Panoply In Devlink
Panoply provides continuous, automated data modeling and maintenance over Redshift. From ingestion to query, Panoply automates and simplifies data analytics, eliminating the overhead of preparing and modeling data, and managing cloud infrastructure.
Setup guidelink
Follow our step-by-step Panoply setup guide to connect Panoply with Fivetran.
Type transformation mappinglink
The data types in Panoply follow Fivetran's standard data type storage.
We use the following data type conversions:
Fivetran Data Type | Destination Data Type | Notes |
---|---|---|
BOOLEAN | BOOLEAN | |
SHORT | SMALLINT | |
INT | INTEGER | |
LONG | BIGINT | |
BIGDECIMAL | DECIMAL | |
FLOAT | REAL | |
DOUBLE | DOUBLEPRECISION | |
LOCALDATE | DATE | |
LOCALDATETIME | TIMESTAMP | |
INSTANT | TIMESTAMP | |
STRING | VARCHAR or TEXT | VARCHAR if bytelength is present, else TEXT |
JSON | VARCHAR | |
BINARY | VARCHAR |
Data load costslink
Panoply does not charge you extra when Fivetran loads data into your destination.
Column data type changeslink
To change the column's data type, Fivetran renames the existing column, creates a new column with the new data type, and then drops the previous version of the column.
Suppose you have set up a view on the table referencing the previous version of the column, then the DROP COLUMN
operation will fail, and your destination table will have a deprecated column that won't be updated.
We recommend that you create the views using the WITH NO SCHEMA BINDING
clause to prevent this issue.
NOTE: For more information on late binding views, see Amazon Redshift's documentation.