Big Data Analytics
BIG DATA ANALYTICS
With the arrival of smartphones, so much data, it generates in the form of various forms. Some of the common ways are texts, phone calls, emails, photos, video searches, and music. it is recorded that 40 exabytes of data get generated every month by a single smartphone user.??
Now imagine this number multiplied by 5 billion smartphone users. That's a lot for our minds even to process or by Cloud computing. This amount of data is quite a lot for traditional computing systems. It is complex to handle the massive amount of data, and we term this as the BIG Data.
The data generated per minute on the internet is around 1 million snaps. Snaps are shared on multiple platforms. About 3.8 million search queries are made on Google, and 1 million people log on to Facebook. Over 4.5 million videos are watched on YouTube, and 188 million emails are sent.
That's a lot of data, and how do you classify any data as big data? This is possible with the concept of 5 V's (Volume, Velocity, Variety, Veracity, and Value).
1000000
Snaps Shared
3800000
Google Searches
1000000
Facebook Log-Ins
4500000
Video Views
5Vs of Big Data
Volume
Healthcare industries across the world are known to generate massive volumes of data. Two thousand three hundred fourteen exabytes of data are collected annually in inpatient records and test results.
Variety
Accuracy and trustworthiness of the generated data are termed as veracity.
Value
Accuracy and trustworthiness of the generated data are termed as veracity.
Velocity
All this data is generated at a very high speed, which attributes to immense data velocity.
Veracity
Variety refers to the various data types.??
Some are structured, semi-structured, and unstructured data. It includes Excel records, log files, and x-ray images.
Many Frameworks
Various frameworks, such as Cassandra, Hadoop, and Spark, are used to store and process and also on the Internet Of things.??
Hadoop is an example of stores and processes Big Data. Hadoop uses a distributed file system known as Hadoop distributed file system to store big data. If you have a huge file, your file will be broken down into smaller chunks and stored in various machines.??
When we break the file, we also make copies of it, which goes into different nodes. This way, you store your big data in a distributed manner and make sure that your data is safe on another even if one machine fails.
Map Reduce Technique in Big Data
MapReduce technique is used to process Big Data especially while using BlockChain technology alongside. A lengthy task is broken into smaller tasks. Imagine a set of functions like A, B, C, and D. Now, instead of one machine, three machines take up each job and complete it in a parallel fashion and assemble the results at the end.????
Thanks to this, the processing becomes easy and fast. This is known as parallel processing.
Big Data Analyzation
After storing and processing our big data, we can analyze for numerous applications. In games like Halo 3 and Call of Duty, designers study user data to understand user behavior. Some of the expected behaviors include at which stage most of the users' pause, restart, or quit playing.??
This insight can help them rework on the storyline of the game and improve the user experience. It helps reduce the customer churn rate.??
Similarly, Big Data also helped with disaster management during Hurricane sandy in 2012. It was used better to understand the storm effect on the US east coast, and the necessary measures were taken.??
It could predict the Hurricanes' landfall five days in advance, which wasn't possible earlier. These are some of the clear indications of how valuable big data can be once it is accurately processed and analyzed.
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