Have you ever wondered how much data does it generate when we all use our smartphones? Approximately a single smartphone generates about 40 exabytes of data! Now imagine the total amount of data generated by 5 billion smartphone users!!
A lot to process, isn’t it?
This massive amount of data is what we term big data.
The concept of 5V’s makes it possible for us to classify anything as a big data
- Volume
- Velocity
- Variety
- Veracity
- Value
Let us take an example of the healthcare industry to understand this a bit more.
Hospitals and clinics across the world generate massive volumes of data that are collected in the form of patient records, test results, etc. All of which is generated at a very high speed. Variety refers to the various data types such as structured, semi-structured, and unstructured data.
Examples include Excel records log files X-ray images.
The accuracy and trustworthiness of the data generated come under veracity.
Analyzing all this data will benefit the medical sector in faster disease detection, better treatment, and reduced costs; this is known as the value of big data.
How do we store and process big data?
There are various frameworks like
- Cassandra
- Hadoop
- Spark
Let us take Hadoop as an example, Hadoop uses a distributed file system to store big data. A huge file is generally broken into smaller files & stored in 2 different machines.
Big data is stored in a distributed way, This ensures that even if one machine fails data is safe on another.
The Map-Reduce technique is used to process big data. A task is broken into smaller tasks. Each machine takes up these tasks parallelly. This is known as parallel processing.
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The next step after storage and processing of data is the analysis of this data for numerous applications. Take the example of the gaming industry, analysts use big data to make sense of gamers’ behavior, understanding where does he or she pause, restarts or quits the game, helping them redesign the story-line of the game, improving user experience, which in turn reduces the customer churn rate.
