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E-grāmata: Enterprise Data Catalog: Improve Data Discovery, Ensure Data Governance, and Enable Innovation

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  • Formāts: 218 pages
  • Izdošanas datums: 15-Feb-2023
  • Izdevniecība: O'Reilly Media
  • Valoda: eng
  • ISBN-13: 9781492098676
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Enterprise Data Catalog: Improve Data Discovery, Ensure Data Governance, and Enable InnovationPalielināt
  • Formāts: 218 pages
  • Izdošanas datums: 15-Feb-2023
  • Izdevniecība: O'Reilly Media
  • Valoda: eng
  • ISBN-13: 9781492098676
Citas grāmatas par šo tēmu:

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Combing the web is simple, but how do you search for data at work? It's difficult and time-consuming, and can sometimes seem impossible. This book introduces a practical solution: the data catalog. Data analysts, data scientists, and data engineers will learn how to create true data discovery in their organizations, making the catalog a key enabler for data-driven innovation and data governance.

Author Ole Olesen-Bagneux explains the benefits of implementing a data catalog. You'll learn how to organize data for your catalog, search for what you need, and manage data within the catalog. Written from a data management perspective and from a library and information science perspective, this book helps you:

  • Learn what a data catalog is and how it can help your organization
  • Organize data and its sources into domains and describe them with metadata
  • Search data using very simple-to-complex search techniques and learn to browse in domains, data lineage, and graphs
  • Manage the data in your company via a data catalog
  • Implement a data catalog in a way that exactly matches the strategic priorities of your organization
  • Understand what the future has in store for data catalogs
Foreword xi
Preface xv
Part I Organizing Data So You Can Search for It
1 Introduction to Data Catalogs
3(20)
The Core Functionality of a Data Catalog
3(1)
Create an Overview of the IT Landscape
4(2)
Organize Data
6(3)
Enable Search of Company Data
9(4)
Data Discovery
13(2)
The Data Discovery Team
15(1)
Data Architects
15(3)
Data Engineers
18(1)
Data Discovery Team Setup
18(1)
End-User Roles and Responsibilities
19(2)
Summary
21(2)
2 Organize Data: Design a Robust Architecture for Search
23(28)
Organizing Domains in the Data Catalog
23(1)
Domain Architecture in a Data Catalog
24(1)
Understanding Domains
25(4)
Processes and Capabilities
29(4)
Data Sources
33(2)
Getting Assets into the Data Catalog
35(1)
Pull
36(1)
Push
37(1)
Organizing Assets in the Domains
38(1)
Asset Metadata
38(7)
Metadata Quality
45(2)
Classification
47(3)
Summary
50(1)
3 Understand Search: Concepts, Features, and Mechanics
51(28)
Why Do You Search in a Data Catalog?
51(2)
Search Features in a Data Catalog
53(1)
Searching in Data Versus Searching for Data
54(4)
How Do You Search a Data Catalog?
58(1)
Data Catalog Query Language
58(1)
The Search Features in a Data Catalog Explained
59(10)
Searching for Everything?
69(1)
The Mechanics of Search
70(1)
Recall and Precision
70(3)
Zipt's Law
73(2)
Serendipity
75(1)
Summary
76(3)
4 Apply Search: From Simple to Advanced Patterns
79(22)
Search Like Librarians--Not Like Data Scientists
79(2)
Search Patterns
81(1)
Basic Simple Search
82(2)
Detailed Simple Search
84(1)
Flexible Simple Search
85(1)
Range Search
86(1)
Block Search
87(3)
Statement Search
90(1)
Browsing Patterns
91(1)
Glossary Browsing
91(1)
Domain Browsing
92(1)
Lineage Browsing
93(1)
Graph Browsing
93(3)
Searching a Graph-Based Data Catalog
96(1)
Summary
96(5)
Part II Democratizing Data with a Data Catalog
5 Discover Data: Empower End Users and Engage Stakeholders
101(16)
A Data Catalog Is a Social Network
101(2)
Active Metadata
103(2)
Ensure Stakeholder Engagement
105(1)
Engage Data Governance Leaders
105(4)
Engage Data Analytics Leaders
109(2)
Engage Domain Leaders
111(2)
Seeing All Data Through One Lens
113(1)
The Operational Backbone and the Data Platform
113(2)
Summary
115(2)
6 Access Data: The Keys to Successful Implementation
117(18)
Choosing a Data Catalog
117(1)
Vendor Analysis
117(1)
Some Key Vendors
118(3)
Catalog of Catalogs
121(1)
How to Access Data
122(1)
Data Providers and Data Consumers
122(2)
Centralized Approach
124(2)
Decentralized Approach
126(3)
Combined Approach
129(1)
Building Domains
130(1)
Questionnaire No. 1 Domain Owner Description of Domain and Assets
131(1)
Questionnaire No. 2 Asset Steward Description of Assets in the Domain
131(1)
Questionnaire No. 3 Asset Steward Description of the Glossary Terms of
Their Assets
132(1)
Summary
132(3)
7 Manage Data: Improve Lifecycle Management
135(26)
The Value of Data Lifecycle Management and Why the Data Catalog Is a Game Changer
135(2)
Various Lifecycles
137(1)
Data Lifecycle
138(1)
Using the Data Catalog for Data Lifecycle Management
139(1)
The Data Asset Lifecycle in the Data Catalog
140(5)
Glossary Term Lifecycle
145(2)
Data Source Lifecycle
147(2)
Lifecycle Influence and Support
149(1)
Applied Search Based on Lifecycles
150(1)
Applied Search for Regulatory Compliance
151(2)
Maintenance Best Practices
153(1)
Maintenance of the Data Outside the Data Catalog
153(1)
Maintenance of Metadata Inside the Data Catalog
154(2)
Improved Data Lifecycle Management
156(1)
Summary
157(4)
Part III Envisioning the Future of Data Catalogs
8 Looking Ahead: The Company Search Engine and Improved Data Management
161(18)
The Company Search Engine
161(1)
The Company Search Engine in Hugin & Munin
162(6)
From Data to Knowledge
168(3)
A Medium Theoretical Take on the Company Search Engine
171(3)
Is the Company Search Engine New?
174(1)
Will the Company Search Engine Become Reality?
175(2)
Summary
177(2)
Afterword 179(4)
Appendix. Data Catalog Query Language 183(4)
Index 187
Ole Olesen-Bagneux holds a PhD in Information Science from the University of Copenhagen, Denmark, where he has also lectured in courses pivotal for data cataloging, such as Knowledge Organization and Information Retrieval that teaches you how to organize data in big collections and retrieve it again. He has worked within the field of Data Management and Governance as a leader, architect, and practitioner for over a decade from the life science sector. He has hands-on experience with several data catalogs, and currently works as an Enterprise Architect in GN Store Nord, in Copenhagen, Denmark.
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