Now that you know how your data is transmitted back to the corporate servers, you feel you have a better understanding of it. You also have a reference frame in your head of how it is operating out in the real world.

Your boss stops by again.

"Is that rolling average job done running yet?" he asks impatiently.

It used to run fine and finished in an hour three months ago. It has steadily taken longer and longer and now sometimes does not even finish. Today, it has been going on six hours, and you are crossing your fingers. Yesterday, it crashed twice with what looked like out-of-memory errors.

You have talked to your IT group and finance group about getting a faster server with more memory. The cost would be significant and will probably take months to complete the process of going through purchasing, putting it on order, and having it installed. Your friend in finance is hesitant to approve it. The money was not budgeted for this fiscal year. You feel bad, especially since this is the only analytic job causing you problems. It just runs once a month but produces key data.

Not knowing what else to say, you give your boss a hopeful, strained smile, and show him your crossed fingers.

"It's still running...that's good, right?"

This chapter is about the advantages to cloud-based infrastructure for handling and analyzing IoT data. We will discuss cloud services including Amazon Web Services (AWS), Azure, and ThingWorx. You will learn how to implement analytics elastically to enable a wide variety of capabilities.

This chapter will cover the following:

• Building elastic analytics
• Designing for scale
• Cloud security and analytics
• Key cloud providers:
  • Amazon AWS
  • Microsoft Azure
• PTC ThingWorx