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Design and implement a data lakehouse using technology-driven simplifications and generalizations. ¿The approach you will learn enables consolidating even incoherent data from multiple source systems across complex enterprise environments. The precise business question does not need to be known in advance and can even change over time. The approach lends itself well to federated, cooperating data mesh nodes. The individual components, called mini-marts, are like the "data part" of a data quantum and are interoperable. We describe data model blueprints to generalize dimensions with synonyms and facts at different granularities. Includes code examples using complex hierarchies as they exist in heterogenous real-world go-to-market organizations.
| Autorius: | Bruno Freitag |
| Leidėjas: | Technics Publications |
| Išleidimo metai: | 2023 |
| Knygos puslapių skaičius: | 170 |
| ISBN-10: | 1634622154 |
| ISBN-13: | 9781634622158 |
| Formatas: | 280 x 216 x 10 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Data Mesh Design: A Practical Pipeline Design Guide for Analytics, Data Science and Machine Learning“
