Building Supporting Coding Skills for IBM Planning Analytics Development

Becoming an expert at IBM Planning Analytics development requires proficiency in multiple syntaxes and languages. TurboIntegrator and Rule scripting has limited documentation and learning resources outside of the official IBM PA documentation. However, several other coding languages with extensive resources are invaluable for building foundational skills. These skills empower developers to interact with data sources, customize solutions, and optimize processes effectively. Here’s a guide to some of the key languages and resources for learning more about them.

  1. SQL: Querying Data Sources

SQL is essential for interacting with databases, extracting data, and preparing it for use in Planning Analytics models. SQL is used in Planning Analytics in every TI process that uses a Database Connection as the data source. Developing SQL skills will allow developers to leverage more complex queries and perform calculations within them that can expand process capabilities.

  • Key Skills to Focus On:
    • Writing queries to retrieve and manipulate data.
    • Understanding joins, subqueries, and aggregate functions.
    • Common SQL Query errors and resolutions.
  • Practical Applications:
    • Connecting data sources for TI processes.
    • Exploring and validating data before loading it into a Planning Analytics cube.
    • Performing calculations on records in the query instead of the TI process.
  • Recommended Resources:
  1. MDX: Subsets and Views

Multidimensional Expressions (MDX) is crucial for querying multidimensional data views and creating custom subsets in Planning Analytics. MDX is primarily used in the Subset Editor, and defining and sharing cube views.

  • Key Skills to Focus On:
    • Building dimension subsets by leveraging PA specific MDX functions.
    • Using functions like FILTER, ORDER, and Member attributes.
    • Cube, dimension, and member reference syntax.
  • Practical Applications:
    • Designing dynamic reports and dashboards.
    • Creating MDX views for TurboIntegrator processes.
  • Recommended Resources:
  1. Shell Scripting and Command Line: Operating System Interfacing

Operating system specific syntax enables direct interaction with the server environment where Planning Analytics is installed. This is primarily enabled by leveraging the ExecuteCommand function in a TI process. Command line syntax is used for Windows based operating systems, and Shell scripting is used for Linux based operating systems.

  • Key Skills to Focus On:
    • Writing scripts for file manipulation and text processing.
    • Automating routine tasks and system-level operations, such as archiving or log cleanup.
  • Practical Applications:
    • Searching files for matching strings.
    • Managing Planning Analytics logs and backups.
  • Recommended Resources:
  1. Python: General Coding Practice

Python is a versatile language for scripting, data manipulation, and can integrate with Planning Analytics using tm1py and DataWORQ. Additionally, there are not many resources for practicing solving problems using TI processes, so substituting for a widely supported language is helpful.

  • Key Skills to Focus On:
    • Solving problems using data manipulation.
    • Practicing with common operators and iterators.
    • Writing scripts for data extraction, transformation, and loading (ETL).
    • Interacting with Planning Analytics through APIs using the tm1py library
    • Using libraries like pandas and numpy for data analysis.
  • Practical Applications:
    • Practicing common coding problems with a widely supported language.
    • Integrating Planning Analytics with external tools.
  • Recommended Resources:
  1. JavaScript: Code Snippets

With the addition to Code Snippets in Planning Analytics Workbench, JavaScript is a supporting language that is used to create custom snippets for TI Processes.

  • Key Skills to Focus On:
    • Converting TI process code into JavaScript Syntax
    • Basic syntax and control structures.
  • Practical Applications:
    • Creating reusable JavaScript snippets to simplify repetitive TI Process tasks, such as automated error handling or string parsing.
  • Recommended Resources:

Conclusion

Learning more about these supporting languages equips developers with additional skills to build robust, efficient, and scalable solutions in IBM Planning Analytics. The additional tools that become available with this knowledge can allow developers to tackle business and technical problems in ways that may not be possible by using Planning Analytics alone. Use the recommended resources to deepen your expertise and stay ahead in this dynamic field.