Big Data Analytics


With the coming of smartphones so much data, it generates in the form of texts phone calls emails photos video searches and music approximately 40 exabytes of data gets generated every month by a single smartphone user. Now imagine this number multiplied by 5 billion smartphone users that's a lot for our mind to even process. This amount of data is quite a lot for traditional computing systems to handle and this massive amount of data is what we term as big data.

The data generated per minute on the internet is around 1 million snaps are shared on Snapchat, 3.8 million search queries are made on Google, 1 million people log on to Facebook, 4.5  million videos are watched on YouTube and 188 million emails are sent.

big data analytics


Snaps Shared


Google Searches


Facebook Log-Ins


Video Views






That's a lot of data so 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. For example from the healthcare industry hospitals and clinics across the world generate massive volumes of data.  2314 exabytes of data are collected annually in the form of patient records and test results. All this data is generated at a very high speed which attributes to the velocity of big data.  Variety refers to the various data types such as structured semi-structured and unstructured data,  examples include Excel records, log files, and x-ray images. Accuracy and trustworthiness of the generated data are termed as veracity analyzing all this data will benefit the medical sector by enabling faster disease detection better treatment and reduced cost this is known as the value of Big Data.

Various frameworks such as Cassandra, Hadoop, and Spark are used to store and process. Hadoop as 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. Not only that 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 way and make sure that even if one machine fails your data is safe on another.

MapReduce technique is used to process Big Data. A lengthy task is broken into smaller tasks A, B, C,  and D, Now instead of one machine three machines take up each task 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.

After storing and processing our big data we can analyze this data for numerous applications. In games like Halo 3 and Call of Duty designers analyze user data to understand 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, which in turn reduces the customer churn rate. Similarly Big  Data also helped with disaster management during Hurricane sandy in  2012. It was used to gain a better understanding of the effect of the storm on the east coast of the US 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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