How Do You Use Open Source With SAS® Event Stream Processing?
Did you miss the Ask the Expert session on using open source with SAS® Event Stream Processing? Not to worry, you can catch it on-demand at your leisure.
Learn how to use SAS® with open source to extract value from streaming data. Through screen share, you’ll see a product demonstration and have access to a free trial.
You will learn:
- How to use open source with SAS® Event Stream Processing.
- Technical knowledge for integration of Python code in SAS® Event Stream Processing.
- How to use Python and the visual drag-and-drop interface in the SAS® Event Stream Processing environment.
Here are the questions from the Q&A segment held at the end of the session for ease of reference. My slides are attached to this post.
Can I use Python to make a REST call from a streaming project?
Yes, you can. No inherent limit to what can be done with the Python code that you put into ESP.
With Python running in ESP, how can I visualize the results? Do I have to write all of the results out to a database?
Surface that using a calculate window. Use stream viewer to visualize what is happening in that window. If using Jupiter notebook, can use regular visualization tools.
I see that you’re integrating Python into ESP, but does ESP call out to Python and if it does, wouldn’t that result in slower performance?
The more native functionality you use inside an ESP project, the better the performance will be. Every tool doesn’t have an equal level of performance. Runs great on performance grade hardware.
How do you integrate with AWS IoT products?
See other response for connectors and adapters from ESP for integration with AWS offerings. SAS ESP is cloud-native and can be deployed in the AWS environment and co-exist within that environment.
Are there any technical content on how AWS and ESP integrate?
SAS ESP provides connectors and adapters to read/write data for Kinesis, which enables data transport within the AWS environment. Docs on these and other connectors can be found at support.sas.com for the ESP product.
It is confusing when PyMAS needs to be used. Do I understand correctly that PyMAS is only used when running Python/C code wrapped by DS2? Is the PyMAS not using in case of the executing raw Python or C in Calculate Window? And PyMAS related environment variables are not needed to be declared.
The use of MAS capability runs in the same memory space as ESP for performance and is used for executing DS2 and Python. SAS ESP and the MAS module approach supports running Python natively when the approach is followed by Evan (using the function signature for entry point).
You mentioned that C%2B%2B code can be executed in MAS in ESP. But at the project's MAS declaration there are only Python, C and DS2 options. Therefore, to run C%2B%2B code should I use C option or there is another way to run C%2B%2B?
AS ESP supports Python, C, and DS2 code execution in the MAS module approach. C is supported so that users can write plug-ins for the ESP Procedural window.
Is there any coloration between SAS visual analytics and stream visual?
SAS Visual Analytics supports data exploration and analytics on large volumes of data for in-memory analysis. In contrast, SAS ESP Streamviewer supports real-time visualization of streaming insights created by SAS ESP. From a Streamviewer object, you can link to a SAS Visual Analytics pre-built report to link real time alerts with SAS VA's historical analysis.
What is the approximate latency in visualization results if we use SAS Visual Analytics instead of ESP Streamview?
Don’t think of it as a real-time dashboard.
If you already have, or are generating DS2 code, and you want to call a Python function, the DS2 PYMAS package lets you do that.
Yes, that is correct.
I suggest looking into the other Ask the Expert sessions on Event Stream Processing for additional information.
Recommended Resources
Event Stream Processing Support Page
Event Stream Manager Product Brief
Event Stream Processing for Edge Product Brief
Event Stream Processing Solution Brief
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