Course Code: 31430

PeopleSoft Query

Class Dates:
1/8/2026
3/30/2026
Length:
2 Days
Cost:
$2595.00
Class Time:
Technology:
Server
Delivery:
Instructor-Led Training, Virtual Instructor-Led Training

Overview

  • Course Overview
  • This course teaches participants how to create SQL queries using basic and advanced features of the PeopleSoft Query tool. Students will learn the fundamentals of Query Manager, including how to retrieve information from multiple tables, define criteria, use aggregate functions, and accept data input by users for filtering results. Students will also learn how to create various types of joins, expressions, subqueries, and unions. The hands-on activities in this course are created using real-world examples and data.
  • Audience
  • Marketers: To define target groups for recruitment, events, or general communication. Administrators/Staff: To create lists for specific operational needs (e.g., list of employees in a certain role).

Prerequisites

  • To use PeopleSoft Query effectively, prerequisites generally include basic PeopleSoft navigation, familiarity with relational databases, core SQL concepts (joins, filters), and ideally, some PeopleTools knowledge

Course Details

  • Foundational Knowledge
  • PeopleSoft Basics: Comfortable navigating the PeopleSoft application interface.
  • Database Concepts: Understanding of relational databases (tables, fields, relationships).
  • SQL Fundamentals: Basic knowledge of SQL (Structured Query Language) for data selection, joins, and filtering.
  • PeopleTools (Recommended): Familiarity with PeopleTools provides deeper context.
  • Key Skills Learned in Training
  • Query Design: Selecting records, applying filters, using conditions, and performing joins.
  • Expressions & Functions: Using built-in functions (SUM, AVG, COUNT) and creating expressions for calculations.
  • Runtime Prompts: Allowing users to input values (dates, IDs) to filter results dynamically.
  • Security: Understanding query access, security records, and profiles.
  • Optimization: Techniques for improving performance on large datasets.