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E-raamat: Data Mesh: Delivering Data-Driven Value at Scale

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  • Formaat: 386 pages
  • Ilmumisaeg: 08-Mar-2022
  • Kirjastus: O'Reilly Media
  • Keel: eng
  • ISBN-13: 9781492092346
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Data Mesh: Delivering Data-Driven Value at ScaleSuurem pilt
  • Formaat: 386 pages
  • Ilmumisaeg: 08-Mar-2022
  • Kirjastus: O'Reilly Media
  • Keel: eng
  • ISBN-13: 9781492092346
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Guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. Original.

We're at an inflection point in data, where our data management solutions no longer match the complexity of organizations, the proliferation of data sources, and the scope of our aspirations to get value from data with AI and analytics. In this practical book, author Zhamak Dehghani introduces data mesh, a decentralized sociotechnical paradigm drawn from modern distributed architecture that provides a new approach to sourcing, sharing, accessing, and managing analytical data at scale.

Dehghani guides practitioners, architects, technical leaders, and decision makers on their journey from traditional big data architecture to a distributed and multidimensional approach to analytical data management. Data mesh treats data as a product, considers domains as a primary concern, applies platform thinking to create self-serve data infrastructure, and introduces a federated computational model of data governance.

  • Get a complete introduction to data mesh principles and its constituents
  • Design a data mesh architecture
  • Guide a data mesh strategy and execution
  • Navigate organizational design to a decentralized data ownership model
  • Move beyond traditional data warehouses and lakes to a distributed data mesh

Foreword xv
Preface xvii
Prologue: Imagine Data Mesh xxv
Part I What Is Data Mesh?
1 Data Mesh in a Nutshell
3(12)
The Outcomes
4(1)
The Shifts
4(2)
The Principles
6(3)
Principle of Domain Ownership
6(1)
Principle of Data as a Product
7(1)
Principle of the Self-Serve Data Platform
8(1)
Principle of Federated Computational Governance
8(1)
Interplay of the Principles
9(1)
Data Mesh Model at a Glance
10(1)
The Data
11(2)
Operational Data
11(1)
Analytical Data
12(1)
The Origin
13(2)
2 Principle of Domain Ownership
15(14)
A Brief Background on Domain-Driven Design
17(1)
Applying DDD's Strategic Design to Data
18(2)
Domain Data Archetypes
20(4)
Source-Aligned Domain Data
21(2)
Aggregate Domain Data
23(1)
Consumer-Aligned Domain Data
24(1)
Transition to Domain Ownership
24(3)
Push Data Ownership Upstream
24(1)
Define Multiple Connected Models
25(1)
Embrace the Most Relevant Domain Data: Don't Expect a Single Source of Truth
26(1)
Hide the Data Pipelines as Domains' Internal Implementation
26(1)
Recap
27(2)
3 Principle of Data as a Product
29(18)
Applying Product Thinking to Data
31(10)
Baseline Usability Attributes of a Data Product
33(8)
Transition to Data as a Product
41(4)
Include Data Product Ownership in Domains
42(1)
Reframe the Nomenclature to Create Change
42(1)
Think of Data as a Product, Not a Mere Asset
43(1)
Establish a Trust-But-Verify Data Culture
43(1)
Join Data and Compute as One Logical Unit
44(1)
Recap
45(2)
4 Principle of the Self-Serve Data Platform
47(20)
Data Mesh Platform: Compare and Contrast
49(5)
Serving Autonomous Domain-Oriented Teams
51(1)
Managing Autonomous and Interoperable Data Products
51(1)
A Continuous Platform of Operational and Analytical Capabilities
52(1)
Designed for a Generalist Majority
52(1)
Favoring Decentralized Technologies
53(1)
Domain Agnostic
54(1)
Data Mesh Platform Thinking
54(8)
Enable Autonomous Teams to Get Value from Data
57(1)
Exchange Value with Autonomous and Interoperable Data Products
58(1)
Accelerate Exchange of Value by Lowering the Cognitive Load
59(1)
Scale Out Data Sharing
60(2)
Support a Culture of Embedded Innovation
62(1)
Transition to a Self-Serve Data Mesh Platform
62(3)
Design the APIs and Protocols First
62(1)
Prepare for Generalist Adoption
63(1)
Do an Inventory and Simplify
63(1)
Create Higher-Level APIs to Manage Data Products
64(1)
Build Experiences, Not Mechanisms
64(1)
Begin with the Simplest Foundation, Then Harvest to Evolve
65(1)
Recap
65(2)
5 Principle of Federated Computational Governance
67(28)
Apply Systems Thinking to Data Mesh Governance
69(6)
Maintain Dynamic Equilibrium Between Domain Autonomy and Global Interoperability
71(3)
Embrace Dynamic Topology as a Default State
74(1)
Utilize Automation and the Distributed Architecture
75(1)
Apply Federation to the Governance Model
75(8)
Federated Team
77(1)
Guiding Values
78(3)
Policies
81(1)
Incentives
82(1)
Apply Computation to the Governance Model
83(3)
Standards as Code
84(1)
Policies as Code
85(1)
Automated Tests
86(1)
Automated Monitoring
86(1)
Transition to Federated Computational Governance
86(3)
Delegate Accountability to Domains
86(1)
Embed Policy Execution in Each Data Product
87(1)
Automate Enablement and Monitoring over Interventions
87(1)
Model the Gaps
88(1)
Measure the Network Effect
88(1)
Embrace Change over Constancy
88(1)
Recap
89(6)
Part II Why Data Mesh?
6 The Inflection Point
95(10)
Great Expectations of Data
96(2)
The Great Divide of Data
98(2)
Scale: Encounter of a New Kind
100(1)
Beyond Order
101(1)
Approaching the Plateau of Return
102(1)
Recap
102(3)
7 After the Inflection Point
105(16)
Respond Gracefully to Change in a Complex Business
106(5)
Align Business, Tech, and Now Analytical Data
107(1)
Close the Gap Between Analytical and Operational Data
108(2)
Localize Data Changes to Business Domains
110(1)
Reduce Accidental Complexity of Pipelines and Copying Data
111(1)
Sustain Agility in the Face of Growth
111(4)
Remove Centralized and Monolithic Bottlenecks
112(1)
Reduce Coordination of Data Pipelines
112(1)
Reduce Coordination of Data Governance
113(2)
Enable Autonomy
115(1)
Increase the Ratio of Value from Data to Investment
115(2)
Abstract Technical Complexity with a Data Platform
116(1)
Embed Product Thinking Everywhere
116(1)
Go Beyond the Boundaries
116(1)
Recap
117(4)
8 Before the Inflection Point
121(22)
Evolution of Analytical Data Architectures
121(5)
First Generation: Data Warehouse Architecture
122(1)
Second Generation: Data Lake Architecture
123(3)
Third Generation: Multimodal Cloud Architecture
126(1)
Characteristics of Analytical Data Architecture
126(11)
Monolithic
128(4)
Centralized Data Ownership
132(1)
Technology Oriented
133(4)
Recap
137(6)
Part III How to Design the Data Mesh Architecture
9 The Logical Architecture
143(28)
Domain-Oriented Analytical Data Sharing Interfaces
147(4)
Operational Interface Design
148(1)
Analytical Data Interface Design
149(1)
Interdomain Analytical Data Dependencies
149(2)
Data Product as an Architecture Quantum
151(9)
A Data Product's Structural Components
152(6)
Data Product Data Sharing Interactions
158(1)
Data Discovery and Observability APIs
159(1)
The Multiplane Data Platform
160(4)
A Platform Plane
161(1)
Data Infrastructure (Utility) Plane
162(1)
Data Product Experience Plane
162(1)
Mesh Experience Plane
162(1)
Example
163(1)
Embedded Computational Policies
164(4)
Data Product Sidecar
165(1)
Data Product Computational Container
166(1)
Control Port
167(1)
Recap
168(3)
10 The Multiplane Data Platform Architecture
171(22)
Design a Platform Driven by User Journeys
174(1)
Data Product Developer Journey
175(10)
Incept, Explore, Bootstrap, and Source
177(3)
Build, Test, Deploy, and Run
180(3)
Maintain, Evolve, and Retire
183(2)
Data Product Consumer Journey
185(4)
Incept, Explore, Bootstrap, Source
188(1)
Build, Test, Deploy, Run
188(1)
Maintain, Evolve, and Retire
189(1)
Recap
189(4)
Part IV How to Design the Data Product Architecture
11 Design a Data Product by Affordances
193(8)
Data Product Affordances
194(3)
Data Product Architecture Characteristics
197(1)
Design Influenced by the Simplicity of Complex Adaptive Systems
198(2)
Emergent Behavior from Simple Local Rules
198(1)
No Central Orchestrator
199(1)
Recap
200(1)
12 Design Consuming, Transforming, and Serving Data
201(32)
Serve Data
201(16)
The Needs of Data Users
201(3)
Serve Data Design Properties
204(12)
Serve Data Design
216(1)
Consume Data
217(9)
Archetypes of Data Sources
219(4)
Locality of Data Consumption
223(1)
Data Consumption Design
224(2)
Transform Data
226(5)
Programmatic Versus Nonprogrammatic Transformation
226(2)
Dataflow-Based Transformation
228(1)
ML as Transformation
229(1)
Time-Variant Transformation
229(1)
Transformation Design
230(1)
Recap
231(2)
13 Design Discovering, Understanding, and Composing Data
233(22)
Discover, Understand, Trust, and Explore
233(11)
Begin Discovery with Self-Registration
236(1)
Discover the Global URI
236(1)
Understand Semantic and Syntax Models
237(1)
Establish Trust with Data Guarantees
238(3)
Explore the Shape of Data
241(1)
Learn with Documentation
242(1)
Discover, Explore, and Understand Design
242(2)
Compose Data
244(8)
Consume Data Design Properties
245(1)
Traditional Approaches to Data Composability
246(4)
Compose Data Design
250(2)
Recap
252(3)
14 Design Managing, Governing, and Observing Data
255(16)
Manage the Life Cycle
255(3)
Manage Life-Cycle Design
256(1)
Data Product Manifest Components
257(1)
Govern Data
258(4)
Govern Data Design
259(1)
Standardize Policies
260(2)
Data and Policy Integration
262(1)
Linking Policies
262(1)
Observe, Debug, and Audit
262(5)
Observability Design
264(3)
Recap
267(4)
Part V How to Get Started
15 Strategy and Execution
271(32)
Should You Adopt Data Mesh Today?
271(4)
Data Mesh as an Element of Data Strategy
275(4)
Data Mesh Execution Framework
279(23)
Business-Driven Execution
280(5)
End-to-End and Iterative Execution
285(1)
Evolutionary Execution
286(16)
Recap
302(1)
16 Organization and Culture
303(30)
Change
305(2)
Culture
307(3)
Values
308(2)
Reward
310(2)
Intrinsic Motivations
311(1)
Extrinsic Motivations
311(1)
Structure
312(12)
Organization Structure Assumptions
313(8)
Discover Data Product Boundaries
321(3)
People
324(5)
Roles
324(3)
Skillset Development
327(2)
Process
329(2)
Key Process Changes
330(1)
Recap
331(2)
Index 333
Zhamak Dehghani is a director of technology at ThoughtWorks, focusing on distributed systems architecture --- big data and operational systems -- in the enterprise. She's a member of the company's Technology Advisory Board and contributes to the creation of ThoughtWorks's Technology Radar. She is an advocate for decentralization of all things - architecture, data and ultimately power. She is the founder of data mesh.
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