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Data Management at Scale

Best Practices for Enterprise Architecture

Paperback Engels 2020 9781492054788
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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


Aantal pagina's:328
Hoofdrubriek:IT-management / ICT


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Over Piethein Strengholt

Piethein Strengholt likes to find practical and lasting solutions to complex problems. After working for more than a decade as a strategy consultant and freelance application developer, he joined ABN AMRO as a principal architect to accelerate subjects like data management, cloud, and integration. In this exciting role, he oversees the company’s data strategy and its impact on the organization. He lives in the Netherlands with his family.

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Who Is This Book For?
What Will I Learn?
Navigating Through This Book
Conventions Used in This Book
O’Reilly Online Learning
How to Contact Us

1. The Disruption of Data Management
Data Management
Analytics Is Fragmenting the Data Landscape
Speed of Software Delivery Is Changing
Networks Are Getting Faster
Privacy and Security Concerns Are a Top Priority
Operational and Transactional Systems Need to Be Integrated
Data Monetization Requires an Ecosystem-to-Ecosystem Architecture
Enterprises Are Saddled with Outdated Data Architectures
Enterprise Data Warehouse and Business Intelligence
Data Lake
Centralized View

2. Introducing the Scaled Architecture: Organizing Data at Scale
Universally Acknowledged Starting Points
Each Application Has an Application Database
Applications Are Specific and Have Unique Context
Golden Source
There’s No Escape from the Data Integration Dilemma
Applications Play the Roles of Data Providers and Data Consumers
Key Theoretical Considerations
Object-Oriented Programming Principles
Domain-Driven Design
Business Architecture
Communication and Integration Patterns
Hub-Spoke Model
Scaled Architecture
Golden Sources and Domain Data Stores
Data Delivery Contracts and Data Sharing Agreements
Eliminating the Siloed Approach
Domain-Driven Design on an Enterprise Scale
Read-Optimized Data
Data Layer as a Holistic Picture
Metadata and the Target Operating Model

3. Managing Vast Amounts of Data: The Read-Only Data Stores Architecture
Introducing the RDS Architecture
Command and Query Responsibility Segregation
What Is CQRS?
CQRS at Scale
Read-Only Data Store Components and Services
Data Quality
RDS Tiers
Data Ingestion
Integrating Commercial Off-the-Shelf Solutions
Extracting Data from External APIs and SaaSs
Historical Data Service
Design Variations
Data Replication
Access Layer
File Manipulation Service
Delivery Notification Service
De-Identification Service
Distributed Orchestration
Intelligent Consumption Services
Populating RDSs on Demand
RDS Direct Usage Considerations

4. Services and API Management: The API Architecture
Introducing the API Architecture
What Is Service-Oriented Architecture?
Enterprise Application Integration
Service Orchestration
Service Choreography
Public Services and Private Services
Service Models and Canonical Data Models
Similarities Between SOA and Enterprise Data Warehousing Architecture
Modern View on SOA
API Gateway
Responsibility Model
The New Role of the ESB
Service Contracts
Service Discovery
The Role of the API Gateway Within Microservices
Service Mesh
Microservices Boundaries
Microservices Within the API Reference Architecture
Ecosystem Communication
API-Based Communication Channels
Backend for Frontend
Using RDSs for Real-Time and Intensive Reads

5. Event and Response Management: The Streaming Architecture
Introducing the Streaming Architecture
The Asynchronous Event Model Makes the Difference
What Do Event-Driven Architectures Look Like?
Mediator Topology
Broker Topology
Event Processing Styles
A Gentle Introduction to Apache Kafka
Distributed Event Data
Apache Kafka Features
The Streaming Architecture
Event Producers
Event Consumers
Event Platform
Event Sourcing and Command Sourcing
Governance Model
Business Streams
Streaming Consumption Patterns
Event-Carried State Transfer
Playing the Role of an RDS
Using Streaming to Populate RDSs
Controls and Policies for Guiding the Domains
Streaming as the Operational Backbone
Guarantees and Consistency
Consistency Level
“At Least Once, Exactly Once, and at Most Once” Processing
Message Order
Dead Letter Queue
Streaming Interoperability
Metadata for Governance and Self-Service Models

6. Connecting the Dots
Recap of the Architectures
RDS Architecture
API Architecture
Streaming Architecture
Strengthening Patterns
Enterprise Interoperability Standards
Stable Data Endpoints
Data Delivery Contracts
Accessible and Addressable Data
Crossing Network Principles
Enterprise Data Standards
Consumption-Optimization Principles
Discoverability of Metadata
Semantic Consistency
Supplying the Corresponding Metadata
Data Origination and Movements
Reference Architecture

7. Sustainable Data Governance and Data Security
Data Governance
Organization: Data Governance Roles
Processes: Data Governance Activities
People: Trust and Ethical, Social, and Economic Considerations
Technology: Golden Source, Ownership, and Application Administration
Data: Golden Sources, Golden Datasets, and Classifications
Data Security
Current Siloed Approach
Unified Data Security for Architectures
Identity Providers
Security Reference Architecture and Data Context Approach
Security Process Flow
Practical Guidance
RDS Architecture
API Architecture
Streaming Architecture
Intelligent Learning Engine

8. Turning Data into Value
Consumption Patterns
Using Read-Only Data Stores Directly
Domain Data Stores
Target Operating Model
Data Professionals as a Target User Group
Business Requirements
Nonfunctional Requirements
Building the Data Pipeline and Data Model
Distributing Integrated Data
Business Intelligence Capabilities
Self-Service Capabilities
Analytical Capabilities
Standard Infrastructure for Automated Deployments
Stateless Models
Prescripted and Configured Workbenches
Standardize on Model Integration Patterns
Model Metadata
Advanced Analytics Reference Architecture

9. Mastering Enterprise Data Assets
Demystifying Master Data Management
Master Data Management Styles
MDM Reference Architecture
Designing a Master Data Management Solution
MDM Distribution
Master Identification Numbers
Reference Data Versus Master Data
Determining the Scope of Your Enterprise Data
MDM and Data Quality as a Service
Curated Data
Metadata Exchange
Integrated Views
Reusable Components and Integration Logic
Data Republishing
Relation to Data Governance

10. Democratizing Data with Metadata
Metadata Management
Enterprise Metadata Model
Enterprise Knowledge Graph
Architectural Approaches for Metadata Management
Metadata Interoperability
Metadata Repositories
Marketplace to Provide Rapid Access to Authorized Data

11. Conclusion
Delivery Model
Fully Decentralized Approach
Partially Decentralized Approach
Structuring Teams
InnerSource Strategy
Technology Choices
The Decline of Traditional Enterprise Architecture
Blueprints and Diagrams
Modern Skills
Control and Governance
Last Words


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