Big Data History, Technologies and Use Cases
This tutorial provides you with a detailed introduction to big data and big data history. We will also discuss the big data technologies like – Hadoop, Apache Spark, and Flink. Various real-life use cases of big data are also discussed in this tutorial.
Big Data – History, Use Cases and Technology
Day by day the big world of 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%. This data comes from many industries like climate information collects by the sensor, different stuff from social media sites, digital images and videos, different records of the purchase transaction. This data is big data.
If these professionals can make a switch to Big Data, so can you:
This section of tutorial gives you a clear picture of big data history-
Two major milestones in the development of Hadoop also added confidence into the Power of open source and Big Data Technologies. Only two years after its first release, in 2008, Hadoop won the terabyte sort benchmark in big data history. This is the first time that either a Java or an open source program has won. In 2010 Facebook claimed that they had the largest Hadoop cluster in the world with 21 PB of storage for their social messaging platform.
While 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 cope with and handle Big Data in a cost-effective manner.
5.1. Apache Hadoop
The backbone of every Big Data solution, It is anticipated that world’s 75% of the data will be stored in Hadoop by 2017.
5.2. Apache Spark
Apache Spark is considered as next generation Big Data tool, It is lightening fast cluster computing engine which is 100 times faster than Hadoop-
5.3. Apache Flink
Apache Flink is called 4G of Big Data. It is an open source framework that can handle streaming as well as batch data.
Because of more than 950 million users, Facebook is collecting a huge amount of data. Every time whenever you are clicking a notification, visiting a page, uploading a photo, or checking out a friend’s link, you’re generating data for the company to track various records.Users shared 2.5 billion content items daily (status updates + wall posts + photos + videos + comments). 300 million photos are uploaded by users per day. 105 terabytes of data scanned via Hive, Facebook’s Hadoop query language in every 30 minutes. 70,000 queries executed on these databases per day. 500+terabytes of new data ingested into the databases every day.
Twitter – the second biggest social network generating less social data as compared to dating app, Tinder. Tinder users swipe 290,278 matches per minute – that is potentially 35 million lovers per hour! on the other hand, twitter users generate 347,222 Tweets each minute – or 21 million Tweets per hour.
The video is a big part of our everyday lives on the internet, and although Facebook is also trying really hard to fit in and it is succeeding, with over 3 billion video views per day but YouTube is still the king. Every minute users are uploading over 300 hours of new video on YouTube.
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To learn more use cases of big data in the Retail industry follow this guide.
To get deep dive into Big data real life use cases follow this comprehensive guide.
So this was all in Big Data History. Hope you like our explanation.