Extending versatility of python to nonprogrammer

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IBM SPSS Programmability

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

IBM SPSS Nodes

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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This presentation covers the following topics: What is logging? The purpose of logging: Debugging The purpose of logging: Security The purpose of logging: Stats & analytics Traditional logging Traditional logging: Advantages Traditional logging: Disadvantages The solution: Large-scale logging Large-scale logging: Core principles Large-scale logging: Solution types Large-scale logging: Cloud vs on-prem Large-scale logging: Operational complexity Large-scale logging: Security Large-scale logging: Costs Large-scale logging: On-prem comparison - Elasticsearch - Grafana Loki - VictoriaLogs On-prem comparison: Setup and operation On-prem comparison: Costs On-prem comparison: Full-text search support On-prem comparison: How to efficiently query 100TB of logs? On-prem comparison: Integration with CLI tools VictoriaLogs for large-scale logging VictoriaLogs demo instance - Ingestion rate: 3600 messages / minute - The number of log messages: 1.1 billion - Uncompressed log messages’ size: 1.5TB - Compressed log messages’ size: 23GB - Compression ratio: 47x - Memory usage: 150MB VictoriaLogs CLI integration demo - Which errors have occurred in all the apps during the last hour? - How many errors have occurred during the last hour? - Which apps generated the most of errors during the last hour? - The number of per-minute errors for the last 10 minutes - Status codes for the last hour - Non-200 status codes for the last week - Top client IPs for the last 4 weeks with 404 and 500 response status codes - Per-month stats for the given IP across all the logs Large-scale logging solution MUST provide excellent CLI integration VictoriaLogs: (temporary) drawbacks VictoriaLogs: Recap - Easy to setup and operate - The lowest RAM usage and disk space usage (up to 30x less than Elasticsearch and Grafana Loki) - Fast full-text search - Excellent integration with traditional command-line tools for log analysis - Accepts logs from popular log shippers (Filebeat, Fluentbit, Logstash, Vector, Promtail, Grafana Agent) - Open source and free to use!

Large-scale Logging Made Easy: Meetup at Deutsche Bank 2024

Large-scale Logging Made Easy: Meetup at Deutsche Bank 2024

Large-scale Logging Made Easy: Meetup at Deutsche Bank 2024

VictoriaMetrics

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ADR, or Architecture Decision Record, is a valuable tool in software development for several reasons. It provides a centralized location for documenting and tracking architectural decisions, aiding both current and future team members. ADRs enhance communication among team members by documenting the rationale behind architectural decisions, especially beneficial during onboarding of new team members or when revisiting decisions. They serve as a knowledge base, enabling teams to learn from past decisions and refine their decision-making process. Additionally, ADRs contribute to transparency by helping stakeholders understand the reasons behind specific architectural choices. As with any other tool or process, introducing them into an organization can face several obstacles, and overcoming these challenges is crucial for successful implementation. In this talk I go through some common problems and our way of solving them.

Architecture decision records - How not to get lost in the past

Architecture decision records - How not to get lost in the past

Architecture decision records - How not to get lost in the past

Papp Krisztián

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Announcing Codolex 2.0 from GDK Software

Announcing Codolex 2.0 from GDK Software

Announcing Codolex 2.0 from GDK Software

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WSO2CON 2024 Slides - Open Source to SaaS

WSO2CON 2024 Slides - Open Source to SaaS

WSO2CON 2024 Slides - Open Source to SaaS

WSO2CON 2024 - WSO2's Digital Transformation Journey with Choreo: A Platforml...

WSO2CON 2024 - WSO2's Digital Transformation Journey with Choreo: A Platforml...

WSO2CON 2024 - WSO2's Digital Transformation Journey with Choreo: A Platforml...

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WSO2CON 2024 - How to Run a Security Program

WSO2CON 2024 - How to Run a Security Program

WSO2CON 2024 - How to Run a Security Program

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WSO2CON 2024 - Navigating API Complexity: REST, GraphQL, gRPC, Websocket, Web...

WSO2CON 2024 - Navigating API Complexity: REST, GraphQL, gRPC, Websocket, Web...

WSO2CON 2024 - Navigating API Complexity: REST, GraphQL, gRPC, Websocket, Web...

WSO2Con2024 - WSO2's IAM Vision: Identity-Led Digital Transformation

WSO2Con2024 - WSO2's IAM Vision: Identity-Led Digital Transformation

WSO2Con2024 - WSO2's IAM Vision: Identity-Led Digital Transformation

Large-scale Logging Made Easy: Meetup at Deutsche Bank 2024

Large-scale Logging Made Easy: Meetup at Deutsche Bank 2024

Large-scale Logging Made Easy: Meetup at Deutsche Bank 2024

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Announcing Codolex 2.0 from GDK Software

Announcing Codolex 2.0 from GDK Software

Announcing Codolex 2.0 from GDK Software

AI & Machine Learning Presentation Template

AI & Machine Learning Presentation Template

AI & Machine Learning Presentation Template

WSO2CON 2024 Slides - Open Source to SaaS

WSO2CON 2024 Slides - Open Source to SaaS

WSO2CON 2024 Slides - Open Source to SaaS

WSO2CON 2024 - WSO2's Digital Transformation Journey with Choreo: A Platforml...

WSO2CON 2024 - WSO2's Digital Transformation Journey with Choreo: A Platforml...

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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

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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