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English
  • Common Content for Data Engineering 10.5.4
  • All Products

Table of Contents

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  1. Preface
  2. Mappings
  3. Mapplets
  4. Mapping Parameters
  5. Where to Assign Parameters
  6. Mapping Outputs
  7. Generate a Mapping from an SQL Query
  8. Dynamic Mappings
  9. How to Develop and Run a Dynamic Mapping
  10. Dynamic Mapping Use Cases
  11. Mapping Administration
  12. Export to PowerCenter
  13. Import From PowerCenter
  14. Performance Tuning
  15. Pushdown Optimization
  16. Partitioned Mappings
  17. Developer Tool Naming Conventions

Developer Mapping Guide

Developer Mapping Guide

Back Next

Functions for Cloud Data Warehouse Applications

Functions for Cloud Data Warehouse Applications

The following table displays the Informatica functions available for pushdown optimization for Amazon Redshift and Snowflake:
Function
Amazon Redshift
Snowflake
ABS()
Yes
Yes
ADD_TO_DATE()
Yes
Yes
ASCII()
No
Yes
AVG()
Yes
Yes
CEIL()
Yes
Yes
CHR()
Yes
Yes
CHRCODE()
No
Yes
CONCAT()
Yes
Yes
COS()
Yes
No
COSH()
No
Yes
COUNT()
Yes
No
DATE_COMPARE()
Yes
No
DATE_DIFF()
Yes
Yes
DECODE()
Yes
Yes
EXP()
Yes
Yes
FIRST()
No
Yes
FLOOR()
Yes
Yes
GET_DATE_PART()
Yes
Yes
IIF()
Yes
Yes
IN()
Yes
No
INITCAP()
Yes
Yes
INSTR()
Yes
No
ISNULL()
Yes
No
LAST_DAY()
Yes
No
LENGTH()
Yes
Yes
LN()
Yes
Yes
LOG()
No
Yes
LOWER()
Yes
Yes
LPAD()
Yes
Yes
LTRIM()
Yes
Yes
MAX()
Yes
Yes
MEDIAN()
No
Yes
MIN()
Yes
Yes
MOD()
Yes
Yes
POWER()
Yes
Yes
ROUND(DATE)
No
Yes
ROUND(NUMBER)
Yes
Yes
RPAD()
Yes
Yes
RTRIM()
Yes
Yes
SIGN()
Yes
Yes
SIN()
Yes
No
SINH()
No
Yes
SQRT()
Yes
Yes
STDDEV()
Yes
Yes
SUBSTR()
Yes
Yes
SUM()
Yes
No
SYSTIMESTAMP()
Yes
No
TAN()
Yes
No
TANH()
No
Yes
TO_BIGINT
Yes
Yes
TO_CHAR(DATE)
Yes
Yes
TO_CHAR(NUMBER)
Yes
Yes
TO_DATE()
Yes
Yes
TO_DECIMAL()
Yes
Yes
TO_DECIMAL38()
Yes
Yes
TO_FLOAT()
Yes
Yes
TO_INTEGER()
Yes
Yes
TRUNC(DATE)
Yes
Yes
TRUNC(NUMBER)
Yes
Yes
UPPER()
Yes
Yes
VARIANCE()
Yes
Yes
If a function is not listed, the Integration Service cannot push that function to any database.
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