Keeping a data warehouse updated from an operational data source in an MS SQL environment typically involves employing Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) processes. The choice depends on your specific requirements, infrastructure, and the size of your data. Here’s an overview of both approaches, followed by some best practices:

1. ETL (Extract, Transform, Load):


  1. Extract: Data is extracted from the source operational system(s).
  2. Transform: This data is then transformed (cleaned, enriched, and made consistent) in a staging area.
  3. Load: The transformed data is loaded into the data warehouse.


  • SQL Server Integration Services (SSIS) is a popular ETL tool in the MS SQL ecosystem.

2. ELT (Extract, Load, Transform):


  1. Extract: Data is extracted from the source operational system(s).
  2. Load: This raw data is loaded into the data warehouse without transformation.
  3. Transform: Transformation operations are executed within the data warehouse itself.

Note: ELT is becoming more popular with cloud-based data warehouses like Snowflake, BigQuery, and Redshift due to their ability to handle large-scale transformations efficiently. SQL Server also supports this approach, especially if you’re using columnstore indexes that are optimized for analytics.

Best Practices:

  1. Change Data Capture (CDC):
    • If your operational system and SQL Server version supports it, consider using CDC. It tracks and captures changes in the source data, allowing you to update the data warehouse incrementally rather than reloading everything.
  2. Incremental Loads:
    • Instead of loading the entire dataset from the source system, only load the records that have been added or changed since the last ETL process. This can be achieved by timestamp columns, versioning, or other mechanisms.
  3. Staging Area:
    • Use a staging area to temporarily hold data before it gets loaded into the data warehouse. This provides a buffer, ensuring that the operations don’t interfere with the data warehouse’s performance during peak times.
  4. Batch Processing:
    • Instead of real-time updates, consider batch processing at off-peak times to reduce operational strain and contention.
  5. Monitoring & Logging:
    • Monitor the ETL/ELT processes and maintain logs to quickly identify and address any issues or failures.
  6. Error Handling:
    • Implement comprehensive error handling. Decide on strategies for dealing with missing data, data inconsistencies, or transformation errors. This might involve creating an error or exception table where issues are logged for further review.
  7. Performance Tuning:
    • Periodically review and optimize the ETL/ELT process, transformations, and indexes in the data warehouse to ensure efficient data loads.
  8. Data Validation:
    • After loading, validate that the data in the data warehouse is consistent with the source system. Consider using checksums or row counts for validation.
  9. Backup and Recovery:
    • Regularly back up the data warehouse and maintain a recovery strategy to handle any failures or data corruption issues.
  10. Documentation:
  • Maintain thorough documentation of the ETL/ELT processes, transformations, and any business logic applied. This will be crucial for maintenance, audits, and troubleshooting.

In conclusion, the choice between ETL and ELT and the specifics of implementing either strategy will largely depend on your infrastructure, data volume, and specific use case. Regardless of the chosen strategy, the aim is to ensure that data in the data warehouse is current, consistent, and accurate.

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