The explosive growth of data has forced organizations to use their enterprise data warehouse (EDW) for purposes that it was never intended for — including running extraction, transformation and loading (ETL) workloads and storing large volumes of unused data. New types of data, updated analytics practices and more efficient, cost-effective methods of storing and accessing data have put an additional strain on EDW infrastructures.
TECHNOLOGY FRAMEWORKS
Data Warehouse Offload
Can your current data architecture handle the massive influx of data that is coming into the enterprise every day? If not, it’s time to think about modernizing your data architecture to ensure you capture and manage one of the most valuable assets your organization has, its data.
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Offloading has to do the following:
- Data transfer between EDW and BDW must therefore be able to transfer data (unidirectional/bi-directional).
- The consistency of the data between EDW and BDW, i.e. the structures on EDW and BDW, must be identical (as allowed by the environment).
- Transfer of rights, i.e. users who have rights to EDW must also have rights to BDW (unidirectional/bi-directional).
- Scalability and economy, i.e. it must not cause instability in the EDW and BDW.
- Auditability of results, i.e. it must provide reporting and information on transfer.
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