A data warehouse is a database that is dedicated to data analysis and reporting. It combines data from multiple operational applications and provides one location for decision-support data. A warehouse should include staging tables — one staging table for each source table or file.
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I’ve been practicing agile database techniques for about twenty years now. My use of agile techniques didn’t start as an explicit plan. Rather it evolved over time as I was working on consulting projects. It made sense to look for ways of working faster and better and with greater customer interaction.
I can think of at least three kinds of agile database techniques.
- For data modeling.
- For data warehouse development.
- For database reverse engineering.
We define agile analytics as the iterative and rapid processing of data for decision support. There is minimal upfront investment. Developers instead process source data and build queries incrementally as business needs emerge.
Big Data and data warehouses are both technologies for realizing agile analytics. They differ in several ways.