This course teaches you how to build SAS Event Stream Processing applications that ingest high-volume and high-velocity data streams, respond in real time, and store only relevant data elements. The course discusses basic concepts of event stream processing and introduces the component objects with which to build event stream processing applications.
Learn How To
Describe the architecture of SAS Event Stream Processing.
Develop and test SAS Event Stream Processing applications using the XML interface as well as the SAS Event Stream Processing Studio interface.
Execute SAS Event Stream Processing applications from a command-line interface.
Use SAS Event Stream Processing connectors and adapters to read data from, and write data to, various event stream formats.
Explain the concepts behind SAS Event Stream Processing transformations.
Use the transformations to ingest, filter, join, and aggregate event streams.
Use additional transformations to execute external routines in Python, C/C++, and DS2 against event streams and to detect patterns and anomalies in event streams.
Discuss performance optimizations.
Use SAS Event Stream Manager to deploy and monitor SAS Event Stream Processing applications into non-production and production environments.
Who Should Attend
Developers and architects who design and create applications that process IoT (Internet of Things) streams in real time, operators who manage IoT project deployments, and data scientists and analysts who need to gain insights from streaming data while it is in motion
Prerequisites
Familiarity with XML and familiarity with the UNIX/Linux command-line interface are required.
SAS Products Covered
SAS Event Stream Processing;SAS Data Quality Solution;SAS Analytics Platform
Course Outline
Introduction to SAS Stream Event Processing
Define event streams and the Internet of Things (IoT).
Explain the purpose of SAS Event Stream Processing.
Identify the components of SAS Event Stream Processing.
SAS Event Stream Processing Models
Describe the SAS Event Stream Processing model hierarchy.
Describe the SAS Event Stream Processing publish and subscribe framework.
Examine an XML model.
Examine real-time event processing.
SAS Event Stream Processing Studio
Describe the main model development functions of SAS Event Stream Processing Studio.
Examine XML Editor caveats.
Event Transformations
Describe SAS Event Stream Processing transformations and windows.
Explain the function of indexes.
Describe and demonstrate the source, union, and filter windows.
Describe the SAS Event Stream Processing operation codes.
Explain the canonical set of events.
Describe and demonstrate the compute, copy, aggregate, and counter windows.
Inputs and Outputs
Review the publish and subscribe system in SAS Event Stream Processing.
List available connectors and adapters.
Identify the publish and subscribe parameters.
Define and demonstrate the project connector.
Define and demonstrate the CAS adapter.
Define and demonstrate the adapter connector.
Advanced Event Transformations
Describe and demonstrate the capabilities of the join window.
Define and demonstrate connector orchestration.
Introduce and demonstrate the pattern window.
Describe pattern matching with events of interest and logic expressions.
Describe the calculate window and demonstrate execution with Python, C/C++, and DS2 code.
Discuss SAS Event Stream Processing design patterns and application performance tuning.
SAS Event Stream Manager
Describe the main functions of SAS Event Stream Manager.
Introduce and demonstrate deployments into non-production and production environments.
Demonstrate how to register ESP server instances with SAS Event Stream Manager.
Explain and demonstrate how to upload Event Stream Processing projects to SAS Event Stream Manager.
Describe and demonstrate the monitoring functions in SAS Event Stream Manager.
The hands-on lab is preconfigured to support this course and will not support hands-on practice for all your enrolled courses.
Hands-On Lab Reservation System
When you are planning your study time, keep in mind that the virtual lab takes 30-45 minutes to start
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