A question that is frequently asked is “when should I use data virtualization and when should I use ETL tools?” Other variants of this question is “does data virtualization replace ETL?” or “I’ve already got ETL, why do I need data virtualization?” This Denodo Technologies architecture brief will answer these questions.   
  
**Extract, Transform, and Load (ETL)** is a good solution for physical data consolidation projects which result in duplicating data from the original data sources into an enterprise data warehouse (EDW) or a new database.   
This includes:

- ETL tools that are designed to bulk copy very large data sets, comprising millions of rows, from large structured data sources.
- Creating historical records of data, e.g. snapshots at a particular time, to analyze how the data set changes over time.
- Performing complex, multi-pass data transformation and cleansing operations, and bulk loading the data into a target data store.

  
The reality is that, while the two solutions are different, data virtualization and ETL are often complementary technologies. Data virtualization can extend and enhance ETL/EDW deployments in many ways, for example:

- Extending existing data warehouses with new data sources.
- Federating multiple data warehouses.
- Acting as a virtual data source to augment an ETL process.
- Isolating applications from changes to the underlying data sources (e.g. migrating a data warehouse.



 

 

 

  

 ![Data Virtualization and ETL](/sites/default/files/asset-thumbnail-details-pages/mockup-dvetl.png) [Download](/system/files/document-attachments/wp-dv-etl-01.pdf) 

 

 

### Role

 ArchitectBusiness User 



### Solutions

- Enterprise Data Services



 

 

 



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