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  • Book Overview & Buying Applied Architecture Patterns on the Microsoft Platform (Second Edition)
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Applied Architecture Patterns on the Microsoft Platform (Second Edition)

Applied Architecture Patterns on the Microsoft Platform (Second Edition) - Second Edition

By : Dovgal, Noriskin
4.9 (7)
Buy this Book
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Applied Architecture Patterns on the Microsoft Platform (Second Edition)

Applied Architecture Patterns on the Microsoft Platform (Second Edition)

4.9 (7)
By: Dovgal, Noriskin
Buy this Book

Overview of this book

Presented in a scenario-driven tutorial way, we lead you through fictitious example problems and present you with the best solutions. This book is intended for architects, developers, and managers who need to improve their knowledge of the Microsoft application platform. This book will appeal to anyone, especially consultants, who want to get up to speed on selecting the most appropriate platform for a particular problem. A good understanding of the general Windows platform and development technologies would be helpful.
Table of Contents (15 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Solution Decision Framework
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1. Solution Decision Framework
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The need for a decision framework
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Sources of input
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Deciding upon your architecture strategy
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Technology evaluation dimensions
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Applying the framework
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Summary
2
2. The .NET Framework Primer
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2. The .NET Framework Primer
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An evaluation framework for .NET Framework APIs
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The .NET Framework
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Summary
3
3. The SQL Server Primer
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3. The SQL Server Primer
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What is included in SQL Server 2012?
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Building the payroll processor application
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New features of SQL Server 2014
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Summary
4
4. The SSIS Primer
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4. The SSIS Primer
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What's new in SSIS 2012
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Building the payroll processor application
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What's new in SSIS 2014
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Summary
5
5. The BizTalk Server Primer
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5. The BizTalk Server Primer
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Using BizTalk in an integration architecture
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BizTalk essentials
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New features of BizTalk Server 2013
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BizTalk Server editions
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BizTalk Server abilities
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Building the BizTalk application
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Summary
6
6. The SharePoint Server Primer
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6. The SharePoint Server Primer
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The SharePoint editions
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The SharePoint platform
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Administration and management
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Core concepts and capabilities of SharePoint
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SharePoint extensibility
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SharePoint APIs
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SharePoint development tools
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Summary
7
7. Other Microsoft Technologies
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7. Other Microsoft Technologies
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Operating systems
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The Microsoft Office software
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Software development tools
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Cloud computing
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Summary
8
8. Integration Patterns and Antipatterns
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8. Integration Patterns and Antipatterns
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Integration styles and challenges
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Point-to-point integration
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The federated request pattern
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The message broker pattern
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The guaranteed delivery problem
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The Publish/Subscribe pattern
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Moving to Enterprise Service Bus
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Summary
9
9. Web Services and Beyond
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9. Web Services and Beyond
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Service-oriented architecture
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Enterprise Service Bus
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RESTful services
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Summary
10
10. Data Exchange Patterns
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10. Data Exchange Patterns
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File transfer
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A shared database
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Data replication and synchronization
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Data migration
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The extract, transform, and load pattern for centralized data aggregation
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Multiple master synchronization
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Data sharing and federation
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Summary
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11. Workflow Patterns
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11. Workflow Patterns
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Fully automated workflows
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Human workflows
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Summary
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12. Presentation Layer Patterns
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12. Presentation Layer Patterns
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Building presentation layers
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MVC, MVP, and MVVM
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Working with a use case – a user interface for the shopping cart
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Working with a use case – a desktop time billing application
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Summary
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13. Conclusion
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13. Conclusion
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Patterns for software-intensive systems
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Where to go next
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Index
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Index

The extract, transform, and load pattern for centralized data aggregation

The extract, transform, and load (ETL) pattern proved very useful in different architectural solutions. In data warehousing, it is used even more often than in data migration.

Tip

A data warehouse is a large data store constructed from several data sources and is intended to be used for reporting and analysis purposes. Data warehouses are constantly updated by feeding data from the data sources.

A part of a data warehouse that focuses on a specific area of interest is called a data mart.

First systems using data aggregation in a single source were developed in the early 1970s. For example, AC Nielsen, a global marketing research firm, used what they called "data mart" around that time to provide aggregated information for their customers.

The data warehouses as we know them today became a reality only in the late 1980s. The term data warehouse was originally used in the 1988 article An architecture for a business and information...

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Tech Concepts
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Programming languages
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