How Big MNC's manage their Big Data?
In this article , I am explaining you how big MNC's like Google, Facebook, Instagram etc stores, manages and manipulate Thousands of Terabytes of data with High Speed and High Efficiency.
What is Big Data?
Big data is a combination of structured, semi-structured and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling and other advanced analytics applications.
What are the types of Big Data?
Big Data encompasses three types of data: structured, semi-structured, and unstructured data. Each type includes a lot of useful information that you can extract for use in different projects.
⭐Structured data has a fixed format and is often digital. In most cases, they are processed by machines rather than humans.
Example: Database Management Systems(DBMS)
⭐Unstructured data is information that is unorganized and does not have a predetermined format, because it can be almost anything.
Example: Audio Files, Images etc
⭐Semi-structured data can contain both types of data, such as web server logs or data from sensors you have set up.
Example:Comma Separated Values(CSV) File.
What comes under big data?
Black Box Data − It is a component of helicopter, airplanes, and jets, etc. It captures voices of the flight crew, recordings of microphones and earphones, and the performance information of the aircraft.
Social Media Data − Social media such as Facebook and Twitter hold information and the views posted by millions of people across the globe.
Stock Exchange Data − The stock exchange data holds information about the ‘buy’ and ‘sell’ decisions made on a share of different companies made by the customers.
Power Grid Data − The power grid data holds information consumed by a particular node with respect to a base station.
Transport Data − Transport data includes model, capacity, distance and availability of a vehicle.
Search Engine Data − Search engines retrieve lots of data from different databases.
Data Growth over the years
The first trace of big data is seen way back in 1663 when John Graunt dealt with overwhelming amounts of information while he studied the bubonic plague, which was haunting Europe at the time. Graunt was the first-ever person to use statistical data analysis. Later, in the early 1800s, the field of statistics expanded to include collecting and analyzing data.
The world first saw the problem with the overwhelming of data in 1880. The US Census Bureau announced that they estimate it would take eight years to handle and process the data collected during the census program that year. In 1881, a man from the Bureau named Herman Hollerith invented Hollerith Tabulating Machine that reduced the calculation work.
Throughout the 20th century, data evolved at an unexpected speed. Big data became the core of evolution. Machines for storing information magnetically and scanning patterns in messages, and computers were also created at that time. In 1965, the US government built the first data centre, with the intention of storing millions of fingerprint sets and tax returns.
“The power of big data derives from collecting vast quantities of information and analyzing it in ways that humans could never achieve without computers in an attempt to perform the apparently impossible.” – Brian Clegg, Science Author
Problem faced for managing Big Data
Big data can be described in terms of data management challenges that – due to increasing volume, velocity and variety of data – cannot be solved with traditional databases. While there are plenty of definitions for big data, most of them include the concept of what’s commonly known as “three V’s” of big data:
Volume: Ranges from terabytes to petabytes of data
Variety: Includes data from a wide range of sources and formats (e.g. web logs, social media interactions, ecommerce and online transactions, financial transactions, etc)
Velocity: Increasingly, businesses have stringent requirements from the time data is generated, to the time actionable insights are delivered to the users. Therefore, data needs to be collected, stored, processed, and analyzed within relatively short windows – ranging from daily to real-time
How Big companies manage their data?
1. Google
Google uses big data to understand what we want from it based on several parameters such as search history, locations, trends, and many more.
After that, it goes through an algorithm where complex estimations are done and afterward Google easily shows the arranged or positioned indexed lists as far as significance and authority intended to coordinate the users prerequisite.
Google easily shows the ranked search results in terms of relevance and authority formulated to match the user’s requirement.
Google has acquired some techniques to understand user’s requirements like Indexed pages, real-time feeds, sorting tools, knowledge graph pages, literal and semantic search, google translate, etc.
2. Facebook
The world’s most popular social media network with more than two billion monthly active users worldwide, Facebook stores enormous amounts of user data, making it a massive data wonderland. It’s estimated that there will be more than 183 million Facebook users in the United States alone by October 2019. Facebook is still under the top 100 public companies in the world, with a market value of approximately $475 billion.
Every day, we feed Facebook’s data beast with mounds of information. Every 60 seconds, 136,000 photos are uploaded, 510,000 comments are posted, and 293,000 status updates are posted. That is a LOT of data.
The statistic shows that 500 terabytes of new data get ingested into the databases of social media site Facebook, every day. This data is mainly generated in terms of photo and video uploads, message exchanges, putting comments etc.
Apart from Google, Facebook is probably the only company that possesses this high level of detailed customer information.
3. Netflix
Netflix has over 100 million subscribers and with that comes a wealth of data they can analyze to improve the user experience. Big data has helped Netflix massively in their mission to become the king of stream.
Big data helps Netflix decide which programs will be of interest to you and the recommendation system actually influences 80% of the content we watch on Netflix. The company even gave away a $1 million prize in 2009 to the group who came up with the best algorithm for predicting how customers would like a movie based on previous ratings. The algorithms help Netflix save $1 billion a year in value from customer retention.
3.Amazon
Amazon uses largest number of server for hosting their data they host around 1,000,000,000 gigabytes of data across more than 1,400,000 servers.
Amazon generates data two-fold. The major retailer is collecting and processing data about its regular retail business, including customer preferences and shopping habits. But it is also important to remember that Amazon offers cloud storage opportunities for the enterprise world.
Amazon S3 — on top of everything else the company handles — offers a comprehensive cloud storage solution that naturally facilitates the transfer and storage of massive data troves. Because of this, it’s difficult to truly pinpoint just how much data Amazon is generating in total.
Instead, it’s better to look at the revenue flowing in for the company which is directly tied to data handling and storage. The company generates more than $258,751.90 in sales and service fees per minute.
Amazon is currently the world’s largest retailer, having overtaken Wal-Mart earlier recently, and is currently the fourth most valuable public company, behind only Apple Inc, Alphabet, and Microsoft. It’s current revenue is estimated to be $107 billion USD, and a total active user base of 244 million, whereas an active user is defined as an account that has purchased something through Amazon in the past 12 months.
5. IBM
For playing global data evangelist by sharing its problem-solving power with cities, businesses, and universities. IBM raked in more revenue from big data–related products and services—a total of $1.3. billion—than any other company in 2012, and it’s not simply because Big Blue excels at data storage and analytics. In the (still) early days of big data, IBM is its biggest, and much needed, evangelist.
for example, to improve traffic flow by predicting points of congestion. IBM is also launching new curriculums centered on big data and analytics at schools like Georgetown and Rensselaer Polytechnic Institute, in an effort to prepare students for the estimated 4.4 million big-data jobs that will be created by 2015.
6.Splunk
For providing businesses with hundreds of homegrown apps to sniff out error files and keep things humming. After going public in 2012, Splunk has continued its explosive growth as a pure-play leader in the big-data space.
It pulled in $200 million in revenue in 2013, thanks to new customers like T-Mobile, the U.S. Department of Energy, and the Latin American e-commerce giant B2W, and to its hundreds of apps that let companies manipulate their data in new ways.
Conclusion
I hope you all understood something new about the concept of Big Data. Although It is a pretty vast topic that has lots to discuss I have tried my best to cram in as much information as possible. Finally, if you have any question, comments or suggestions you can leave them in the comments section.
Happy learning!!
