Extending versatility of python to nonprogrammer

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This document discusses extending the versatility of Python to non-programmers through IBM SPSS software. It describes how IBM SPSS Modeler and Statistics allow building and scoring of Python models through their GUIs. Python can also be used for data preparation and generating charts and outputs. The document outlines options for scaling Python execution in Modeler, including on servers, Hadoop, and leveraging databases. It promotes a new IBM SPSS community and provides contact information for the presenter.

Software

Extending versatility of Python to nonprogrammer
by Armand Ruiz (IBM SPSS Product Manager)
1st Annual Conference on Python applications in Data Analysis, Machine Learning, and Web

About Me
• Role: IBM SPSS Product Manager Programmability
• E-mail: armand.ruiz@us.ibm.com
• Twitter: @armand_ruiz
• Website: http://www.armandruiz.com

Content available here:
http://tinyurl.com/nzgvn69

IBM SPSS Software:
IBM SPSS Modeler – Latest version is 17
IBM SPSS Statistics – Latest version is 23
Trials available

Extensibility with R/Python for Modeler AND Statistics
• Build and score R/Python models through the Modeler and Statistics GUIs
• Use R/Python functions for data preparation
• Generate R/Python Charts and Outputs within SPSS output management system
• Custom Dialog Builder:

Enterprise R/Python Scalability and Deployment
• A range of options exist for scaling R execution via Modeler. The following modes of operation are supported:
- Modeler Desktop
- Modeler Server (Python/R execution on Server Tier to minimise data movement)
- Pushdown to Analytic Server (Hadoop) leveraging Map Reduce (HUGE!!!)
- Database pushback through leveraging Netezza, SAP Hana or Oracle R engines
• Streams can be deployed/automated through:
- Modeler Batch
- C&DS Job Scheduling and Model Management
- C&DS Scoring Service/Solution Publisher/InfoSphere Streams
- IBM SPSS Analytical Decision Management

Social
Databases Models
And many more…!
IBM Predictive Extensions
Geospatial

New IBM SPSS Community https://developer.ibm.com/predictiveanalytics

Q&A
• E-mail: armand.ruiz@us.ibm.com
• Twitter: @armand_ruiz
• Website: http://www.armandruiz.com
https://developer.ibm.com/predictiveanalytics

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Beyond Event Sourcing - Embracing CRUD for Wix Platform - Java.IL

Natan Silnitsky

In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey. Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience. Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system. Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.

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Extending versatility of python to nonprogrammer

1. Extending versatility of Python to nonprogrammer by Armand Ruiz (IBM SPSS Product Manager) 1st Annual Conference on Python applications in Data Analysis, Machine Learning, and Web

2. About Me • Role: IBM SPSS Product Manager Programmability • E-mail: armand.ruiz@us.ibm.com • Twitter: @armand_ruiz • Website: http://www.armandruiz.com

3. Content available here: http://tinyurl.com/nzgvn69

4. IBM SPSS Software: IBM SPSS Modeler – Latest version is 17 IBM SPSS Statistics – Latest version is 23 Trials available

5. Extensibility with R/Python for Modeler AND Statistics • Build and score R/Python models through the Modeler and Statistics GUIs • Use R/Python functions for data preparation • Generate R/Python Charts and Outputs within SPSS output management system • Custom Dialog Builder:

6. Enterprise R/Python Scalability and Deployment • A range of options exist for scaling R execution via Modeler. The following modes of operation are supported: - Modeler Desktop - Modeler Server (Python/R execution on Server Tier to minimise data movement) - Pushdown to Analytic Server (Hadoop) leveraging Map Reduce (HUGE!!!) - Database pushback through leveraging Netezza, SAP Hana or Oracle R engines • Streams can be deployed/automated through: - Modeler Batch - C&DS Job Scheduling and Model Management - C&DS Scoring Service/Solution Publisher/InfoSphere Streams - IBM SPSS Analytical Decision Management

7. IBM SPSS Nodes

9. 9

10.

11. 11

12. Social Databases Models And many more…! IBM Predictive Extensions Geospatial

13. New IBM SPSS Community https://developer.ibm.com/predictiveanalytics

14. Q&A • E-mail: armand.ruiz@us.ibm.com • Twitter: @armand_ruiz • Website: http://www.armandruiz.com https://developer.ibm.com/predictiveanalytics

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