Merge pull request #7935 from mSarts/patch-35

Update big-data-with-iot-content.md

docs/example-scenario/data/big-data-with-iot-content.md

@@ -6,6 +6,8 @@ This example scenario is relevant to organizations building solutions that integ
 
 *Download a [Visio file][visio-download] of this architecture.*
 
+### Dataflow
+
 The data flows through the solution as follows:
 
 1. Construction equipment collects sensor data and sends the construction results data at regular intervals to load balanced web services hosted on a cluster of Azure virtual machines.
@@ -62,12 +64,16 @@ Other relevant use cases include:
 
 ## Considerations
 
+These considerations implement the pillars of the Azure Well-Architected Framework, which is a set of guiding tenets that can be used to improve the quality of a workload. For more information, see [Microsoft Azure Well-Architected Framework](/azure/architecture/framework).
+
 The broad availability of Azure regions is an important factor for this scenario. Having more than one region in a single country can provide disaster recovery while also enabling compliance with contractual obligations and law enforcement requirements. Azure's high-speed communication between regions is also an important factor in this scenario.
 
 Azure support for open-source technologies allowed the customer to take advantage of their existing workforce skills. The customer can also accelerate the adoption of new technologies with lower costs and operating workloads compared to an on-premises solution.
 
 ### Cost optimization
 
+Cost optimization is about looking at ways to reduce unnecessary expenses and improve operational efficiencies. For more information, see [Overview of the cost optimization pillar](/azure/architecture/framework/cost/overview).
+
 The following considerations will drive a substantial portion of the costs for this solution.
 
 - Azure virtual machine costs will increase linearly as additional instances are provisioned. Virtual machines that are deallocated will only incur storage costs, and not compute costs. These deallocated machines can then be reallocated when demand is high.