Managing Data in Motion Data Integration Best Practice Techniques and Technologies

ISBN-10: 0123971675

ISBN-13: 9780123971678

Edition: 2013

Authors: April Reeve

List price: $49.95
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Description:

The management of the "data in motion" in organizations is rapidly becoming one of the biggest concerns for business and IT management. As even more systems are added into an organization's portfolio the complexity of the interfaces between the systems grows exponentially, making management of those interfaces overwhelming. Learn the techniques, technologies, and best practices to integrate disparate data together in an enterprise environment in this definitive, vendor-neutral reference from data integration expert April Reeve. Managing Data in Motion: Data Integration Best Practice Techniques and Technologies includes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling a scalable architecture. Author April Reeve brings over two decades of experience to present a sensible approach that can be understood by IT and business managers as well as programmers and architects. Presents a vendor-neutral overview of the different technologies and techniques for moving data between computer systems Explains the architecture and components required for an organization to perform Data Integration Appeals to managers and programmers because it doesn't require an extensive technology background
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Book details

List price: $49.95
Copyright year: 2013
Publisher: Elsevier Science & Technology Books
Publication date: 3/15/2013
Binding: Paperback
Pages: 204
Size: 7.50" wide x 9.00" long x 0.50" tall
Weight: 1.210
Language: English

Foreword
Acknowledgements
Biography
Introduction
Introduction to Data Integration
The Importance of Data Integration
The natural complexity of data interfaces
The rise of purchased vendor packages
Key enablement of big data and virtualization
What Is Data Integration?
Data in motion
Integrating into a common format-transforming data
Migrating data from one system to another
Moving data around the organization
Pulling information from unstructured data
Moving process to data
Types and Complexity of Data Integration
The differences and similarities in managing data in motion and persistent data
Batch data integration
Real-time data integration
Big data integration
Data virtualization
The Process of Data Integration Development
The data integration development life cycle
Inclusion of business knowledge and expertise
Batch Data Integration
Introduction to Batch Data Integration
What is batch data integration?
Batch data integration life cycle
Extract, Transform, and Load
What is ETL?
Profiling
Extract
Staging
Access layers
Transform
Simple mapping
Lookups
Aggregation and normalization
Calculation
Load
Data Warehousing
What is data warehousing?
Layers in an enterprise data warehouse architecture
Operational application layer
External data
Data staging areas coming into a data warehouse
Data warehouse data structure
Staging from data warehouse to data mart or business intelligence
Business Intelligence Layer
Types of data to load in a data warehouse
Master data in a data warehouse
Balance and snapshot data in a data warehouse
Transactional data in a data warehouse
Events
Reconciliation
Interview with an expert: Krish Krishnan on data warehousing and data integration
Data Conversion
What is data conversion?
Data conversion life cycle
Data conversion analysis
Best practice data loading
Improving source data quality
Mapping to target
Configuration data
Testing and dependencies
Private data
Proving
Environments
Data Archiving
What is data archiving?
Selecting data to archive
Can the archived data be retrieved?
Conforming data structures in the archiving environment
Flexible data structures
Interview with an expert: John Anderson on data archiving and data integration
Batch Data Integration Architecture and Metadata
What is batch data integration architecture?
Profiling tool
Modeling tool
Metadata repository
Data movement
Transformation
Scheduling
Interview with an expert: Adrienne Tannenbaum on metadata and data integration
Real Time Data Integration
Introduction to Real-Time Data Integration
Why real-time data integration?
Why two sets of technologies?
Data Integration Patterns
Interaction patterns
Loose coupling
Hub and spoke
Synchronous and asynchronous interaction
Request and reply
Publish and subscribe
Two-phase commit
Integrating interaction types
Core Real-Time Data Integration Technologies
Confusing terminology
Enterprise service bus (ESB)
Interview with an expert: David S. Linthicum on ESB and data integration
Service-oriented architecture (SOA)
Extensible markup language (XML)
Interview with an expert: M. David Allen on XML and data integration
Data replication and change data capture
Enterprise application integration (EAI)
Enterprise information integration (EII)
Data Integration Modeling
Canonical modeling
Interview with an expert: Dagna Gaythorpe on canonical modeling and data integration
Message modeling
Master Data Management
Introduction to master data management
Reasons for a master data management solution
Purchased packages and master data
Reference data
Masters and slaves
External data
Master data management functionality
Types of master data management solutions-registry and data hub
Data Warehousing with Real-Time Updates
Corporate information factory
Operational data store
Master data moving to the data warehouse
Interview with an expert: Krish Krishnan on real-time data warehousing updates
Real-Time Data Integration Architecture and Metadata
What is real-time data integration metadata?
Modeling
Profiling
Metadata repository
Enterprise service bus-data transformation and orchestration
Technical mediation
Business content
Data movement and middleware
External interaction
Big, Cloud, Virtual Data
Introduction to Big Data Integration
Data integration and unstructured data
Big data, cloud data, and data virtualization
Cloud Architecture and Data Integration
Why is data integration important in the cloud?
Public cloud
Cloud security
Cloud latency
Cloud redundancy
Data Virtualization
A technology whose time has come
Business uses of data virtualization
Business intelligence solutions
Integrating different types of data
Quickly add or prototype adding data to a data warehouse
Present physically disparate data together
Leverage various data and models triggering transactions
Data virtualization architecture
Sources and adapters
Mappings and models and views
Transformation and presentation
Big Data Integration
What is big data?
Big data dimension-volume
Massive parallel processing-moving process to data
Hadoop and MapReduce
Integrating with external data
Visualization
Big data dimension-variety
Types of data
Integrating different types of data
Interview with an expert: William McKnight on Hadoop and data integration
Big data dimension-velocity
Streaming data
Sensor and GPS data
Social media data
Traditional big data use cases
More big data use cases
Health care
Logistics
National security
Leveraging the power of big data-real-time decision support
Triggering action
Speed of data retrieval from memory versus disk
From data analytics to models, from streaming data to decisions
Big data architecture
Operational systems and data sources
Intermediate data hubs
Business intelligence tools
Data virtualization server
Batch and real-time data integration tools
Analytic sandbox
Risk response systems/recommendation engines
Interview with an expert: John Haddad on Big Data and data integration
Conclusion to Managing Data in Motion
Data integration architecture
Why data integration architecture?
Data integration life cycle and expertise
Security and privacy
Data integration engines
Operational continuity
ETL engine
Enterprise service bus
Data virtualization server
Data movement
Data integration hubs
Master data
Data warehouse and operational data store
Enterprise content management
Data archive
Metadata management
Data discovery
Data profiling
Data modeling
Data flow modeling
Metadata repository
The end
References
Index
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