What's the process of modeling slowly changing dimensions (SCD) in a data warehouse using temporal tables in Snowflake?
In Snowflake, you can model slowly changing dimensions (SCD) using temporal tables. Temporal tables in Snowflake are designed to maintain historical versions of data, making them ideal for implementing SCDs. They simplify the process of tracking changes to dimension data over time, allowing you to analyze historical records easily. Here's the process of modeling slowly changing dimensions using temporal tables in Snowflake:
**Step 1: Create the Temporal Table:**
To create a temporal table, use the **`AS OF`** clause with the **`CREATE TABLE`** statement. This clause specifies the time travel setting for the table, enabling you to access historical data.
```sql
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CREATE TEMPORAL TABLE employee_dimension AS
SELECT
employee_id,
name,
address,
valid_from AS OF SYSTEM_TIME,
valid_to
FROM
employee_history;
```
**Step 2: Load Initial Data:**
Load the initial set of data into the temporal table. The data should include the valid_from and valid_to timestamps representing the period for which each record is valid.
**Step 3: Insert New Records:**
To handle SCD Type 2 changes (add rows with versioning), insert new records with updated data, setting the valid_from timestamp to the current timestamp and the valid_to timestamp to a default future date.
```sql
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INSERT INTO employee_dimension (employee_id, name, address, valid_from, valid_to)
VALUES (123, 'John Doe', 'New Address', CURRENT_TIMESTAMP(), '9999-12-31 00:00:00');
```
**Step 4: Update Existing Records:**
To handle SCD Type 2 changes, update the **`valid_to`** timestamp of the current active record to the current timestamp before inserting a new record.
```sql
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-- Mark the current record as no longer active (valid_to is set to current timestamp).
UPDATE employee_dimension
SET valid_to = CURRENT_TIMESTAMP()
WHERE employee_id = 123 AND valid_to = '9999-12-31 00:00:00';
-- Insert a new record with updated data.
INSERT INTO employee_dimension (employee_id, name, address, valid_from, valid_to)
VALUES (123, 'John Doe', 'Updated Address', CURRENT_TIMESTAMP(), '9999-12-31 00:00:00');
```
**Step 5: Retrieve Historical Data:**
To access historical versions of data, you can use the **`AS OF`** clause in your queries. This allows you to analyze the state of the dimension at a specific point in time.
```sql
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-- Retrieve the state of the dimension for employee_id = 123 at a specific time.
SELECT *
FROM employee_dimension AS OF TIMESTAMP_NTZ('2023-07-31 12:00:00')
WHERE employee_id = 123;
```
By modeling slowly changing dimensions using temporal tables in Snowflake, you can easily maintain historical data versions and efficiently track changes to dimension data over time. This approach simplifies the handling of SCDs and provides a straightforward way to access historical records for data analysis and reporting purposes.
