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E-raamat: Data Management at Scale

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  • Formaat: 412 pages
  • Ilmumisaeg: 10-Apr-2023
  • Kirjastus: O'Reilly Media
  • Keel: eng
  • ISBN-13: 9781098138837
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Data Management at ScaleSuurem pilt
  • Formaat: 412 pages
  • Ilmumisaeg: 10-Apr-2023
  • Kirjastus: O'Reilly Media
  • Keel: eng
  • ISBN-13: 9781098138837
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As data management continues to evolve rapidly, managing all of your data in a central place, such as a data warehouse, is no longer scalable. Today's world is about quickly turning data into value. This requires a paradigm shift in the way we federate responsibilities, manage data, and make it available to others. With this practical book, you'll learn how to design a next-gen data architecture that takes into account the scale you need for your organization.

Executives, architects and engineers, analytics teams, and compliance and governance staff will learn how to build a next-gen data landscape. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed.

  • Examine data management trends, including regulatory requirements, privacy concerns, and new developments such as data mesh and data fabric
  • Go deep into building a modern data architecture, including cloud data landing zones, domain-driven design, data product design, and more
  • Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata

Foreword xi
Preface xiii
1 The Journey to Becoming Data-Driven
1(24)
Recent Technology Developments and Industry Trends
2(2)
Data Management
4(4)
Analytics Is Fragmenting the Data Landscape
8(1)
The Speed of Software Delivery Is Changing
9(1)
The Cloud's Impact on Data Management Is Immeasurable
10(1)
Privacy and Security Concerns Are a Top Priority
11(1)
Operational and Analytical Systems Need to Be Integrated
12(1)
Organizations Operate in Collaborative Ecosystems
13(1)
Enterprises Are Saddled with Outdated Data Architectures
14(1)
The Enterprise Data Warehouse: A Single Source of Truth
14(3)
The Data Lake: A Centralized Repository for Structured and Unstructured Data
17(1)
The Pain of Centralization
18(1)
Defining a Data Strategy
19(3)
Wrapping Up
22(3)
2 Organizing Data Using Data Domains
25(36)
Application Design Starting Points
26(1)
Each Application Has a Data Store
26(1)
Applications Are Always Unique
26(1)
Golden Sources
26(1)
The Data Integration Dilemma
27(1)
Application Roles
27(2)
Inspirations from Software Architecture
29(3)
Data Domains
32(1)
Domain-Driven Design
32(3)
Business Architecture
35(10)
Domain Characteristics
45(5)
Principles for Distributed and Domain-Oriented Data Management
50(1)
Design Principles for Data Domains
51(2)
Best Practices for Data Providers
53(2)
Domain Ownership Responsibilities
55(1)
Transitioning Toward Distributed and Domain-Oriented Data Management
56(1)
Wrapping Up
57(4)
3 Mapping Domains to a Technology Architecture
61(38)
Domain Topologies: Managing Problem Spaces
62(1)
Fully Federated Domain Topology
62(4)
Governed Domain Topology
66(3)
Partially Federated Domain Topology
69(1)
Value Chain-Aligned Domain Topology
70(1)
Coarse-Grained Domain Topology
71(2)
Coarse-Grained and Partially Governed Domain Topology
73(1)
Centralized Domain Topology
74(3)
Picking the Right Topology
77(1)
Landing Zone Topologies: Managing Solution Spaces
78(2)
Single Data Landing Zone
80(7)
Source- and Consumer-Aligned Landing Zones
87(1)
Hub Data Landing Zone
88(1)
Multiple Data Landing Zones
89(3)
Multiple Data Management Landing Zones
92(1)
Practical Landing Zones Example
93(2)
Wrapping Up
95(4)
4 Data Product Management
99(44)
What Are Data Products?
99(1)
Problems with Combining Code, Data, Metadata, and Infrastructure
100(1)
Data Products as Logical Entities
101(2)
Data Product Design Patterns
103(1)
What Is CQRS?
104(2)
Read Replicas as Data Products
106(1)
Design Principles for Data Products
107(1)
Resource-Oriented Read-Optimized Design
108(1)
Data Product Data Is Immutable
109(1)
Using the Ubiquitous Language
109(1)
Capture Directly from the Source
110(1)
Clear Interoperability Standards
110(1)
No Raw Data
110(1)
Don't Conform to Consumers
111(1)
Missing Values, Defaults, and Data Types
112(1)
Semantic Consistency
112(1)
Atomicity
112(1)
Compatibility
113(1)
Abstract Volatile Reference Data
113(1)
New Data Means New Ownership
113(1)
Data Security Patterns
114(1)
Establish a Metamodel
114(1)
Allow Self-Service
115(1)
Cross-Domain Relationships
115(1)
Enterprise Consistency
115(1)
Historization, Redeliveries, and Overwrites
116(1)
Business Capabilities with Multiple Owners
116(1)
Operating Model
116(1)
Data Product Architecture
117(1)
High-Level Platform Design
117(2)
Capabilities for Capturing and Onboarding Data
119(2)
Data Quality
121(1)
Data Historization
122(5)
Solution Design
127(2)
Real-World Example
129(4)
Alignment with Storage Accounts
133(1)
Alignment with Data Pipelines
134(1)
Capabilities for Serving Data
135(1)
Data Serving Services
136(1)
File Manipulation Service
137(1)
De-Identification Service
137(1)
Distributed Orchestration
138(1)
Intelligent Consumption Services
138(1)
Direct Usage Considerations
139(1)
Getting Started
139(1)
Wrapping Up
140(3)
5 Services and API Management
143(30)
Introducing API Management
144(1)
What Is Service-Oriented Architecture?
145(3)
Enterprise Application Integration
148(2)
Service Orchestration
150(3)
Service Choreography
153(1)
Public Services and Private Services
154(1)
Service Models and Canonical Data Models
154(1)
Parallels with Enterprise Data Warehousing Architecture
155(2)
A Modern View of API Management
157(1)
Federated Responsibility Model
157(1)
API Gateway
158(2)
API as a Product
160(1)
Composite Services
160(1)
API Contracts
161(1)
API Discoverability
161(1)
Microservices
161(1)
Functions
162(1)
Service Mesh
162(2)
Microservice Domain Boundaries
164(1)
Ecosystem Communication
165(1)
Experience APIs
166(1)
GraphQL
166(1)
Backend for Frontend
167(1)
Practical Example
167(2)
Metadata Management
169(1)
Read-Oriented APIs Serving Data Products
170(1)
Wrapping Up
170(3)
6 Event and Notification Management
173(28)
Introduction to Events
174(1)
Notifications Versus Carried State
175(1)
The Asynchronous Communication Model
176(1)
What Do Modern Event-Driven Architectures Look Like?
177(1)
Message Queues
177(1)
Event Brokers
177(2)
Event Processing Styles
179(1)
Event Producers
180(2)
Event Consumers
182(2)
Event Streaming Platforms
184(7)
Governance Model
191(1)
Event Stores as Data Product Stores
192(1)
Event Stores as Application Backends
193(1)
Streaming as the Operational Backbone
193(1)
Guarantees and Consistency
194(1)
Consistency Level
194(1)
Processing Methods
195(1)
Message Order
196(1)
Dead Letter Queue
196(1)
Streaming Interoperability
196(1)
Governance and Self-Service
197(1)
Wrapping Up
198(3)
7 Connecting the Dots
201(22)
Cross-Domain Interoperability
202(1)
Quick Recap
203(1)
Data Distribution Versus Application Integration
204(1)
Data Distribution Patterns
205(1)
Application Integration Patterns
206(2)
Consistency and Discoverability
208(4)
Inspiring, Motivating, and Guiding for Change
212(1)
Setting Domain Boundaries
213(2)
Exception Handling
215(1)
Organizational Transformation
216(2)
Team Topologies
218(3)
Organizational Planning
221(1)
Wrapping Up
222(1)
8 Data Governance and Data Security
223(40)
Data Governance
223(1)
The Governance Framework
224(6)
Processes: Data Governance Activities
230(1)
Making Governance Effective and Pragmatic
231(3)
Supporting Services for Data Governance
234(2)
Data Contracts
236(5)
Data Security
241(1)
Current Siloed Approach
241(1)
Trust Boundaries
242(1)
Data Classifications and Labels
243(1)
Data Usage Classifications
244(1)
Unified Data Security
245(3)
Identity Providers
248(1)
Real-World Example
248(3)
Typical Security Process Flow
251(5)
Securing API-Based Architectures
256(3)
Securing Event-Driven Architectures
259(1)
Wrapping Up
260(3)
9 Democratizing Data with Metadata
263(28)
Metadata Management
265(1)
The Enterprise Metadata Model
266(1)
Practical Example of a Metamodel
267(2)
Data Domains and Data Products
269(1)
Data Models
270(5)
Data Lineage
275(1)
Other Metadata Areas
275(2)
The Metalake Architecture
277(1)
Role of the Catalog
277(2)
Role of the Knowledge Graph
279(9)
Wrapping Up
288(3)
10 Modern Master Data Management
291(20)
Master Data Management Styles
293(2)
Data Integration
295(1)
Designing a Master Data Management Solution
296(1)
Domain-Oriented Master Data Management
297(1)
Reference Data
297(2)
Master Data
299(3)
MDM and Data Quality as a Service
302(1)
MDM and Data Curation
303(1)
Knowledge Exchange
304(1)
Integrated Views
305(1)
Reusable Components and Integration Logic
305(1)
Republishing Data Through Integration Hubs
305(1)
Republishing Data Through Aggregates
306(2)
Data Governance Recommendations
308(1)
Wrapping Up
309(2)
11 Turning Data into Value
311(38)
The Challenges of Turning Data into Value
312(2)
Domain Data Stores
314(4)
Granularity of Consumer-Aligned Use Cases
318(2)
DDSs Versus Data Products
320(2)
Best Practices
322(1)
Business Requirements
322(1)
Target Audience and Operating Model
323(1)
Nonfunctional Requirements
324(2)
Data Pipelines and Data Models
326(3)
Scoping the Role Your DDSs Play
329(2)
Business Intelligence
331(1)
Semantic Layers
331(2)
Self-Service Tools and Data
333(2)
Best Practices
335(1)
Advanced Analytics (MLOps)
336(3)
Initiating a Project
339(1)
Experimentation and Tracking
340(2)
Data Engineering
342(1)
Model Operationalization
343(1)
Exceptions
344(1)
Wrapping Up
345(4)
12 Putting Theory into Practice
349(24)
A Brief Reflection on Your Data Journey
349(1)
Centralized or Decentralized?
350(1)
Making It Real
351(1)
Opportunistic Phase: Set Strategic Direction
351(5)
Transformation Phase: Lay Out the Foundation
356(5)
Optimization Phase: Professionalize Your Capabilities
361(4)
Data-Driven Culture
365(1)
DataOps
365(4)
Governance and Literacy
369(1)
The Role of Enterprise Architects
369(1)
Blueprints and Diagrams
370(1)
Modern Skills
370(1)
Control and Governance
370(1)
Last Words
371(2)
Index 373
Piethein Strengholt is passionate about technology, innovation, and data! He likes to solve problems at scale and is very familiar with topics such as data management, data integration, and cloud. Piethein is chief data architect for a large enterprise, where he oversees data strategy and its impact on the organization. Prior to this role, he worked as a strategy consultant, designing many architectures and participating in large data management programs, and as a freelance application developer. He lives in the Netherlands with his family.
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