How Evolving Technologies And Facebook Are Deploying Big Data
Each day, 2.5 million TB of data are generated. This huge quantity of data needs to be stored so that they can be easily accessed later. They are data measured in zettabytes, petabytes and exabytes, which are lesser known terms.
As companies gather rising amounts of data, requirements on infrastructure and technology have become larger. The following 3 Vs describes what Big Data is all about.
This feature represents the absolute amount of data produced by companies. It includes data from transaction information, social media created information, sensor and machine to machine data. Without technologies to accumulate such large amounts of data, storing it easily would be a problem.
Latest technologies allow faster data processing through sensors, RFID tags and other technologies. However, structuring and storing them in real time is a big challenge.
Data come in a wide variety of formats. It can range from regular databases, unstructured video, audio and email to transaction data.
These three features of Big Data offer a challenge for the companies that need to store data in an integrated, structured, affordable and accessible way.
Facebook And Big Data
Social media assure the growth and development of innovation through mass collaboration. Across various industries, companies use social media platforms to promote products and services. They also use it to observe how people respond to brands.
One of the largest Big Data experts is Facebook. It deals with petabytes of data on a regular basis. As the world connects through this platform, it generates algorithms to track those connections.
Whether it’s a wall post or your favorite movies and books, Facebook surveys each and every bit of your data. It does this to provide you superior services each time you log in.
Some Of The Contributors Behind Facebook’s Big Data
There is a variety of technology working behind this platform. Below are the most significant ones:
Facebook manages the biggest Hadoop cluster. It has beyond 4,000 machines and stores millions of gigabytes. This large-scale cluster provides some crucial skills to developers.
The developers can openly write MapReduce programs in any language.
SQL has been combined to process large data sets, as the majority of data in the Hadoop’s file system are in table format. Because of this, it becomes easier for developers to access smaller SQL subsets.
Beginning with searching, recommendation system, log processing and data warehousing, Hadoop is enabling Facebook in any way possible. In fact, its first user-facing app, Facebook Messenger, is based on Hadoop database.
Encountering a large amount of disorganized data each day, Facebook realized that it requires a platform to speed up its analysis. It developed Scuba to help the Hadoop developers plunge into the massive data set. It also allows them to carry on an ad-hoc analysis in real-time.
Facebook wasn’t meant to run across different data centers. A single malfunction could cause the whole platform to fail.
Scuba enables the developers to store in-memory data in bulk. It’s supposed to help speed up the informational analysis. It executes small software agents to collect data from data centers and compress them into log data pattern. The compressed data are further compressed by Scuba into smaller memory systems which can be promptly assessed.
After the launch of Hadoop by Yahoo for its search engine, Facebook also decided to empower its data scientists. It needed help in storing huge amount of data in the Oracle data warehouse. Thus, Hive came into existence.
This tool enhanced the query capability of Hadoop by using a part of SQL. Eventually, it became popular in the unstructured world. Nearly thousands of jobs are running using this system today.
As Facebook doesn’t run on multiple data centers, it needed the help of Prism. It’s a platform that brings out several namespaces. It helps in developing many logical clusters, too.
It was getting difficult to manage the task trackers and cluster resources. As pull-based scheduling model was causing a delay in handling small jobs, MapReduce came to existence. Hadoop was restricted by its slot-based resource management model. It wasted the slot every time the cluster size was unable to fit the configuration.
Developing and executing Corona helped in establishing a new scheduling framework. It helped distinguish resource management from job coordination.
Today, Facebook has become one of the substantial corporations in the world. It’s all thanks to its considerable data on over 1.5 Billion people on earth. However, security and privacy concerns remain as it isn’t sure whether Facebook will just save its big data on its server or use them in order to make money.
But, one thing is for sure. It’s the Big Data that pushed Facebook, a small-time startup in Harvard, into becoming one of the large corporations of all times.
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Author: Ritesh Patil
Ritesh Patil is the co-founder of Mobisoft Infotech that helps startups and enterprises in mobile technology. He loves technology, especially mobile technology. He’s an avid blogger and writes on mobile application. He works in a leading android development company with skilled android app developers that has developed innovative mobile applications across various fields such as Finance, Insurance, Health, Entertainment, Productivity, Social Causes, Education and many more and has bagged numerous awar