Big Data For Dummies

In this part

• Trace the evolution of data management
• Define big data and its technology components
• Understand the different types of big data
• Integrate structured and unstructured data.
• Understand the difference between real-time and non-real-time data
• Scale your big data operation with distributed computing

In this Chapter

• Looking at a history of data management
• Understanding why big data matters to business
• Applying big data to business effectiveness
• Defining the foundational elements of big data
• Examining big data's role int the future

In this Chapter

• Identifying structured and unstructured data
• Recognizing real-time and non-real-time requirements for data types
• Integrating data types into a big data environment

Although data management has been around for a long time, two factors are new in the big data world:

• Some sources of big data are actually new like the data generated from sensors, smartphone, and tablets
• Previously produced data hadn't been captured or stored and analyzed in a usable way. the main reason for this is that the technology wasn't there to do so. In other words, we didn't have a cost-effective way to deal with all that data.

• Computer- or machine-generated: Machine-generated data generally refers to data that is created by a machine without human intervention.
• Human-generated: This is data that humans, in interaction with computers, supply.

Here are some examples of machine-generated unstructured data:

• Satellite images: This includes weather data or the data that the government captures in its satellite surveillance imagery.
• Scientific data: This includes seismic imagery, atmospheric data, and high energy physics.

The following list shows a few examples of human-generated unstructured data:

• Text internal to your company: Think of all the text within documents, logs, survey results, and e-mails. Enterprise information actually represents a large percent of the text information in the world today.
• Social media data: This data is generated from the social media platforms such as YouTube, Facebook, Twitter, LinkedIn, and Flickr.
• Mobile data: This includes data such as text messages and location information.
• Website content: This comes from any site delivering unstructured content, like YouTube, flickr, or Instagram.

• CMS¹⁾
• AIIM²⁾ : www.aiim.org
• ECM³⁾

• Monitoring for an exception with a new piece of information, like fraud/intelligence
• Monitoring news feeds and social media to determine events that may impact financial markets, such as a customer reaction to a new product announcement
• Changing your ad placement during a big sporting event based on real-time Twitter streams
• Providing a coupon to a customer based on what he bought at the point of sale