Guiding effective data merging from SAP platforms through skillful engineering practices
Prompt Engineering in Advanced Data Engineering Settings: A Case Study with SAP HCM System
Prompt engineering has emerged as a powerful tool in integrating and analyzing complex data sources, particularly in systems like SAP HCM. Here's a step-by-step guide on how prompt engineering can be used to solve a specific problem in such a setting.
The Problem
The business case revolves around determining the number of employees and their organizational assignment for a specific point in time, using data stored in a SAP HCM system.
The Solution
Christian Koch, an Enterprise Architect at BWI GmbH and Lecturer at the Nuremberg Institute of Technology Georg Simon Ohm, leverages prompt engineering to create a solution using PySpark code.
Step 1: Preparing the Data
The code first infers the Database Schema, including example datasets and field descriptions, to ChatGPT using few-shot prompting. This step ensures that the language model understands the structure of the data.
Step 2: Joining the Data
The code recognizes the join criteria for both tables based on Column STAT2 of Table PA0000 and column STATV of table T529U. Both code versions will create a dataframe for the flattened hierarchical organization structure.
Step 3: Filtering the Data
In this step, a DataFrame is created that contains date values from 2020-01-01 to 2024-01-01 to join all valid employees according to their entry and possible exit dates. This is achieved using SAP's HRP1001 table.
Step 4: Organizing the Data
A DataFrame is built to represent the organizational structure of the company and determine each object's organizational assignment, specifically the highest level organizational unit. The language model produces a recursive function to dissolve the hierarchy in the organizational structure.
Markus Stadi, a Senior Cloud Data Engineer at Dehn SE, working in the field of Data Engineering, Data Science, and Data Analytics for many years, emphasizes the importance of using business descriptions as column aliases to improve readability. Users who prefer common table expressions (CTEs) can provide the hint in the input prompt for a more readable and understandable PySpark statement.
In conclusion, prompt engineering offers a promising approach for integrating and analyzing diverse data sources in advanced settings like SAP HCM systems. By creating precise and effective prompts, data engineers can leverage AI to simplify data retrieval, enhance data analysis, and provide customized outputs, despite the challenges posed by data complexity, privacy, and integration with legacy systems.
As AI technology evolves, prompt engineering may integrate with broader practices like context engineering, focusing on providing strategic layers of information to improve AI performance in complex tasks. This could lead to more sophisticated data integration and analysis capabilities in SAP HCM systems and similar environments.
- In the field of business, understanding the organizational structure of a company is essential, and prompt engineering can help solve complex data problems, as demonstrated in the case study with SAP HCM system.
- With prompt engineering, professionals in finance, data-and-cloud-computing, and technology can create precise and effective prompts to help AI simplify data retrieval and analysis, making it essential for careers in data engineering and analytics.
- Online education platforms can leverage prompt engineering to provide learners with education and self-development opportunities in data engineering, making it easier for individuals to acquire the skills needed for careers in various industries.
- Sports-betting industry could benefit from prompt engineering to process large amounts of data required for market analysis, strategy development, and making informed predictions, leading to improved business outcomes.
- As AI technology evolves, prompt engineering may integrate with context engineering, providing strategic layers of information to improve AI performance in complex tasks, which could lead to more sophisticated data integration and analysis capabilities in education-and-self-development, sports, and other settings.
