Taming The Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics (Wiley and SAS Business Series)

Most public cloud servers operate independent of each other. MPP systems are one big system.

By identifying who ends up in similar locations at similar times repeatedly, it is possible to identify people who may not know each other or be part of the same social network today, but who have a lot of common interests.

Based on that, it may be easier to have different types of data in different physical tables so that they can be updated independently. It will save system resources by not having the overhead of a lot of extra metrics

MapReduce can run on commodity hardware. As a result, it can be very cheap to get up and running. It can also be very cheap to expand.

Where appliances are often designed for one or two specific workloads, the enterprise data warehouse is designed to support many workloads.

Many organizations are starting to realize the power of knowing “when” their customers are “where” and are attempting to get permission to collect such information from their customers.

The big data sources we’ll cover include: Auto insurance: The value of telematics data. Multiple industries: The value of text data. Multiple industries: The value of time and location data. Retail and manufacturing: The value of radio frequency identification (RFID) data. Utilities: The value of smart-grid data. Gaming: The value of casino chip tr

The reason is that the process of executing social network analysis requires taking already-large data sets and using them in a way that effectively increases their size by orders of magnitude.

Better assessing paid search and online advertising results is another high-impact analysis enabled with customer level web behavior data.