In the present times, with the advancement of technology, it wouldn’t be probably wrong to equate data with real money. A tremendous amount of data is being generated daily as the millennials are connected to the internet and are browsing or using some applications all the time. With that being said, a new term was coined – Big Data. To put in this way, big data is simply larger, more complex data especially generated from the new data sources.
The problem with these datasets is that these are so voluminous that traditional data processing software is unable to manage the same. But what is important to know is that until and unless data is analyzed, there is no use of collecting such a huge amount of data. The truthfulness and reliability of data play another major role.
For instance, let’s take an example of any big tech company. A large amount of services and value they offer comes from their data- that is they are constantly analyzing the data to make the product and services more effective for you. More importantly, it’s the insightful analysis and the entire discovery process that helps companies make informed decisions and assumptions and predict the correct behavior.
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Technically defining Big Data was or rather is based on principles of 3 V(s). These are:
i) Volume ii) Velocity iii) Variety.
Volume is a large amount of unstructured data that is being generated. Some examples include data from Youtube feed, tweets from your Twitter account or data from Netflix subscription. Velocity is the rate at which data is incoming and perhaps acted on.
Variety refers to the type of incoming data. Most of the incoming data is rather unstructured as opposed to the neatly arranged data that we were used to. Therefore, these used to fit in a relational database but this not the case with the big data. Semi-structured data types like audio, video or text may require more processing to extract meaningful content from the same.
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As of now, it is very clear we cannot use traditional databases to analyze Big Data. Here’s when comes the importance of Hadoop. Hadoop is an open-source software developed and managed by Apache that manages data processing and storage for Big Data Application in scalable clusters of the computer server. Therefore it is the ecosystem of Big Data Technology. It can handle both structured and unstructured data, therefore it is more flexible for analyzing data rather than traditional databases.
Hadoop’s flexibity, low cost, and scalability along with computational power make it one of the best software for analyzing big data. However, some of its drawbacks include Data Security, a non-friendly user environment and a widely acknowledged talent gap.
Some surveys report Big Data guys are paid roughly the same as with the Machine Learning team with the average salary being $1,17,000 which is further expected to increase with the rise in demand. Data will never go down the drain and with more and more people connecting to the internet, companies will always need upcoming engineers to correctly analyze them, therefore they will never compromise in this aspect.
This makes Big Data one of the top technologies to keep track of in the current decade.