History Of Big Data

The main objective of this tutorial is to provide you with a detailed introduction to big data and big data history. Here we will also discuss the big data technologies like Apache Hadoop, Apache Spark, and Flink. Many real-life use cases of big data are also discussed in this tutorial.

In recent days the big world of the internet is creating 2.5 quintillion bytes of data on regular basis, according to the statistics, the percentage of data that has been generated from the last two years is 90%. The data which is being generated on daily basis comes from many industries like climate information which is collected by the sensors, different forms of data from social media websites, digital photos and videos, different records of the purchase transaction. All the data which is being generated from different sources is called is big data.

History Of Big Data:

The below section of this tutorial gives you a clear picture of the history of big data-

· Research & Development: The Big Data native businesses are very close, and very close to the research and open source community.

· every paper on the cost-efficient innovative information processing techniques have been accompanied by open source adoption within an ever-growing ecosystem called Hadoop.

The major milestones in the development of Hadoop also added confidence to the Power of open source and Big Data Technologies. In the first two years after its first release, in 2008, Hadoop won the terabyte sort benchmark in big data history. This is for the first time that either a Java or an open-source program has won. In the year 2010 Facebook claimed that they had the largest Hadoop cluster in the world with 21 PB of storage for their social messaging platform.

Facts And Figures:

· Almost 91% of leaders belong to marketing believe successful brands use customer data to drive business decisions.

· The concluded percentage of the world’s total data that has been created just within the past two years is 90%.

· Nearly 87% of companies agree capturing and sharing the right data is important to effectively measure ROI in their own company.

· The daily call records analyzed by IBM to predict the customer’s churns are 500 million.

· The annual meter readings converted by IBM through Big Data to better predict power consumption is 350 million.

· Nearly 30 billion pieces of content are sharing by users each month on Facebook.

Big Data Technologies

Whereas the topic of Big Data is broad and encompasses many trends and new technology developments, the top emerging technologies are given below that are helping users to deal and cost-effectively handle Big Data.

i.Apache Hadoop

Apache Hadoop is the backbone of every Big Data solution, It is predicted that the world’s 75% of the data will be stored in Hadoop by 2017. 

ii.Apache Spark :

Apache is a lightning-fast cluster computing engine that is 100 times faster than MapReduce. It is considered as the next generation Big Data tool.

iii.Apache Flink :

Apache Flink is an open-source framework that can handle streaming as well as batch data. Apache Flink is called 4G of Big Data.

Big Brands Figures

i. Facebook

Facebook is collecting a huge amount of data because of more than 950 million users. Whenever you click a notification, visit a page, upload a photo, or check out a friend’s link, you are generating data for the company to track various records. Nearly all the users of Facebook shared 2.5 billion content items daily (status updates + wall posts + photos + videos + comments). Almost 300 million photos are uploaded by users per day. Nearly 105 terabytes of data are scanned via Hive, which is Facebook’s Hadoop query language in every 30 minutes. Per day nearly 70,000 queries are executed on these databases. Approximately 500+ terabytes of new data ingested into the databases every day.

ii. Twitter

Twitter is the second biggest social network generating fewer amounts of social data as compared to the dating app, Tinder. This dating app I,e Tinder users swipe around 290,278 matches per minute which is potentially 35 million lovers per hour!. Twitter users generate 347,222 Tweets each minute or 21 million Tweets per hour on the other hand.

iii. YouTube :

As the video is a big part of our everyday lives on the internet, Facebook is also trying hard to fit in and it is succeeding, with over 3 billion video views per day but YouTube is still the king. On YouTube, every minute users are uploading over 300 hours of new video.

Use Cases Of Big Data

We cannot discuss data without discussing the people, they are the ones who are benefited by Big Data applications. Nearly all the industries today are leveraging Big Data applications in one or the other way.Big Data Analytics

  • Healthcare: Healthcare is making use of the petabytes of patient’s data. With the help of this data, the organization can extract meaningful information and then build applications that can predict the patient’s deteriorating condition in advance.
  • Telecom: Telecommunication sectors collect information, analyzes it, and provide solutions to different problems. By adopting Big Data applications, telecom companies have been able to significantly reduce data packet loss, which occurs when networks are overloaded, and thus, providing a seamless connection to their customers.
  • Retail: The retail sector has some of the tightest margins, and is one of the greatest beneficiaries of big data. The quality of using big data in retail is to understand consumer behavior. By using Big Data, Amazon’s recommendation engine provides suggestions based on the browsing history of the consumer.
  • Traffic control: The congestion caused by traffic is a major challenge for many cities globally. Effective usage of data and sensors will be key to managing traffic better as cities become increasingly densely populated.
  • Manufacturing: In the manufacturing industry we can reduce component defects, improve product quality, increase efficiency, and save time and money by adapting to Big Data technologies.
  • Search Quality: When we are extracting information from google, we are simultaneously generating data for it every time. The search engine giant stores this data and uses it to improve its search quality.

Until now in this Big Data tutorial, I have just shown you the best picture of Big Data. If it was so easy to leverage Big data, don’t you think all the organizations would invest in it? Let me tell you upfront, that is not the case. Several challenges come along when you are working with Big Data.