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The magic of the data warehouse was figuring out how to get data out of these transactional systems and reorganize it in a structured way optimized for analysis and reporting. But those end users werent always clear on which data they should use for which reports, as the datadefinitions were often unclear or conflicting.
Prime examples of this in the data catalog include: Trust Flags — Allow the data community to endorse, warn, and deprecate data to signal whether data can or can’t be used. DataProfiling — Statistics such as min, max, mean, and null can be applied to certain columns to understand its shape.
They offer a range of features and integrations, so the choice depends on factors like the complexity of your data pipeline, requirements for connections to other services, user interface, and compatibility with any ETL software already in use. Include tasks to ensure data integrity, accuracy, and consistency.
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