1. Introduction to Azure Streaming Analytics
Presented By: Warren Sifre

2. Who Am I? Email: warren.sifre@moserit.com Twitter: @WAS_SQL
Professional History
20 years in the technology industry focusing on Information Technology
LinkedIn:

3. Agenda What is Streaming Analytics? How it works? Use Cases
Configuration and Dependencies
SAQL
Demo

4. Typical Environment

5. Challenges… Real-Time Analytics Environment Scalability
Many Steps in between the Source Data and the Visualization/Reporting Output.
Environment Scalability
Months/Years of planning is needed to plan out equipment procurement and scale out to meet increasing demand.
Resource Cost Management
Ideal configuration would require the purchasing of enough equipment to handle peak performance requirements. Although those peak requirements may only be for a few hours of any given day.
Disaster Recovery Strategy
Architecting and maintaining a DR strategy where performance, RTOs, and RPOs are met can be challenging and leave the organization with a lot of underutilized resources.

6. What is Streaming Analytics?
A way to evaluate data before it has reached its final repository destination.
Why?
Hours to weeks can be the time it takes for data to be transmitted, received, processed, aggregated, then visualized in the traditional Data Warehouse architecture. Business requirements have changed and the desire to glean insights from this data sooner is now becoming a requirement, not a nice to have.

7. How it can work Streaming Analytics Job Event Hub Power BI
Process and Deliver data to multiple end points
Transmit Data
Event Hub
Queues data for processing
Power BI
Visualize real-time data stream
Data Factory
Gather and process data for Predictive Analytics Process
Machine Learning
Predictive Analytics Processing
Azure SQL
Store data

8. Reimagined Environment

9. Use Cases Transportation Energy Manufacturing Medical Device
Reduce the need to pull vehicles from service for routine inspections by using sensors to determine when actual anomalies are occurring.
Energy
Monitor equipment from central locations such as Wind Turbines and Power Generators, thus reducing time spent on manual/physical inspection or replacement of parts just because of time instead of actual degraded performance.
Manufacturing
Monitor equipment and plant conditions for optimal performance.
Medical Device
Through remote monitoring expensive replacement parts can be ordered closer to the end-of-life of an equipment than by a schedule. This can reduce the cost of having an overstock of parts on-hand.

10. Configuration Options

11. SAQL - Elements

12. SAQL - Elements

13. Tumbling Window
Fixed window of time with no overlap

14. Hopping Window
Fixed window of time with a fix time of overlap

15. Sliding Window
A Fixed window time, but a window is defined as the moment an event enters or exits an existing window.

16. Sliding Window
A Fixed window time, but a window is defined as the moment an event enters or exits an existing window.

17. Sliding Window
A Fixed window time, but a window is defined as the moment an event enters or exits an existing window.

18. Sliding Window
A Fixed window time, but a window is defined as the moment an event enters or exits an existing window.

19. Sliding Window
A Fixed window time, but a window is defined as the moment an event enters or exits an existing window.

20. Sliding Window
A Fixed window time, but a window is defined as the moment an event enters or exits an existing window.

21. Sliding Window
A Fixed window time, but a window is defined as the moment an event enters or exits an existing window.

22. Demonstration

23. Helpful Links More Information on Streaming Analytics SAQL
SAQL Query Patterns
Azure Portal Link
Azure Portal

24. Thank You! Email: warren.sifre@moserit.com Twitter: @WAS_SQL
LinkedIn: