Table of Contents


  1. Preface
  2. Introduction to Informatica Data Engineering Integration
  3. Mappings
  4. Mapping Optimization
  5. Sources
  6. Targets
  7. Transformations
  8. Python Transformation
  9. Data Preview
  10. Cluster Workflows
  11. Profiles
  12. Monitoring
  13. Hierarchical Data Processing
  14. Hierarchical Data Processing Configuration
  15. Hierarchical Data Processing with Schema Changes
  16. Intelligent Structure Models
  17. Blockchain
  18. Stateful Computing
  19. Appendix A: Connections Reference
  20. Appendix B: Data Type Reference
  21. Appendix C: Function Reference

Window Functions

Window Functions

Window functions calculate a return value for every input row of a table, based on a group of rows.
A window function performs a calculation across a set of table rows that are related to the current row. You can also perform this type of calculation with an aggregate function. But unlike regular aggregate functions, a window function does not group rows into a single output row. The rows retain unique identities.
You can define the LEAD and LAG analytic window functions in an Expression transformation. LEAD and LAG give access to multiple rows within a table, without the need for a self-join.


We’d like to hear from you!