Using components: Snowflake Destination (beta)

Use the Snowflake destination component to store the output of a data flow in a Snowflake table.

The destination component stores the data intermediately into Amazon S3 and then uses Snowflake's COPY statement to push the data into the table.

Connection

Select an existing Snowflake connection or create a new one.

Destination Properties

  • Target schema - the target table's schema. If empty, the default schema is used.
  • Target table - the name of the target table in your Redshift cluster. By default, if the table doesn't exist, it will be created automatically.
  • Automatically create table if it doesn't exist - if unchecked and the table doesn't exist, the job fails.
  • Automatically add missing columns - when checked, the job will check if each of the specified columns exist in the table and if one does not exist, it will add it. Key columns can't be automatically added to a table.

Operation type

Append (Insert only) - default behaviour. Data will only be appended to the target table

Overwrite (Truncate and insert) - truncate the target table before data is inserted into the target table.

Overwrite (Delete and insert) - deletes all of the target table before the data flow executes. If a truncate statement can't be executed on the target table due to permissions or other constraints, you can use this instead.

Merge with existing data using delete and insert - incoming data is merged with existing data in the table by deleting target table data that exists in both the data sets and then inserting all the incoming data into the target table. Requires setting the merge keys correctly in field mapping. Merge is done in a single transaction:

  1. The dataflow's output is copied into a temporary table with the same schema as the target table.
  2. Rows with keys that exist in the temporary table are deleted from the target table.
  3. All rows in the temporary table are inserted into the target table.
  4. temporary table is dropped.

Merge with existing data using update and insert - incoming data is merged with existing data in the table by updating existing data and inserting new data. Requires setting the merge keys correctly in field mapping. Merge is done in the following manner:

  1. The dataflow's output is copied into a temporary table with the same schema as the target table.
  2. Existing records (by key) in the target table are updated and new records are inserted using the MERGE statement.
  3. temporary table is dropped.

Pre and post action SQL

Pre-action SQL - SQL code to execute before inserting the data into the target table. If a merge operation is selected, the sql code is executed before the staging table is created.

Post-action SQL - SQL code to execute after inserting the data into the target table. If a merge operation is selected, the sql code is executed after the staging table is merged into the target table.

Advanced options

  • Maximum errors - If this number of errors occurs in Redshift while loading data into the table, the job fails.
  • Truncate columns - Truncates string values in order for them to fit in the target column specification.

Schema Mapping

Map the dataflow fields to the target table's columns. Columns defined as key will be used as the sort key when Xplenty creates the table. If merge operation is used, you must select at least a field or multiple fields as keys, which will be used to uniquely identify rows in the table for the merge operation.

The data types in Xplenty are mapped as follows when the table is created automatically. Note that since Xplenty doesn't have a notion of maximum string length, the string columns are created with the maximum length allowed in Redshift.

Xplenty Snowflake
String VARCHAR
Integer NUMBER
Long NUMBER
Float DOUBLE
Double DOUBLE
DateTime TIMESTAMP_TZ
Boolean BOOLEAN

Creating packages

  1. Creating a new package in New Xplenty
  2. Creating a workflow
  3. Working in the new package designer
  4. Validating a package
  5. Using components: Amazon Redshift Source
  6. Using components: Bing Ads Source
  7. Using components: Database Source
  8. Using components: Facebook Ads Insights Source
  9. Using components: File Storage Source
  10. Using components: Google Adwords source
  11. Using components: Google Analytics Source
  12. Using components: Google BigQuery Source
  13. Using components: Google Cloud Spanner Source
  14. Using components: MongoDB Source
  15. Using components: NetSuite Source
  16. Using components: Salesforce source
  17. Using components: Rest API Source
  18. Using components: Aggregate Transformation
  19. Using components: Assert Transformation
  20. Using components: Clone transformation
  21. Using components: Cross Join Transformation
  22. Using components: Distinct Transformation
  23. Using components: Filter Transformation
  24. Using components: Join Transformation
  25. Using components: Limit Transformation
  26. Using components: Rank Transformation
  27. Using components: Select Transformation
  28. Using components: Sort Transformation
  29. Using components: Union Transformation
  30. Using components: Window Transformation
  31. Using components: Sample Transformation
  32. Using components: Cube transformation
  33. Using components: Amazon Redshift Destination
  34. Using components: Database Destination
  35. Using components: File Storage Destination
  36. Using components: Google BigQuery Destination
  37. Using components: Google Spanner Destination
  38. Using components: MongoDB Destination
  39. Using components: Salesforce Destination
  40. Using components: Snowflake Destination (beta)
  41. Using Components: Rest API Destination
  42. Using and setting variables in your packages
  43. System and pre-defined variables
  44. Using pattern-matching in source component paths
  45. Using ISO 8601 string functions
  46. Using Expressions in Xplenty
  47. Xplenty Functions

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