Big Data

Big Data Strategy

Big data is governed by the big four Vs:

Volume

Volume indicates more data, the entire essence of the word BigData. Even though the data is much rough and granular, this is what makes this much unique. Bigdata processing requires high volumes of low-density, unstructured in the Hadoop clusters hence this data becomes much essential for processing. Normally stored in NoSQL, these can be from Twitter data feeds, to Facebook comments to clickstreams on a mobile app, network traffic on a webpage, IOT equipment capturing data, to colossal healthcare records and many more. It is a complex task to convert such Hadoop data into valuable information. The information size can be in tens of terabytes and petabytes. Therefore bigdata processing becomes much essential.

Velocity

The amount of data has tippled itself after the birth of IOT and access to superfast data bandwidths. And this information is relayed at a breakneck speed making this a significant reason for using technologies like Apache Flume, Kafka. Where the idea is to have low latency very high availability event ingestion, which further processed with technologies like HDFS, Storm, ElasticSearch, HBase all in parallel making a robust structure for the real-time data incoming. with instances like a GPS tracker for children sending coordinates every 20 seconds and 6meaters change in location

Variety

New types of data are in the form of Unstructured and semi-structured data types, like Instagram images on stories or status updates in text, in SoundCloud audio, and video from youtube. All the above are not traditional sequential data but are powerful to predict business decisions. But to conclude, it requires additional processing of the meaning as well as the supporting metadata.

Veracity

With the growth of the new ways in Data Collection, the sanctity of data has degraded over time. Data sources have become more and more unreliable with the increase of speed of induction, faulty bias from social data and fake news playing a part of data collection. This uncertainty of the data has raised business decisions that in reality. Hence the intrinsic value must be kept intact. There are multiple investigative techniques to derive the value from data collected. Therefore discovering a consumer sentiment on a product or service, to making a suitable offer by demographics, or for identifying a specific inventory should be stocked or not. Attempting to learn, to ask the right questions, recognizing patterns in the answers and finally making informed assumptions, and predicting behavior.

Client Speak

It usually starts as a “symptom” – the need for something more than traditional DB management tools in your enterprise. There are a standard set of activities which when performed, enable the linking of this “symptom” or a “business challenge” to your Big-data needs.

Richard Jhone
Senior Product Manager Secure Digital Services Ltd.

It usually starts as a “symptom” – the need for something more than traditional DB management tools in your enterprise. There are a standard set of activities which when performed, enable the linking of this “symptom” or a “business challenge” to your Big-data needs.

Richard Jhone
Senior Product Manager Secure Digital Services Ltd.

It usually starts as a “symptom” – the need for something more than traditional DB management tools in your enterprise. There are a standard set of activities which when performed, enable the linking of this “symptom” or a “business challenge” to your Big-data needs.

Richard Jhone
Senior Product Manager Secure Digital Services Ltd.

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