A Guide to Filtering and Processing CSV Data Using PI UFL

A Guide to Filtering and Processing CSV Data Using PI UFL

In today's data-driven world, the ability to efficiently manage and process data across multiple sites is pivotal for any organization. One common challenge faced by many companies is dealing with varied CSV files containing site-specific data yet conforming to a single template. PI UFL (Universal File Loader) offers a robust solution for this, allowing for effective data filtration and extraction, enabling the seamless transfer of vital data into the PI System.

Understanding PI UFL

PI UFL is a tool that facilitates the importing of unstructured data from files into the PI System. It utilizes configuration files (.INI files) to parse the data, transforming it into structured PI Tags. This flexibility is particularly useful when dealing with CSV files from various sites that require a unified approach to data processing.

Problem Statement

Imagine having a single folder containing CSV files, each named differently for different sites, but all following the same data template. The goal is to filter and store specific location data into appropriate PI Tags using a single UFL INI File.

Strategy for Filtering and Storing Data

1. File Navigation and Initial Setup

   • Ensure that all CSV files are located in a single directory accessible to the PI UFL system.
   • Develop a plan for handling filenames to identify data origins during the UFL parsing process.

2. Utilizing IF Statements for Filtration

   • Leverage IF statements within the INI configuration to distinguish between location-specific data. This is crucial for deciding which lines of data should be processed and stored.
   • Example configuration line:

     IF (Location == "desired_location") THEN
        ... [process data] ...
     END IF
     

   • Such logic ensures that only data relevant to specified locations is extracted, reducing unnecessary data processing.

3. Extracting Location Data

   • Use string manipulations to extract location information from the CSV lines. This involves understanding the structure of each line in the CSV.
   • An example extraction would look something like:

     Location = ["(*),*"]
     

   • This code takes everything before the first comma, assuming the location identifier precedes it.

4. INI File Configuration

   • Develop a comprehensive INI configuration that considers each line of your CSV files as a text string. This approach simplifies the manipulation and extraction processes.
   • Engage in iterative testing to refine the configuration's effectiveness — preview, modify, test, and adjust.

Conclusion

PI UFL is a powerful utility that, when configured correctly, provides a streamlined approach to handling CSV file data across multiple sites. By strategically utilizing IF statements, extracting key information, and methodically configuring the INI files, you can ensure accurate data filtering and storage within PI Tags. This aligns operational data with business needs, enabling deeper insights and informed decision-making.

Further Reading

For those interested in deepening their understanding of PI UFL, I recommend exploring the official PI UFL user guide. Additionally, communities such as PI Square offer a wealth of shared strategies, experiences, and solutions that can enhance your data integration efforts with PI UFL.