Abstract
Suppose you are a business analyst and you need to find the customer purchase records of a certain market in the last quarter from 1.3 million tables and billions of records to make a predictive analysis of the consumption trend in this region for the next quarter. How are you going to do it? This task is like looking for a needle in a haystack. What is even more frustrating is that when you finally find the relevant data after spending weeks on source data exploration, it is out of date, and new essential data is available. This example illustrates that it is not sufficient for companies to make data accessible; they also need to make it discoverable, understandable, and consumable in near real time to gain timely and relevant insights from the data.
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Notes
- 1.
See Reference [1] for more details on the metadata definition.
- 2.
Please review Chapter 5, where we have introduced metadata in the context of generating active metadata.
- 3.
See Reference [2] for more details on metadata management.
- 4.
See Reference [3] for more information on intelligent data catalog tools.
- 5.
Please, see Chapter 6 for more details on NLP.
- 6.
See Reference [4] for more use cases about semantic search.
- 7.
See Reference [5] for more details on data lineage and provenance.
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© 2023 The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature
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Hechler, E., Weihrauch, M., Wu, Y.(. (2023). Intelligent Cataloging and Metadata Management. In: Data Fabric and Data Mesh Approaches with AI. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-9253-2_13
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DOI: https://doi.org/10.1007/978-1-4842-9253-2_13
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