The document provides an overview of Azure Stream Analytics, highlighting its capabilities in complex event processing and real-time data ingestion using a SQL-like query language. It discusses its advantages in cloud environments, scalability, and integration with other Azure offerings, while also comparing it to alternative solutions like Apache Storm. Additionally, it outlines advanced features, use cases, and potential applications in fields such as fraud detection and customer sentiment analysis.

2. AZURE STREAM
ANALYTICS
DAVIDE MAURI
@mauridb
dmauri@solidq.com

3. • Microsoft SQL Server MVP
• Works with SQL Server from 6.5, on BI from 2003
• Specialized in Data Solution Architecture, Database Design,
Performance Tuning, High-Performance Data Warehousing, BI, Big
Data
• President of UGISS (Italian SQL Server UG)
• Regular Speaker @ SQL Server events
• Consulting & Training, Mentor @ SolidQ
• E-mail: dmauri@solidq.com
• Twitter: @mauridb
• Blog: http://sqlblog.com/blogs/davide_mauri
Davide Mauri

4. • COMPLEX EVENT PROCESSING
• LAMBDA ARCHITECTURE
• AZURE STREAM ANALYTICS
• DATA INGESTION
• AZURE STREAM ANALYTICS QUERY LANGUAGE
• ADVANCED FEATURES
• ADDITIONAL RESOURCES

5. COMPLEX EVENT PROCESSING
•Event processing is a method of tracking and analyzing
(processing) streams of information (data) about things that
happen (events)
•Complex event processing, or CEP, is event processing that
combines data from multiple sources to infer events or
patterns that suggest more complicated circumstances.
• Start to appear in 1990
• Goal: identify meaningful events (such as opportunities or
threats) and respond to them as quickly as possible

6. EVENT PROCESSING USE CASES
• Network monitoring
• Intelligence and surveillance
• Risk management
• E-commerce
• Fraud detection
• Smart order routing
• Transaction cost analysis
• Pricing and analytics
• Market data management
• Algorithmic trading
• Data warehouse augmentation

7. REAL TIME USE CASES
http://www.digital4.biz

8. LAMBDA ARCHITECTURE
Generic, scalable and fault-tolerant data processing architecture […]
in which low-latency reads and updates are required.
http://lambda-architecture.net/

9. HADOOP BUT NOT ONLY THAT!
•Apache Hadoop Ecosystem is the typical solution nowadays
• “Mature” Option
• Flume (optional collector and streaming data movement system)
• Kafka (distributed messaging system)
• Storm (distributed real-time computation system)
• “Innovative” Option
• Spark + Spark Streaming
•Very powerful, but very complex

10. WHY AZURE?
•Due to the high scalability and computing power that a
streaming solution may require, the cloud is a perfect
environment for it
•Very cheap and Very Simple to start a project
•Very well integrated with all other Azure offerings
• From Monitoring to Power BI

11. STREAM ANALYTICS
•Real-Time (somehow) complex event processing engine
•Enables real-time event processing in a very simple and
cheap way
• SQL-Like language
• Temporal Semantic Support
• (but different from SQL Server 2016)
•Azure Only at present time

12. STREAM ANALYTICS
•Platform-as-a-Service
• Can handle millions of events per second
• Based on the REEF project (now Apache incubated)
•Main objects: Job, Query, Functions, Input & Outputs
• Totally manageable from a REST interface
•“Streaming Units” is the base concept to manage
performance, scalability and costs
• Roughly 1 Streaming Units = 1 MB/Sec of throughput

13. DATA INGESTION
•Inputs for Stream Analytics
• Streaming Sources (“Data in motion”)
• JSON, CSV or AVRO
• Reference Data (“Data at rest”)
• JSON or CSV
• Blob Store (max 50MB)
•Streaming Sources
• Event Hubs
• IoT Hub

14. DEMO

15. STREAM ANALYTICS QUERY ENGINE
•Take date from one or more input
•Send resulting data to one or more output
•Support most common data types:
• bigint, float, unicode strings, datetime
• key-value pairs
• arrays

16. STREAM ANALYTICS QUERY LANGUAGE
•Stream Analytics Query Language Reference
• https://msdn.microsoft.com/library/azure/dn834998.aspx
•Subset of T-SQL
•With specific temporal extension
• Time values to be used can be set using TIMESTAMP BY directive

17. STREAM ANALYTICS QUERY LANGUAGE
DML Statements
• SELECT
• FROM
• WHERE
• GROUP BY
• HAVING
• CASE
• JOIN
• UNION
Windowing Extensions
• Tumbling Window
• Hopping Window
• Sliding Window
• Duration
Aggregate Functions
• SUM
• COUNT
• AVG
• MIN
• MAX
Scaling Functions
• WITH
• PARTITION BY
Date and Time Functions
• DATENAME
• DATEPART
• DAY
• MONTH
• YEAR
• DATETIMEFROMPARTS
• DATEDIFF
• DATADD
String Functions
• LEN
• CONCAT
• CHARINDEX
• SUBSTRING
• PATINDEX
Statistical Functions
• VAR
• VARP
• STDEV
• STDEVP

18. ADVANCED FEATURES
•Partitioning Support
• Specially useful for high scalability
•CTE-Like constructs that also helps scaling out
•Temporal aggregations
• Tumbling, Hopping and Sliding Windows
•Join between input streams

19. DEMO

20. STREAM ANALYTICS AND MACHINE LEARNING
•Apply AzureML model to streaming data
•Sample use-cases
• Fraud Detection
• Product Recommendation
• Customer Sentiment Analysis
•Right now in preview and available only through the “old”
portal
• https://manage.windowsazure.com/

21. DEMO

22. STREAM ANALYTICS ALTERNATIVE (ON AZURE)
•Apache Storm
•IaaS and not PaaS
•Much more complex to manage and develop…but much
more powerful
• https://azure.microsoft.com/en-
us/documentation/articles/stream-analytics-comparison-storm/

23. STREAM ANALYTICS ON-PREMISES?
•Apache Hadoop Ecosystem
• Flume / Kafka / Storm
•StreamInsight
• CEP solution part of the SQL Server Platform
•EventStore
• Javascript OpenSource CEP
•None of them has native temporal extension

24. ADDITIONAL RESOURCES
•Online Documentation
•Stream Analytics Reference Architecture
•Lambda Architecture
•GitHub Repository

25. QUESTIONS & ANSWERS

27. TO DO LIST
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