche in the market within a short time.

At the same time, your products or services could be useful and generate great demand in another, hitherto unknown market. Most Indian small businesses remain blissfully unaware of where their real market lies. Therefore, most end up targeting what may seem like an obvious and profitable market but fetch disastrous results.

Here’s where data analytics helps. By analyzing the number of people that visit your website and social media pages from any particular region, you can understand the demographics of your market. With proper planning and a clear strategy, you can focus your efforts on that segment of the population and propel your small business to profits.

2. Cost per Client

Is your small business barking up the wrong tree? Meaning, are you targeting the wrong clientele despite spending a considerable amount of money on digital marketing resources and advertising? If yes, then it’s high time to engage in data analytics and find out exactly how much money you’re wasting on getting potential leads and generating them as customers.

Since digital marketing and online ads are much more economical than conventional print and electronic media advertising, most small businesses happily bear the expense. However, it’s worth remembering that profits from your customers have to also cover the money you’re spending on attracting them to your small business.

By data analysis, you can find how much money you’re spending on getting a customer and the profits. And when you find that the cost is greater than profits, it’s easier to adapt, fine-tune, or scrap your digital marketing strategy. Instead, you can expend the same resources on developing newer, more profitable markets.

3. Catering to Seasonal Demands

As a land of festivals, the Indian market witnesses seasonal demands. Naturally, competition in every form gets hotter and hotter during festivals and shopping seasons. At the same time, the tastes of Indian buyers are prone to wild swings. What was in demand last year needn’t be in vogue this year or in years to come.

How does one find out what Indian shoppers are attracted to during the forthcoming festival or shopping season? Once again, the clues lie in data analysis. By analyzing what shoppers are looking for, based on their search keywords as well as hits to other websites, Google Trends, and data from various sources, your small business can specifically identify their needs.

A small business cannot have a large inventory of goods. Hence, data analytics helps you identify exactly what products or services are in demand today and which are the bestsellers. That helps cut inventory costs while optimizing profitability if done rightly.

4. Keeping Tabs on Competitors

Just in case you’re unaware, data analytics can give you a lead and head-start over competitors. There are numerous ways a good data analytics expert can find out what type of clientele are your closest rivals attracting and what they’re buying, average spend per purchase, and profits.

This is precious information for every small business if you can use it to your advantage. You can know where to strike a competitor and gain that leading edge to emerge at the top in that particular field of business. It helps you focus every digital marketing effort, lower costs, offer the right products and services, and gain a larger clientele.

This isn’t as easy as it might sound. Data analysis isn’t that proverbial cakewalk. It needs experienced and qualified persons to provide you astute information about rivals and their markets.

5. Improving Offerings of Small Business

American magnate Peter F. Drucker aptly says: “Every business has only two functions: marketing and innovation.” And innovation is made possible by data analysis. You might falsely believe that your small business has that killer service or product that meets the needs of your target market. But that needn’t be the case. There’s every possibility that offerings of your small business could be outdated or even irrelevant in the market.

The only way to turntables is by innovating your brand offerings to suit existing market conditions. Data analysis makes it possible to know what people look for in a product or service. It helps identify deficiencies in your offerings and innovate them to make them saleable in the market.

In Conclusion

Data analysis helps you improve customer service, cut costs on digital marketing and advertising efforts, and gain more customers if done professionally. Unfortunately in India, few small businesses engage in data analysis due to costs. If you invest in data analysis, your small business could gain a lot.

If you found this helpful and wish to learn more such concepts, join Great Learning Academy’s free online course.

Contributed by: Mitali Roy

If you are looking to pick up Big Data Analytics skills, you should check out GL Academy’s Big Data Analytics Free Course. This course is specially designed for beginners and will help you learn all the concepts.

Data Science and Machine Learning from MIT

PG in Data Science & Business Analytics from UT Austin