These values will be converted into NULLs.Ĭolumn names will be converted to lowercase.ĭata arrives with column names containing spaces Panoply will reject the entire record containing the two columns. Panoply will reject all data for the table and surface a “too many columns” error.ĭata arrives with two column names that canonicalize to the same name The maximum number of allowed columns is 1,600. Note that the limit of 115 characters is to leave room for suffixes that are a result of column splitting.Ī table arrives with more columns than Panoply allows. Column names are limited to 115 characters.Ĭolumns with names longer than 115 characters will be rejected columns with names less than 115 characters will persist to Panoply. Panoply will reject all data for the table.ĭata arrives with a column name that exceeds the maximum length for Panoply. Table names are limited to 127 characters. Table is created and contains only the Stitch replication ( _sdc) columns.ĭata arrives with a table name that exceeds the maximum length for Panoply. Only columns that are populated in at least one record are created.įirst batch of data arrives with columns that are all NULL Rows with NULL Replication Keys will not be replicated after the initial sync if there is at least one non- NULL value in the column.įirst batch of data arrives with some columns that are completely empty Table is created and rows with NULL Replication Keys are created. Table is created without Primary Key and no NOT-NULL columns.įirst batch of data arrives where the Replication Key column contains NULLs Primary Key info is stored as a comment on the table.įirst batch of data arrives with multiple Primary Keysįirst batch of data arrives without Primary Keys Table is created without Primary Key and no NOT-NULL columns. Summaryįirst batch of data arrives with a single Primary Key These scenarios apply when Stitch creates a new table in Panoply. Tip: Enter some keywords in the Search box above the tables to display specific rows of information. Direct data warehouse changes - These scenarios apply when you make a direct change to your Panoply data warehouse.Nested data scenarios - These scenarios apply when Stitch loads nested data into Panoply.Schema change scenarios - These scenarios apply when a table undergoes structural changes.Data typing scenarios - These scenarios apply when Stitch encounters specific data types (ex: timezones).Data loading scenarios - These scenarios apply when Stitch encounters specific data types (ex: timezones).New table scenarios - These scenarios apply when Stitch creates a new table in Panoply.To make browsing easier, we’ve grouped these scenarios into the following categories: This doc covers many of the common scenarios Stitch will encounter and how Panoply specifically handles them. Because data can come from a variety of integrations and all those integrations may structure or handle data differently, Stitch will encounter numerous scenarios when replicating and loading data into your Panoply data warehouse.

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