Big Data - Crucial to store and Harder to manage

World have reached to yottabytes (approximately 1,000 zettabytes) of Data that can't be stored, Process and analysed at faster rate at same system even no system exists with such configurations it's also take large cost to create and maintain such Storage Appliance. Due to Input/Optput and Size issues (we will discuss in this article).

This huge amount of Data is coming from social Media Platforms and businesses. Approx 12300+ TB data per day. So this much huge data is turn out to be Big Data Problem for MNCs but on the other hand the data is key of their business on which they perform multiple ananysis, and processes.

MNCs leverage big data to drive there business and getting data faster to market is their primary need. The range of industry giants like Google, Amazon, Facebook, Microsoft to the smaller businesses which have put big data as center of their business model, like Kaggle, Cornerstone, Altoros e.t.c.

When we jump out to the Technical Definition of Bigdata it's like "Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation. "

Let's now jump to the problems that contributes to bigdata:-

🔶 Volume -> the huge amount of data that is produced each day by companies.

🔶 Variety -> It refers to the diversity of data types and data sources.

🔶 Velocity -> refers to the speed with which the data is generated, analyzed and reprocessed.

🔶 Validity -> It guarantees of data quality or, alternatively, Veracity is the authenticity and credibility of the data.

🔶 Value -> It denotes the added value for companies. Many companies have recently established their own data platforms, filled their data pools and invested a lot of money in infrastructure.

Big Data - Big Advantage

The MNCs get advantage of Data in Association and Recommendation of products as per the user data. Like Amazon Shopping, Big Basket, FlipKart give recommendations offers and sales according to the Data Log they Use.

How Big Data is crucial for MN C's and how they work on it

-> GOOGLE

Google influenced the way we can now analyse big data (think MapReduce, BigQuery, etc.) – > they are probably more responsible than anyone else for making it part of our everyday lives. Many of the innovative things Google is doing today, Google builds up vast amounts of data about the people using Profiles and Gmail Messages .

In 2010 Google launched BigQuery, its commercial service for allowing companies to store and analyse big data sets on its cloud platforms. Companies pay for the storage space and computer time taken in running the queries. Also working on  self-driving cars.

-> AMAZON

Amazon is a big data giant. We all know that Amazon pioneered e-commerce in many ways, but possibly one of its greatest innovations was the personalized recommendation system – which, of course, is built on the big data it gathers from its millions of customer transactions.

An important factor to consider when looking at Amazon is how commercial its big data is, compared to those of other companies that deal with data on a comparable scale. Unlike, say, Facebook – which might know an awful lot about which movies you like or who your friends are – the vast majority of Amazon’s data on us relates to how we spend hard cash.

 Amazon Web Services offers cloud-based computing and big data analysis on an enterprise scale. This allows companies which need to run highly processor-intensive procedures to rent the computing time far more cheaply than setting up their own data processing centres – just like Google’s BigQuery.

-> MICROSOFT

The Comapany was founded in 1975 by Bill Gates and Paul Allen, Microsoft has been a key player in just about every major advance in the use of computers, at home and in business.Just as it anticipated the rise of the personal computer, the graphical operating system and the internet, it wasn’t taken by surprise by the dawn of the big data era.

In the business-to-business market, where Microsoft made its first fortunes with its OS and office software, it is now throwing all of its considerable weight into big data-related services for enterprise. Like Google with its Adwords, Bing Ads provides pay-per-click advertising services which are targeted at a precise audience segment, identified through data collected about our browsing habits.

-> FACEBOOK

Facebook – it’s the world’s biggest social network by a huge margin, and most of us are used to using it to share details of our everyday lives with our friends and families. It’s no secret now that we’re also sharing it with their advertisers, but that hasn’t put most of us off using it! So here’s a brief rundown of how Facebook has been one of the most successful companies in the world at gathering our data and turning it into profit – and why some think its business practices sometimes overstep the mark.  

A big difference between Google and Facebook is that Google’s information on who we are is often a “best guess” based on what sites we are visiting. From the start, Facebook explicitly asks us who we are, where we live and what we are interested in. Yes, Google eventually started to do the same with Google+, but by then, they were simply playing catch-up. Advertisers clearly value this direct approach. Your Data is marketed and used in the Advertisements and work as Big Data need can clearly be visible for today's market and Trade Scenarios.  

Similarly companies like Kaggle which seems to embody all the principles of big data entrepreneurship under one roof, Crowd sourcing, predictive modelling, gamification – Kaggle has it all - and has worked out how to turn a profit from them. Likewise we have Cornerstone, GE (General Electric) e.t.c.

There are many products that aims to solve the big data problem like Hadoop, Ceph, GlusterFS, AWS S3 e.t.c. These all work on Distributed Storage.

Widely Used, Hadoop is an open-source framework that was manufactured by the Apache Software Foundation. It is one of the tools used to handle big data which consists of both structured and unstructured data. This collection of data cannot be processed or stored by traditional methods. Hadoop lead to Distributed File Storage Strategy for storing variety, volumes, and values of data.

Distribution of Data over large system will make the problem solved to a particular extent. This Research Article of Big Data is endless will explore more :)