Python ETL Magic: Syncing Fleet Driver Eligibility Between HR and Operations
When facing the challenge of synchronizing driver eligibility data between our ERP, ADP, and fleet management systems, the client needed a practical solution that would establish a reliable workflow.
ETL Challenge
The complexity of this project stemmed from the need to:
Extract comprehensive employee data from ADP's API using pagination
Query a SQL database efficiently for driver eligibility information
Update ADP through their event-based API architecture
Technical Implementation
The solution required careful consideration of several technical aspects:
Data Retrieval from ADP Use of a Python script handled pagination by implementing a $top and $skip approach to retrieve all employee records without overwhelming the API. This required proper error handling and authentication with certificate-based security.
Efficient Database Operations To minimize SQL overhead, I processed data in batches, retrieving driver eligibility status for groups of employees rather than making individual queries. This significantly reduced database load and improved execution time.
ADP Custom Field Utilization I leveraged ADP's custom indicator fields to store driver eligibility information, allowing this data to become part of the standard employee record accessible to other systems.
API Update Management The most intricate part was constructing proper event payloads for ADP's API. Each update required specific JSON formatting to identify both the employee and the custom field being modified.
Key Takeaways
From the outside this simple update seems small, yet it a very complex one time ETL workflow. It highlighted important considerations for building reoccurring data workflows between disparate systems:
Identify a clear source of truth early in the project
Design custom fields that will serve downstream systems' needs
Create efficient batch operations when working with large datasets
Test thoroughly with small samples before full implementation
While the script was a one-time solution to establish the initial data flow, it established a pattern that could be adapted for ongoing synchronization if needed in the future.
The success of this ETL data synchronization demonstrates how targeted Python scripts can bridge critical gaps between enterprise systems.