Big Data Data Analytics and Applications
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- Overview
The Harvard Business Review once called the role of the Data Scientist "The Sexiest Job of the 21st Century." That’s because it takes someone with the skills of a scientist to make something useful out of big data.
Big data analytics applications enable big data analysts, data scientists, predictive modelers, statisticians and other analytics professionals to analyze growing volumes of structured transaction data, plus other forms of data that are often left untapped by conventional BI and analytics programs.
This encompasses a mix of semi-structured and unstructured data - for example, Internet clickstream data, web server logs, social media content, text from customer emails and survey responses, mobile phone records, and machine data captured by sensors connected to the internet of things (IoT).
The value of the data is tied to comparing, associating or referencing it with other data sets. Analysis of big data usually deals with a very large quantity of small data objects with a low tolerance for storage latency.
- Synchronous and Asynchronous Analytics
Big data analytics is the use of advanced analytic techniques against very large, diverse big data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.
There are two basic types of big data analytics - synchronous and asynchronous - but both have big data storage appetites and specialized needs.
Synchronous and asynchronous are distinguished by the way they process data. But they both have big data storage appetites and specialized needs. S
- Synchronous Analytics:
- Asynchronous Analytics:
[More to come ...]
