Week 2 – Source & Schema: Ingesting Employee Data

📘 Reference: Full Dayforce Integration Studio Administrator Guide 

Objective 

Wire up your source data, define schemas, and validate your Employee data structure in Dayforce Integration Studio. 

Overview 

This week focuses on connecting Dayforce to a data source, defining the schema structure for your integration, and running your first test import or export using a small dataset. You’ll learn how schemas control validation and how to align field data types with Dayforce expectations. 

Source Types 

• API Sources: Use Dayforce HCM Anywhere APIs (like Employee Bulk API) to pull live data. 

• File Sources: Use CSV, XML, or JSON files uploaded to the Dayforce SFTP Import folder. 

• Reports: Use existing Dayforce Reports via the Analytics Data Connector. 

Tip: For repeatable integrations, APIs are preferred for flexibility and automation; reports are best for quick validation or one-time exports. 

Lab – Configure Your Source (30-45 min) 

1. Select Build My Own (Outbound) or Inbound connector type based on your challenge design.
2. In Step 2 (Define the source details), choose either the Employee Bulk API or a sample JSON file as your data source.
3. Click Review Source Schema at the bottom of Step 2 to review the source data structure in a graphical format. Identify the main array (e.g., Data for Employee Bulk).
4. Save the integration. Click View IDL at the top of Step 3 to review the source schema in Integration Description Language (IDL) code. 

Example Schema Snippet (IDL) 

block { 
  id = "EmployeeData"; 
  block { 
    id = "Employee"; 
    allow_multiple = true; 
    field { id = "XRefCode"; data_type = "string"; required = true; } 
    field { id = "FullName"; data_type = "string"; required = true; } 
    field { id = "HireDate"; data_type = "date"; required = false; } 
    field { id = "WorkLocation"; data_type = "string"; required = false; } 
  } 
} 
 

Tip: Match Dayforce field types to your schema data_type. If a mismatch occurs (like treating a date as text), your test run may fail validation. 

Lab – Validate Your Schema 

1. Import a sample file or define some fields in Step 3 to create a simple destination data structure.
2. Map each destination data field to the desired field from the source data.
3. Run a test integration with 5–10 employee records.
4. Check the Integration Log for any validation or transformation errors.
5. Adjust field names or data types in your schema as needed.
6. Review the schema to identify arrays, which are represented with the setting allow_multiple = true 

Validation Checklist 

• ✅ Schema validates successfully 

• ✅ Dates parse correctly (e.g.,MM/dd/yyyy) 

• ✅ Only expected fields are included 

Checkpoint 

Share your schema snippet and Integration Log summary with your team for peer review. Confirm readiness to move to mapping and transformation in Week 3. 

Next Week Preview: Week 3 dives into mapping, IDL expressions, and transformation logic to prepare your final CSV output. 

💬 Join the Discussion 

Head to the Integration Studio Challenge Forum and share: 

• Your mapping plan (table or diagram) 

• The data source you’re using 

• Any lessons learned while planning 

Your post helps other partners learn and collaborate — and might even spark your first integration partnership.