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ETL Channel


Extract, transform and load (ETL) is the core process of data integration and is typically associated with data warehousing. ETL tools extract data from a chosen source(s), transform it into new formats according to business rules, and then load it into target data structure(s). Managing rules and processes for the increasing diversity of data sources and high volumes of data processed that ETL must accommodate, make management, performance and cost the primary and challenges for users. The traditional ETL approach requires users to map each physical data item with a unique metadata description; newer ETL tools allow the user to create an abstraction layer of common business definitions and map all similar data items to the same definition before applying target-specific business rules, isolating business rules from data and allowing easier ETL management.

Articles

How to Calculate Data Warehouse Reliability

Designing extract, transform and load architecture has not received the attention and research it deserves

SQL Skills Essential for ETL Developers

The strength of the SQL, if used intelligently, can increase the performance and reduce complexity of ETL mappings

Advancing the Art of Data Integration

Why are so many DI users unhappy, and why is the DI industry in what Gartner calls the “trough of disillusionment” that technologies fall into when they fail to meet expectations?

Beyond ETL and Data Warehousing

Data integration suffers from an image problem - it has become synonymous with extract, transform and load

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Setting the Standard for ETL Unit Testing

Test the success and failure of every business rule and piece of logic

Add Real-World Value to the Staging Area

Architecture and design can add lots of value when staging data ETL jobs

The What-If Review

The technical design process should include worst-case scenario planning

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