A Beginner's Guide to Learning the OSIsoft PI System

In today's data-driven world, the ability to collect, analyze, and visualize real-time data is crucial for making informed decisions. The OSIsoft PI System is a powerful suite of software tools designed to handle high-fidelity, time-series data from various sources. Whether you're an engineer, data scientist, or software developer, learning the PI System can significantly enhance your skill set and open up new opportunities in industries like energy, manufacturing, and utilities.

This blog post will guide you through the steps to start learning the OSIsoft PI System effectively, providing a structured learning path and highlighting key resources to accelerate your journey.

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Understanding the OSIsoft PI System

Before diving into the learning process, it's essential to grasp what the PI System is and its core components.

What is the PI System?

The PI System is an enterprise infrastructure for managing real-time data and events. It collects data from various sources like sensors, equipment, and control systems, storing it in a time-series database. The system enables users to:

• Collect: Gather data from multiple sources in real-time.
• Store: Maintain high-fidelity historical data for analysis.
• Analyze: Perform calculations and derive insights from the data.
• Visualize: Create dashboards and displays to monitor operations.
• Share: Distribute data and insights across the organization.

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Structured Learning Path

To master the PI System, it's beneficial to follow a structured learning path that builds upon each component's understanding.

1. Start with the Basics

PI System Overview

• Objective: Understand the overall architecture and capabilities.
• Focus Areas:
  • Core components (PI Data Archive, PI Asset Framework).
  • How data flows through the system.
  • Use cases in different industries.

2. Dive into Core Components

PI Data Archive

• Objective: Learn how data is stored and retrieved.
• Focus Areas:
  • Time-series data storage concepts.
  • Data compression and archiving.
  • Basic administration tasks.

PI Interfaces and Connectors

• Objective: Understand how data is collected from sources.
• Focus Areas:
  • Configuring interfaces for different data sources.
  • Data collection mechanisms.
  • Troubleshooting data collection issues.

PI Asset Framework (AF)

• Objective: Organize data into a meaningful structure.
• Focus Areas:
  • Building AF hierarchies and models.
  • Defining elements and attributes.
  • Utilizing templates for consistency.

3. Visualization and Analysis

PI Vision

• Objective: Learn to create interactive dashboards.
• Focus Areas:
  • Building displays and trends.
  • Using symbols and graphics.
  • Sharing displays with stakeholders.

PI DataLink

• Objective: Integrate PI data with Microsoft Excel.
• Focus Areas:
  • Retrieving data for reports.
  • Performing ad-hoc analyses.
  • Automating data refreshes.

4. Advanced Topics for Developers

As a software engineer with experience in Python and C#, you can leverage your skills to interact with the PI System programmatically.

PI AF SDK (C#)

• Objective: Access and manipulate PI System data using C#.
• Focus Areas:
  • Setting up development environment.
  • Reading and writing data.
  • Managing AF elements and attributes.

PI Web API (Python and C#)

• Objective: Interact with PI System over RESTful services.
• Focus Areas:
  • Authenticating and making API calls.
  • Fetching and updating data.
  • Handling JSON responses in your applications.

Scripting and Automation with Python

• Objective: Automate tasks and perform data analysis.
• Focus Areas:
  • Using libraries like requests for API calls.
  • Data manipulation with pandas.
  • Integrating with data science workflows.

5. Hands-On Practice

• Set Up a Development Environment: If possible, get access to a test PI System environment.
• Work on Sample Projects: Implement small projects to reinforce learning.
• Contribute to Community Projects: Engage with open-source projects or examples provided by the community.

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Essential Resources

To support your learning journey, make use of the following resources:

YouTube Channels

1. OSIsoft Learning

   • Content: Tutorials, webinars, product demos.
   • Link: OSIsoft Learning YouTube Channel

2. PI Square Community Videos

   • Content: Community-contributed tutorials and use cases.
   • Link: PI Square YouTube Channel

Online Documentation

• OSIsoft PI System Documentation
  • Content: Official guides and reference materials.
  • Link: OSIsoft Documentation

Communities and Forums

• PI Square Community
  • Content: Forums, blogs, and discussion groups.
  • Link: PI Square Community

Social Media

LinkedIn Pages

1. OSIsoft

   • Updates on products and industry trends.
   • Follow on LinkedIn

2. PI Developers Club

   • Networking with other PI System developers.
   • Join the Group

Twitter Accounts

1. OSIsoft (@OSIsoft)

   • Latest news and updates.
   • Follow on Twitter

2. PI Developers Club (@PIDevClub)

   • Developer-focused tips and resources.
   • Follow on Twitter

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Practical Tips for Developers

As a seasoned software engineer, you can accelerate your learning by focusing on areas that leverage your programming skills.

Integrating PySpark with PI System

• Objective: Process PI System data using PySpark for big data analytics.
• Approach:
  • Use the PI Web API to fetch data into your PySpark environment.
  • Handle data transformations and analyses in a distributed manner.
  • Consider data volume and optimize for performance.

Accessing Full PI Point Data in Python

• Use PI Web API:

  • Fetch complete historical data for a PI Point.
  • Handle authentication (Kerberos for Windows credentials).
  • Manage SSL certificates and warnings appropriately.

• Sample Code Snippet:

  import requests
  from requests_kerberos import HTTPKerberosAuth, OPTIONAL
  
  url = "https://your-piwebapi-server/piwebapi/streams/{webId}/recorded"
  auth = HTTPKerberosAuth(mutual_authentication=OPTIONAL)
  
  params = {
      'startTime': '*-1y',
      'endTime': '*',
      'maxCount': 100000
  }
  
  response = requests.get(url, auth=auth, params=params, verify=False)
  data = response.json()
  

Setting Up Development Environment

• Windows vs. macOS:
  • PI System tools are primarily Windows-based.
  • Use virtual machines or remote desktops if on macOS.
  • Leverage cross-platform tools like PI Web API for development.

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Conclusion

Learning the OSIsoft PI System is a valuable investment for professionals in industries reliant on real-time data. By following a structured learning path, utilizing available resources, and leveraging your existing programming skills, you can effectively master the PI System.

Remember to engage with the community, participate in forums, and continuously explore new features and updates. The combination of practical experience and community support will accelerate your proficiency and open up new opportunities in your career.

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Additional Resources

• OSIsoft Learning Courses

  • Explore online courses and training modules.
  • OSIsoft Learning

• PI World Conferences

  • Attend events to network and learn from experts.
  • PI World Events

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Happy learning, and welcome to the world of real-time data with the OSIsoft PI System!