Table of Contents

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  1. Preface
  2. Introduction to Informatica Big Data Management
  3. Connections
  4. Mappings in the Hadoop Environment
  5. Mapping Objects in the Hadoop Environment
  6. Processing Hierarchical Data on the Spark Engine
  7. Stateful Computing on the Spark Engine
  8. Monitoring Mappings in the Hadoop Environment
  9. Mappings in the Native Environment
  10. Profiles
  11. Native Environment Optimization
  12. Data Type Reference
  13. Complex File Data Object Properties
  14. Function Reference
  15. Parameter 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.


Updated November 09, 2018