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Data Management at Scale: Best Practices for Enterprise Architecture [Pehme köide]

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  • Formaat: Paperback / softback, 300 pages, kõrgus x laius: 233x178 mm
  • Ilmumisaeg: 31-Aug-2020
  • Kirjastus: O'Reilly Media
  • ISBN-10: 149205478X
  • ISBN-13: 9781492054788
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Data Management at Scale: Best Practices for Enterprise ArchitectureSuurem pilt
  • Formaat: Paperback / softback, 300 pages, kõrgus x laius: 233x178 mm
  • Ilmumisaeg: 31-Aug-2020
  • Kirjastus: O'Reilly Media
  • ISBN-10: 149205478X
  • ISBN-13: 9781492054788
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781492054788.html
Märksõnad:

As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you&;ll learnhow to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption.

Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed.

  • Examine data management trends, including technological developments, regulatory requirements, and privacy concerns
  • Go deep into the Scaled Architecture and learn how the pieces fit together
  • Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata
Foreword ix
Preface xi
1 The Disruption of Data Management
1(16)
Data Management
2(3)
Analytics Is Fragmenting the Data Landscape
5(1)
Speed of Software Delivery Is Changing
6(1)
Networks Are Getting Faster
7(1)
Privacy and Security Concerns Are a Top Priority
8(1)
Operational and Transactional Systems Need to Be Integrated
9(1)
Data Monetization Requires an Ecosystem-to-Ecosystem Architecture
9(1)
Enterprises Are Saddled with Outdated Data Architectures
10(5)
Enterprise Data Warehouse and Business Intelligence
10(3)
Data Lake
13(2)
Centralized View
15(1)
Summary
15(2)
2 Introducing the Scaled Architecture: Organizing Data at Scale
17(34)
Universally Acknowledged Starting Points
18(2)
Each Application Has an Application Database
18(1)
Applications Are Specific and Have Unique Context
18(1)
Golden Source
18(1)
There's No Escape from the Data Integration Dilemma
19(1)
Applications Play the Roles of Data Providers and Data Consumers
19(1)
Key Theoretical Considerations
20(13)
Object-Oriented Programming Principles
21(1)
Domain-Driven Design
22(3)
Business Architecture
25(8)
Communication and Integration Patterns
33(2)
Point-to-Point
33(1)
Silos
34(1)
Hub-Spoke Model
34(1)
Scaled Architecture
35(14)
Golden Sources and Domain Data Stores
36(2)
Data Delivery Contracts and Data Sharing Agreements
38(1)
Eliminating the Siloed Approach
39(1)
Domain-Driven Design on an Enterprise Scale
40(3)
Read-Optimized Data
43(1)
Data Layer as a Holistic Picture
44(3)
Metadata and the Target Operating Model
47(2)
Summary
49(2)
3 Managing Vast Amounts of Data: The Read-Only Data Stores Architecture
51(34)
Introducing the RDS Architecture
51(1)
Command and Query Responsibility Segregation
52(6)
What Is CQRS?
52(2)
CQRS at Scale
54(4)
Read-Only Data Store Components and Services
58(20)
Metadata
59(2)
Data Quality
61(2)
RDS Tiers
63(1)
Data Ingestion
64(3)
Integrating Commercial Off-the-Shelf Solutions
67(1)
Extracting Data from External APIs and SaaSs
67(1)
Historical Data Service
68(3)
Design Variations
71(2)
Data Replication
73(1)
Access Layer
74(2)
File Manipulation Service
76(1)
Delivery Notification Service
76(1)
De-Identification Service
76(1)
Distributed Orchestration
77(1)
Intelligent Consumption Services
78(3)
Populating RDSs on Demand
81(1)
RDS Direct Usage Considerations
82(1)
Summary
82(3)
4 Services and API Management: The API Architecture
85(36)
Introducing the API Architecture
85(1)
What Is Service-Oriented Architecture?
86(13)
Enterprise Application Integration
89(3)
Service Orchestration
92(3)
Service Choreography
95(1)
Public Services and Private Services
96(1)
Service Models and Canonical Data Models
97(1)
Similarities Between SOA and Enterprise Data Warehousing Architecture
98(1)
Modern View on SOA
99(7)
API Gateway
100(1)
Responsibility Model
101(2)
The New Role of the ESB
103(1)
Service Contracts
104(1)
Service Discovery
105(1)
Microservices
106(7)
The Role of the API Gateway Within Microservices
107(1)
Functions
108(2)
Service Mesh
110(1)
Microservices Boundaries
111(1)
Microservices Within the API Reference Architecture
112(1)
Ecosystem Communication
113(2)
API-Based Communication Channels
115(2)
GraphQL
116(1)
Backend for Frontend
117(1)
Metadata
117(2)
Using RDSs for Real-Time and Intensive Reads
119(1)
Summary
120(1)
5 Event and Response Management: The Streaming Architecture
121(34)
Introducing the Streaming Architecture
121(1)
The Asynchronous Event Model Makes the Difference
122(1)
What Do Event-Driven Architectures Look Like?
123(4)
Mediator Topology
124(1)
Broker Topology
125(1)
Event Processing Styles
125(2)
A Gentle Introduction to Apache Kafka
127(4)
Distributed Event Data
129(1)
Apache Kafka Features
130(1)
The Streaming Architecture
131(16)
Event Producers
131(3)
Event Consumers
134(2)
Event Platform
136(1)
Event Sourcing and Command Sourcing
137(2)
Governance Model
139(1)
Business Streams
140(3)
Streaming Consumption Patterns
143(2)
Event-Carried State Transfer
145(1)
Playing the Role of an RDS
146(1)
Using Streaming to Populate RDSs
146(1)
Controls and Policies for Guiding the Domains
147(1)
Streaming as the Operational Backbone
147(1)
Guarantees and Consistency
148(3)
Consistency Level
148(1)
"At Least Once, Exactly Once, and at Most Once" Processing
149(1)
Message Order
149(1)
Dead Letter Queue
150(1)
Streaming Interoperability
150(1)
Metadata for Governance and Self-Service Models
151(1)
Summary
152(3)
6 Connecting the Dots
155(30)
Recap of the Architectures
155(4)
RDS Architecture
156(1)
API Architecture
156(1)
Streaming Architecture
157(1)
Strengthening Patterns
157(2)
Enterprise Interoperability Standards
159(10)
Stable Data Endpoints
159(3)
Data Delivery Contracts
162(1)
Accessible and Addressable Data
163(1)
Crossing Network Principles
164(5)
Enterprise Data Standards
169(13)
Consumption-Optimization Principles
170(3)
Discoverability of Metadata
173(3)
Semantic Consistency
176(4)
Supplying the Corresponding Metadata
180(1)
Data Origination and Movements
180(2)
Reference Architecture
182(2)
Summary
184(1)
7 Sustainable Data Governance and Data Security
185(34)
Data Governance
185(15)
Organization: Data Governance Roles
187(2)
Processes: Data Governance Activities
189(2)
People: Trust and Ethical, Social, and Economic Considerations
191(1)
Technology: Golden Source, Ownership, and Application Administration
191(2)
Data: Golden Sources, Golden Datasets, and Classifications
193(7)
Data Security
200(9)
Current Siloed Approach
201(1)
Unified Data Security for Architectures
201(2)
Identity Providers
203(1)
Security Reference Architecture and Data Context Approach
204(1)
Security Process Flow
205(4)
Practical Guidance
209(8)
RDS Architecture
209(2)
API Architecture
211(4)
Streaming Architecture
215(1)
Intelligent Learning Engine
216(1)
Summary
217(2)
8 Turning Data into Value
219(30)
Consumption Patterns
220(3)
Using Read-Only Data Stores Directly
220(1)
Domain Data Stores
221(2)
Target Operating Model
223(1)
Data Professionals as a Target User Group
224(1)
Business Requirements
225(1)
Nonfunctional Requirements
226(1)
Building the Data Pipeline and Data Model
227(6)
Distributing Integrated Data
233(2)
Business Intelligence Capabilities
235(1)
Self-Service Capabilities
236(3)
Analytical Capabilities
239(4)
Standard Infrastructure for Automated Deployments
240(1)
Stateless Models
240(1)
Prescripted and Configured Workbenches
240(1)
Standardize on Model Integration Patterns
241(1)
Automation
242(1)
Model Metadata
242(1)
Advanced Analytics Reference Architecture
243(4)
Summary
247(2)
9 Mastering Enterprise Data Assets
249(16)
Demystifying Master Data Management
250(1)
Master Data Management Styles
250(2)
MDM Reference Architecture
252(4)
Designing a Master Data Management Solution
253(1)
MDM Distribution
254(1)
Master Identification Numbers
255(1)
Reference Data Versus Master Data
256(1)
Determining the Scope of Your Enterprise Data
256(3)
MDM and Data Quality as a Service
259(1)
Curated Data
259(3)
Metadata Exchange
260(1)
Integrated Views
260(1)
Reusable Components and Integration Logic
261(1)
Data Republishing
261(1)
Relation to Data Governance
262(1)
Summary
262(3)
10 Democratizing Data with Metadata
265(22)
Metadata Management
265(2)
Enterprise Metadata Model
267(7)
Enterprise Knowledge Graph
274(4)
Architectural Approaches for Metadata Management
278(4)
Metadata Interoperability
279(1)
Metadata Repositories
280(2)
Marketplace to Provide Rapid Access to Authorized Data
282(3)
Summary
285(2)
11 Conclusion
287(10)
Delivery Model
288(4)
Fully Decentralized Approach
289(1)
Partially Decentralized Approach
290(1)
Structuring Teams
290(1)
InnerSource Strategy
291(1)
Culture
292(1)
Technology Choices
292(1)
The Decline of Traditional Enterprise Architecture
293(2)
Blueprints and Diagrams
293(1)
Modern Skills
294(1)
Control and Governance
294(1)
Last Words
295(2)
Glossary 297(14)
Index 311
Piethein Strengholt started with technologies at the age of 13 and never stopped learning. Very young, he started creating websites, started programming and had his own hosting company. Piethein's passion for technology has since lasted and helped him in his career as a consultant and later as a senior manager at a big consultancy firm.

Three years ago, he switched to ABN AMRO to be assigned with the responsibility to deliver a vision and strategy for the topics 'Cloud, Data Management, Data Integration, Data Warehousing, Business Intelligence, Big Data & Analytics'. The success and fun he has is immense and really keeps his passion for technology trends alive.