Acknowledgments |
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iii | |
Foreword |
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vii | |
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1 | (6) |
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Early Data Storage and Management |
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1 | (3) |
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From Centralized to Distributed |
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4 | (1) |
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Databases, OLTP, OLAP, Warehouses, Master Data Management, Marts, Lakes, Lakehouses, Hadoop |
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5 | (2) |
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2 The Impact of Hybrid Cloud |
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7 | (10) |
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7 | (3) |
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The Roles of Different "As a Service" Models |
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10 | (4) |
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Red Hat Open Shift Container Platform |
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14 | (3) |
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17 | (4) |
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The Best Performers Are Data-Driven |
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17 | (4) |
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4 Reducing Complexity with a Data Fabric |
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21 | (10) |
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Data Fabric vs. Data Mesh |
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24 | (1) |
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Delivering a Data Fabric as Part of a Hybrid Cloud Data and AI Platform |
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25 | (2) |
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Automation: The Key to Agility |
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27 | (2) |
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Data and AI: How and Where the Users Need It |
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29 | (2) |
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5 The Data Fabric Advantage |
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31 | (8) |
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Data Fabric vs. Data Virtualization |
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33 | (3) |
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Other Advantages of Data Fabric Architectures |
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36 | (3) |
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6 Data Fabric, DataOps, and the Data Catalog |
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39 | (8) |
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Data Catalog Attributes and Benefits |
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40 | (3) |
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Using a Data Catalog to Improve Business Taxonomy |
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43 | (4) |
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7 Common Use Cases for a Data Fabric |
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47 | (24) |
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Use Case 1 Driving Simpler Cloud Integration |
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47 | (5) |
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Use Case 2 Going 360 Degrees |
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52 | (3) |
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Use Case 3 True Enterprise Governance |
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55 | (5) |
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Use Case 4 Toward Trustworthy, Transparent AI |
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60 | (3) |
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Use Case 5 Data Observability |
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63 | (8) |
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8 Two Cornerstones of a Data Fabric |
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71 | (10) |
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Examples of the Value of a Data Catalog |
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71 | (3) |
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74 | (7) |
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9 How IBM Delivers Value Through the Data Fabric |
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81 | (24) |
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Use Case 1 Data Governance and Privacy |
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85 | (6) |
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91 | (3) |
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Use Case 3 MLOps and Trustworthy AI |
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94 | (7) |
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Use Case 4 Multicloud Data Integration |
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101 | (4) |
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105 | (4) |
Appendix A Different Types of Data Storage Paradigms |
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109 | (9) |
Notices and Disclaimers |
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118 | |