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Abstract
tive users as of 2018:
Facebook: 2.41 billion
Instagram: 1 billion
Twitter: 320 million
LinkedIn: 575 million</p><figure id="bd7a"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*CqkwTanBp9GxW0za7Utq3A.png"><figcaption>Facebook monthly active users growth since 2008</figcaption></figure><h2 id="b99f">3) Variety</h2><p id="d572">It refers to <b>Structured</b>, <b>Semi-structured</b> and <b>Unstructured</b> data due to different sources of data generated either by humans or by machines.</p><p id="ad30"><b>Structured data:</b> It’s the traditional data which is organized and conforms to the formal structure of data. This data can be stored in a relational database. Example: Bank statement containing date, time, amount etc.</p><figure id="d0c5"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*GmwP-tc8XjqEnqtmZ5gcFg.png"><figcaption></figcaption></figure><p id="356e"><b>Semi-structured data:</b> It’s semi-organized data. It doesn’t conform to the formal structure of data. Example: Log files, JSON files, Sensor data, csv files etc.</p><figure id="a045"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*yFuo1cY1HumSFbTW2pNNcw.png"><figcaption></figcaption></figure><p id="5e51"><b>Unstructured data:</b> It’s not an organized data and doesn’t fit into rows and columns structure of a relational database. Example: Text files, Emails, images, videos, voicemails, audio files etc.</p><figure id="5cbb"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*j7fKA5bVAOWCkvgg58Rk7w.png"><figcaption></figc
Options
aption></figure><h2 id="a00a">4) Veracity</h2><p id="0b5e">It refers to the assurance of <b>quality/integrity/credibility/accuracy</b> of the data. Since the data is collected from multiple sources, we need to check the data for accuracy before using it for business insights.</p><figure id="a7a1"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*TEWPYT3tJdxw0yQbgJks9w.png"><figcaption></figcaption></figure><h2 id="2256">5) Value</h2><p id="ed99">Just because we collected lots of Data, it’s of no value unless we garner some insights out of it. Value refers to how useful the data is in decision making. We need to extract the value of the Big Data using proper analytics.</p><figure id="03d3"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*uXsYTpPHIXufxEKHCMHmww.png"><figcaption></figcaption></figure><h2 id="230c">What are the other V’s?</h2><p id="02f1"><b>Viscosity </b>(complexity or degree of correlation), <b>Variability </b>(inconsistency in data flow), <b>Volatility </b>(durability or how long time data is valid and how long it should be stored), <b>Viability </b>(capability to be live and active), <b>Validity </b>(understandable to find the hidden relationships).</p><h2 id="5624">Where is the Big Data stored?</h2><figure id="9aea"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*0QkmQ69BXYEDiGPsBjjdrg.png"><figcaption></figcaption></figure><p id="b6e0"><b>Thank you</b> for reading! Please 👏and <b>follow me</b> if you liked this post, as it <b>encourages me</b> to write more!</p></article></body>
