By Dr. Arvind Sathi
Bringing a practitioner’s view to special info analytics, this paintings examines the drivers in the back of tremendous information, postulates a collection of use instances, identifies units of answer parts, and recommends a variety of implementation methods. This paintings additionally addresses and carefully solutions key questions about this rising subject, together with What is gigantic facts and the way is it getting used? How can strategic plans for giant information analytics be generated? and How does mammoth info swap analytics architecture? the writer, who has greater than twenty years of expertise in info administration structure and supply, has drawn the cloth from a wide breadth of workshops and interviews with company and data expertise leaders, supplying readers with the most recent in evolutionary, innovative, and hybrid methodologies of relocating ahead to the courageous new international of massive info.
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Additional resources for Big Data Analytics: Disruptive Technologies for Changing the Game
For a 100-million-subscriber CSP, the CDRs could easily exceed 5 billion records a day. 2 Velocity There are two aspects to velocity, one representing the throughput of data and the other representing latency. Let us start with throughput, which represents the data moving in the pipes. 8 exabytes per month in 20164 as consumers share more pictures and videos. To analyze this data, the corporate analytics infrastructure is seeking bigger pipes and massively parallel processing. Latency is the other measure of velocity.
1 shows the monitoring station with the dashboard. Big Data Analytics can be used to monitor social media for feedback on product, price, and promotions as well as to automate the actions taken in response to the feedback. This may require communication with a number of internal organizations, tracking a product or service problem, and dialog with customers as the feedback results in product or service changes. When consumers provide feedback, the dialog can only be created if the responses are provided in low latency.
How many users could use the product to perform basic functions offered by the product? What were the highest frequency functions? The next level of analysis is to understand component failures. How many times did the product fail to perform? Where were the failures most likely? What led to the failure? What did the user do after the failure? Can we isolate the component, replace it, and repair the product online? These analysis capabilities can now be combined with product changes to create a sophisticated test-marketing framework.