Foreword
The classic data warehouse and business intelligence architecture relies on a repository of quality, integrated data at its core. In the very early days of business intelligence, we struggled with manual processes to extract data from multiple operational systems, combine the data, fix any errors, fill in missing fields, remove duplicate data, and finally load the integrated data into a database, creating a physical data warehouse, or “single source of data,” for reporting and analytics.
Shortly thereafter came the technological innovation of extraction, transformation, and load (ETL) tools, which automated many manual data integration tasks in a reliable and repeatable fashion. ETL tools greatly improved the overall process of creating ...
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