Snowflake SCD2
In this article
Overview
This Snap provides the functionality of SCD (Slowly Changing Dimension) Type 2 on a target Snowflake table. You can use this Snap to execute one SQL lookup request per set of input documents to avoid making a request for every input record. Its output is typically a stream of documents for the Snowflake - Bulk Upsert Snap, which updates or inserts rows into the target table. Therefore, this Snap must be connected to the Snowflake - Bulk Upsert Snap to accomplish the complete SCD2 functionality.
Snap Type
The Snowflake SCD2 is a Read-type Snap that enables you to execute multiple queries as a single atomic unit.
Prerequisites
Read and write access to the Snowflake instance.
The target table should have the following three columns for field historization to work:
Column to demarcate whether a row is a current row or not. For example, "CURRENT_ROW". For the current row, the value would be true or 1. For the historical row, the value would be false or 0.
Column to denote the starting date of the current row. For example, "START_DATE".
Column to denote when the row was historized. For example, "END_DATE". For the active row, it is null. For a historical row, it has the value that indicates it was effective till that date.
Security Prerequisites
You should have the following permissions in your Snowflake account to execute this Snap:
Usage (DB and Schema): Privilege to use database, role and schema.
Create table: Privilege to create a table on the database. role and schema.
For more information on Snowflake privileges, refer to Access Control Privileges.
Internal SQL Commands
This Snap uses the SELECT command internally. It enables querying the database to retrieve a set of rows.
Support for Ultra Pipelines
Works in Ultra Pipelines. However, we recommend that you not use this Snap in an Ultra Pipeline.
Known Issues
Because of performance issues, all Snowflake Snaps now ignore the Cancel queued queries when pipeline is stopped or if it fails option for Manage Queued Queries, even when selected. Snaps behave as though the default Continue to execute queued queries when the Pipeline is stopped or if it fails option were selected.
We plan to address this issue in a patch for the next monthly release in December.
Snap Views
Type | Format | Number of Views | Examples of Upstream and Downstream Snaps | Description |
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Input | Document |
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| A document in the input view should contain a data map of key-value entries. The input data must contain data in the Natural Key (primary key) and Cause-historization fields. |
Output | Document |
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| A document in the output view contains a data map of key-value entries for all fields of a row in the target Snowflake table. |
Error | Error handling is a generic way to handle errors without losing data or failing the Snap execution. You can handle the errors that the Snap might encounter while running the Pipeline by choosing one of the following options from the When errors occur list under the Views tab. The available options are:
Learn more about Error handling in Pipelines. |
Snap Settings
Asterisk (*): Indicates a mandatory field.
Suggestion icon (): Indicates a list that is dynamically populated based on the configuration.
Expression icon (): Indicates whether the value is an expression (if enabled) or a static value (if disabled). Learn more about Using Expressions in SnapLogic.
Add icon (): Indicates that you can add fields in the field set.
Remove icon (): Indicates that you can remove fields from the field set.
Field Name | Field Type | Description | ||
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Label*
Default Value: Snowflake - SCD2 | String | Specify the name for the Snap. You can modify this to be more specific, especially if you have more than one of the same Snap in your pipeline.
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Schema name
Default Value: N/A | String/Expression | Specify the database schema name. In case it is not defined, then the suggestion for the Table Name will retrieve all tables names of all schemas. The property is suggestible and will retrieve available database schemas during suggest values.
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Table Name
Default Value: N/A | String/Expression | Specify the name of the table in the instance. The table name is suggestible and requires an account setting. The target table should have the following three columns for field historization to work:
Use the ALTER table command to add these columns to your target table if they are not present. | ||
Natural key*
Default Value: N/A | String/Expression | Specify the names of fields that identify a unique row in the target table. The identity key cannot be used as the Natural key, since a current row and its historical rows cannot have the same natural key value | ||
Cause-historization fields
Default Value: N/A | String/Expression | Specify the names of fields where any change in value causes the historization of an existing row and the insertion of a new current row.
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SCD fields | The historical and updated information for the Cause-historization field. Click + to add SCD fields. By default, there are four rows in this field-set:
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Meaning*
Default Value:
| Dropdown list | Specifies the table columns that are to be updated for implementing the SCD2 type transformation.
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Field*
Default Value: N/A | String/Expression | Specify the fields in the table will contain the historical information. Below are the values that must be configured for each row:
By default, the start and end date for both Current row and Historical row are null. After the Snap is executed, the start date for the updated row data automatically becomes the end date for the earlier version of the data (Historical row). | ||
Value*
Default Value:
| String/Expression | Specify the value to be assigned to the current or historical row. For date-related rows, the default is The Value field should be configured as follows:
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Ignore unchanged rows
Default value: Deselected | Checkbox | Specifies whether the Snap must ignore writing unchanged rows from the source table to the target table. If you enable this option, the Snap generates a corresponding document in the target only if the Cause-historization column in the source row is changed. Else, the Snap does not generate any corresponding document in the target. | ||
Number of Retries
Default Value: 0 | Integer/Expression | The number of times that the Snap must try to write the fields in case of an error during processing. An error is displayed if the maximum number of tries has been reached. | ||
Retry Interval (Seconds)
Default Value: 1 | Integer/Expression | The time interval, in seconds, between subsequent retry attempts.
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Auto Historization Query | This field-set is used to specify the fields that are to be used to historize table data. Historization is in the sort order specified. Care must be taken that the field is sortable. You can also add multiple fields here; historizaton occurs when even of the fields is changed. | |||
Field*
Default Value: N/A | String/Expression | Specify the name of the field. This is a suggestible field and suggests all the fields in the target table. | ||
Sort Order*
Default Value: Ascending Order | Dropdown list | The order in which the selected field is to be historized. Available options are:
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Input Date Format
Default Value: Continue to execute the snap with given input Date format | Dropdown list | The property has the following two options:
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Manage Queued Queries
Default Value: Continue to execute queued queries when the Pipeline is stopped or if it fails | Dropdown list | Select this property to decide whether the Snap should continue or cancel the execution of the queued Snowflake Execute SQL queries when you stop the pipeline. | ||
Snap Execution
Default Value: Validate & Execute | Dropdown list | Select one of the following three modes in which the Snap executes:
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Examples
Historizing Incoming Records
This example demonstrates how you can use the Snowflake SCD2 Snap to auto-historize records. In this example, since the existing record in the Snowflake table is the latest, the incoming records are historized.
This Pipeline performs the following operations:
Input Data: Reading, Parsing and Mapping
This Pipeline is configured to send records into the target table. The File Reader Snap is configured to read a CSV file that contains the records. The downstream CSV Parser Snap parses the CSV file read by the File Reader Snap. Below is a preview of this file:
Based on the values of HistoryStDate and HistoryEndDate, it is clear that the existing record in the target table is the latest (or current) record.
Since the output from the CSV Parser Snap is a string, it has to be parsed into the appropriate data type. Parsing and data mapping is done using the Mapper Snap, as shown below:
This mapped data is then sent to the Snowflake SCD2 Snap.
Data Processing
The Snowflake SCD2 Snap performs SCD2 operations on the target table. We configure it as shown below:
Let us take a look at the highlighted Snap fields and how they affect the Snap functionality in this example:
Natural key: The Snap looks for records with matching POINT ID values in the incoming documents to group the records.
Cause-historization fields: For each unique POINT ID, changes in SCHEDULEDVOLUME initiate historization. If a change has not occurred, the incoming records are historized..
SCD fields:
The state of the current or historical record is marked in the FLAG field, T for current record and F for the historical record.
The columns STARTDATE and ENDDATE in the target table are maintained to denote the start and end dates of the current state of the table's data. The ENDDATE is always blank for a current record.
Auto Historization Query: The Snap sorts the values in the HISTORYSTARTDATE and HISTORYENDDATE columns for the same POINT ID in the Snowflake table and the incoming documents in ascending order. The record with the highest value in those fields is considered the current record.
All incoming records pertaining to a POINT ID are historized. The value F is assigned under the FLAG column to these fields and the corresponding STARTDATE and ENDDATE are evaluated by the expression Date.now()
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This can be seen in the SCD2 Snap's output preview:
The Snap identifies the current and historical records and this data is now ready to be updated and inserted into the target table.
Upsert Data into the Target Table
We use the Snowflake Bulk Upsert Snap to update the target table with this historized data. We configure the Snowflake Bulk Upsert Snap as shown below: