How to Import Only the New Data and New Records that Have Changed Using the ETL Process in a Data Warehouse?

-- Example data transformation in Python using Pandas
import pandas as pd

# Load data from source system
df = pd.read_csv('data.csv')

# Convert data types
df['Date'] = pd.to_datetime(df['Date'])

# Handle null values
df.fillna(df.mean(), inplace=True)

# Aggregate data
df = df.groupby(['Category', 'Region']).agg({'Sales': 'sum'})

Step 3: Load Data into the Data Warehouse

The final step in the ETL process is to load the transformed data into the data warehouse. Use the following strategies to import only the new data and new records that have changed:

Strategy 1: Insert-Only Approach

In this approach, you insert only new records into the data warehouse, without updating existing ones.

-- Example insert-only query in SQL Server
INSERT INTO dw_Customers (CustomerID, Name, Email)
SELECT src.CustomerID, src.Name, src.Email
FROM src_Customers src
LEFT JOIN dw_Customers dw ON src.CustomerID = dw.CustomerID
WHERE dw.CustomerID IS NULL

Strategy 2: Update-Else-Insert Approach

In this approach, you update existing records in the data warehouse, and insert new records that don’t exist.

-- Example update-else-insert query in SQL Server
MERGE dw_Customers dw
USING src_Customers src
ON dw.CustomerID = src.CustomerID
WHEN MATCHED THEN
    UPDATE SET dw.Name = src.Name, dw.Email = src.Email
WHEN NOT MATCHED THEN
    INSERT (CustomerID, Name, Email)
    VALUES (src.CustomerID, src.Name, src.Email)

Strategy 3: Change Data Capture (CDC) Approach

In this approach, you use CDC to track changes and updates in the source system, and apply those changes to the data warehouse.

-- Example CDC-based query in SQL Server
MERGE dw_Customers dw
USING (
    SELECT *
    FROM sys.fn_cdc_get_all_changes_dossier(
        'dbo_Customers',
        0,
        0,
        'all'
    )
    WHERE __$operation = 'I' OR __$operation = 'U'
) src
ON dw.CustomerID = src.CustomerID
WHEN MATCHED THEN
    UPDATE SET dw.Name = src.Name, dw.Email = src.Email
WHEN NOT MATCHED THEN
    INSERT (CustomerID, Name, Email)
    VALUES (src.CustomerID, src.Name, src.Email)

Conclusion

And there you have it! By following these steps and strategies, you can import only the new data and new records that have changed using the ETL process in a data warehouse. Remember to choose the approach that best fits your business requirements and data volume.

Happy ETL-ing!

If you have any questions or need further clarification on any of the steps, feel free to ask in the comments section below!

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