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Big Data Analytics – Volume, Variety, Velocity
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Big data is the new buzzword within the data warehousing and business analytics community.
According to TDWI recent report on BIG data, there are 3 Vs of big data – Volume which is multiple terabytes or over petabytes, Variety which is numbers, audio, video, text, streams , weblogs, Social media etc & velocity which is the speed with which it is collected.
Today, enterprises are exploring big data to discover facts they didn’t know before. This is an important task right now because the recent economic recession forced deep changes into most businesses, especially those that depend on mass consumers. Using advanced analytics, businesses can study big data to understand the current state of the business and track customer behavior.
Here are few examples of Big Data to get the idea:
· Twitter produces over 90 million tweets per day
· Wal-Mart is logging one million transactions per hour
· Facebook creates over 30 billion pieces of content ranging from web links, news, blogs, photos etc.
Big Data Analytics usability – think about the possibilities of real-time location data with regard to promoting coupons or customized offers to consumers who pass by a retailer’s location, Insurance companies can analyze the data collected by electronic toll transponders to accurately determine a driver’s speed, location, and mileage – and adjust insurance rates accordingly.
Because it’s early on, big-data technologies are still evolving and haven’t yet reached the level of product maturity.
Discovery analytics against big data can be enabled by different types of analytic tools, including those based on SQL queries, data mining, statistical analysis, fact clustering, data visualization, natural language processing, text analytics, artificial intelligence, and so on.
Solutions getting most advantages by Big Data Analytics:
· Customer analytics – segmentation & behavior analytics
· Fraud detection
· Risk analytics
· Advanced data visualization
Today various technology platforms are becoming available for big data analytics – Hadoop-Mapreduce, Teradata, Greenplum, Kognitio.
Hadoop has become more popular amongst all the tools as it is open source with less total cost of ownership & allows combination of any form of data without needing to have any data types or schemas defined. With massively parallel processing using MapReduce functionality it gives power to get the results quickly. It can scale up & out
Big players like Google, Yahoo, Facebook, Linkedin have already proved the Hadoop usability.
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