- Data Volume and Frequency
- Process Complexity and Performance
- Aggregation Complexity and Performance
Data of the example:
- 100 Devices
- 1 message/second, Message co1000unt per day: 8,640,000
- message size: 4KB, Message size per day: 33GB
Factor | Level |
---|---|
Data Volume and Frequency | Medium |
Process Complexity and Performance | Medium |
Analysis Complexity and Performance | Medium |
The typical Azure IoT solution architecture.
- Stream Analytics: TODO
- Azure SQL database: TODO
https://azure.com/e/4b80638abe1b41d584ce80046beec1a5
Factor | Level |
---|---|
Data Volume and Frequency | Medium |
Process Complexity and Performance | Medium |
Analysis Complexity and Performance | Low |
When there is simple visualization requirement at the output data of Stream Analytics.
- Storage Table
https://azure.com/e/7305ca1df52d4059924af06a8730fb33
Factor | Level |
---|---|
Data Volume and Frequency | Medium |
Process Complexity and Performance | Low |
Analysis Complexity and Performance | Low |
- Data Lake Storage: TODO
- Azure Functions: TODO
- Storage Table: TODO
https://azure.com/e/8945b3fc6ba44c6892c6217270c0aa87
If Device Management is not needed, IoT Hub can be replaced by EventHubs.
https://azure.com/e/4e0ca59a036b4c319b19a1707e175a04
Factor | Level |
---|---|
Data Volume and Frequency | High |
Process Complexity and Performance | Medium |
Analysis Complexity and Performance | High |
- CsomosDB: TODO
https://azure.com/e/05f0b2f9e52e437c9df57c0ab03fc3c6
Factor | Level |
---|---|
Data Volume and Frequency | High |
Process Complexity and Performance | High |
Analysis Complexity and Performance | High |
- Azure Databricks: TODO
https://azure.com/e/9e7c3fab3066491f942bf495d0995a67
A calculation tool