Abstract
The vigilant reader of this book has certainly noticed the distinguished focus that we have put on applying AI with automation and intelligent augmentation and optimization to nearly all aspects of a Data Fabric architecture and Data Mesh solution. There are indeed numerous areas of both concepts, which are increasingly optimized with AI-infused automation, such as automated workload performance prediction and runtime adjustment, automated capacity planning and resource demand estimation (e.g., CPU capacity, network bandwidth, memory sizes, etc.), automated query generation, intelligent information integration, automated data curation, and automated creation of data products.
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Notes
- 1.
See Reference [1] for an example from IBM about performance and capacity planning.
- 2.
See Reference [2] for more information on capacity planning for enterprise Data Fabrics.
- 3.
All samples in this chapter are derived from the IBM Watson Knowledge Catalog.
- 4.
See Reference [3] for a comprehensive list of metadata standards and Reference [4] for Egeria, an open metadata standard from the Linux Foundation.
- 5.
See Reference [5] for mor information on automated metadata management.
- 6.
See Reference [6] for more information on profiling data assets.
- 7.
See Reference [7] for more information on applying ML to profiling.
- 8.
See Reference [8] for a comparison of data profiling and data mining.
- 9.
- 10.
See Reference [9] for more information on labeling and Reference [10] for more information on tagging for autonomous driving.
- 11.
See Reference [11] for more information on data quality assessments and data quality dimensions.
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Hechler, E., Weihrauch, M., Wu, Y.(. (2023). Automated Data Fabric and Data Mesh Aspects. In: Data Fabric and Data Mesh Approaches with AI. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-9253-2_14
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DOI: https://doi.org/10.1007/978-1-4842-9253-2_14
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