1. 1 JBoss Enterprise Data Services Peter Larsen JBoss
Solutions Architect, Red Hat Inc. [email protected]
2. 2 Agenda Motivation for Data Services EDS in the community /
History Positioning Data Services in an Enterprise Architecture Use
Case Domains / Customer Examples Technical Architecture Demo
3. 3 The Business Inflexibility Trap Inflexibility: The
essential Business Problem With agility all problems are solvable
With enough eyes, all bugs are shallow
4. 4 Agility is Key Because change is the only constant
Technologies Requirements Regulations Standards Models Processes
??? What is the single most important problem preventing
Agility?
5. 5 Problem: Data Challenges Challenges Different physical
structure Different terminology and meaning Different interfaces
May need to federate/integrate May be locked in to database Must
ensure performance Maintain/Improve security Tremendous value in
existing information assets, but... Time consuming and costly to
implement new applications that leverage this information Data
Warehouse Packaged Applications Operational Data Stores Data
Gap
6. 6 Problem: Data Challenges Alternatives Time consuming
difficult/costly No re-use of data logic Any changes break the
application Data Gap Data Gap Hard Code Replicate/Data Mart Data
not fresh Costly additional licenses More copies of data = more
silos Governance/security
7. 7 Solution: JBoss Enterprise Data Services JBoss Enterprise
Data Services Data Service Data ServiceData Service SQL Web
Services Access to multiple data stores in real time
Standards-based read/write access Speeds application development
Transform data structure and semantics Consolidates data into a
single view Centralized access control Enterprise-proven flexible,
scalable, high-performance Turns the Data You Have Into the
Information You Need
8. 8 Data Services Platform Where it fits JBoss Enterprise Data
Services Platform Other Vendor Portal / ESB/SOA Platforms Data
Service Data Service Data Service Data Service Data Service
9. 9 Integration Technologies ETL SOA EDS Data
ProcessIntegration Style DataIntegrationTimeliness Real Time
Batch
10. 11 Data Services Platform Common Use Cases Service Oriented
Architecture Federate/transform data efficiently use by
higher-level services Insulate business processes from data access
details Business Intelligence, Operational Analytics, Reporting
Consolidated financial reports/dashboards/KPIs Virtual data marts
Information Consolidation, Reference Data Management Single/360
view of Customer Single/360 view of Supplier Single/360 view of
Employee Regulatory Compliance Provide common security, central
access and auditing of data VISA PCI, Sarbanes Oxley, Basel II,
HIPAA
11. 12 JDBC/ODBC Query Engine Data Virtualization, Federation
JBoss Enterprise Data Services JBoss ModeShape Repository Services
JBoss jBPM JBoss Rules JBoss Enterprise SOA Platform JBossESB JBoss
Enterprise Application Platform Red Hat Enterprise Linux Windows,
UNIX, other Linux Turns the data you have into the information you
want Augments and extends SOA Platform to address data access,
integration and abstraction. SOA Patterns, best practices
Reporting/Analytics enablement Information Consolidation, Data
Mgmnt Data Governance, Compliance Real-time read/write access to
heterogeneous data stores Speeds application development by
simplifying access to distributed data Centralized access control,
auditing JBoss Enterprise Data Services
12. 13
13. 14 Data Services Platform Architecture
14. 15 Query Performance & Optimization Minimal overhead
for simpler requests Control enforce mandatory criteria with
certain requests enforce time and size limitations on requests
Rule-based optimization use criteria to avoid unnecessary fields
and records removal of unnecessary joins across data sources merge
all transformation logic for a single source Cost-based
optimization join algorithms (nested loop, merge, dependent, hash)
cost profile of each data source Data caching and staging
(materialized views) Manage dataflow buffer management
15. 16 Designer Tooling Virtual Models Physical Models
representing actual data sources Shows structural transformations
Defines transformations with Selects Joins Criteria Functions
Unions User Defined
16. 18 Semantic Mediation/Integration T Authoritative Sources:
Mapped to logical view Multiple Internal/External Information
Sources Application views of information: Relational, XML, Java T T
XML Document T T T Web Services Web Services Workflow/ ESB
Workflow/ ESB Business Applications Business Applications Claims,
Billing, Policies, bldg_id SITENUM Facility_ID Location_ID
bldg_type Depot_Number Location_Type Semantic Data Services Data
Dictionary: Based on logical data model or XML schema Support for
multiple COIs Support for multiple versions
17. 19 Data Services and the ESB User-facing Logic (Service
Consumers) Business Logic Data Logic Process and Other Integration
Logic Rich or Thin Desktop Process, Integraion Services Business
Services ESB Direct ODBC, JDBC ODBC, JDBC WSDL, SOAP, MOM, other
WSDL, SOAP, MOM, other Process Orchestration Services Data
Services
18. 20 JBoss Enterprise SOA Platform Enables Business Process
Automation by integrating and orchestrating application components
and services running on JBoss Enterprise Middleware and/or any
other standards-based AS Single distribution that integrates JBoss
ESB, jBPM, JBoss Rules, Enterprise, Application Platform Enables
multiple integration styles: SOA integration, EAI, EDA, process and
business rules technologies to automate business processes to
improve business productivity Certified Platform for Service
Integration and Orchestration Simple, Flexible, and Scalable Light
footprint, simple installation JBoss ON platform management and
services monitoring Scalable clustering to support high transaction
volumes A flexible, standards-based platform to integrate
applications, SOA services, business events and automate business
processes. Red Hat Enterprise Linux Windows, UNIX, other Linux
Workflow Rules JBoss Enterprise SOA Platform JBossESB
Transformation, Routing, Registry JBoss Enterprise Application
Platform Container services, Hibernate, Web Services stack, Seam,
Clustering, Cache, Messaging, Transactions
19. 22 Enterprise Data Services 5.2 EDS 5.2 Released December
2011 Tighter data services/ESB tooling integration Performance
tweaks LOB handling Cost-based optimizer enhancements Programmatic
view creation Repository enhancements Versioning support More
artifact types Cloud-based data sources Fixes, minor enhancements,
additional platform certs
20. 23 Business Value of Enterprise Data Services Greater
agility, faster time to solution Increased ROA Improved
organizational performance Better control of information Improved
utilization of data assets Derive more value from existing
investments Complements existing systems Jumpstart Your SOA
Initiatives! Better/faster than hand coding Faster, less costly
than data replication Data virtualization provides loose coupling
The right data at the right time to the right people Decision
support, BI with a complete view of information across the
enterprise Powerful security, Auditing, Data Firewall Avoid data
silo proliferation Central data access and policy, Compliance
21. 24 A Comprehensive Middleware Portfolio JBoss Enterprise
Data Services Platform JBoss Enterprise SOA Platform JBoss
Enterprise Application Platform JBoss Enterprise Web Platform JBoss
Enterprise Web Server Red Hat Enterprise Messaging JBoss Enterprise
Portal Platform JBoss Enterprise Business Rules Management System
JBossDeveloper Studio Seam Hibernate WebFramework Kit JBoss
Operations Network RedHatServices Cloud Implementation Cloud
GovernanceCloud Strategy & Selection VMWare Microsoft Hyper-V
Red Hat Enterprise Virtualization PrivatePublic Amazon EC2 Other
RHEL,Unix,Windows
22. 25 Where did Teiid come from? Project lineage is from
MetaMatrix starting in ~1999. Teiid - http://www.jboss.org/teiid
Teiid Designer - http://www.jboss.org/teiiddesigner DNA -
http://www.jboss.org/dna/ MetaMatrix was the leader in Enterprise
Information Integration (EII) hence Teiid. Red Hat acquired
MetaMatrix in 2007. Last major MetaMatrix product release, 5.5.4
11/09
23. 26 Project Status (March 2011) Open source 2/2009 heavily
refactored from 5.5 line 7.0 Initial release 6/2010 7.1 Teiid /
Teiid Designer release 8/2010 Basis for EDS 5.1 release with
hundreds of issues resolved and targeted enhancements 7.4 Coming
Soon! More source integration (MDX via XMLA, Ingres), expanded
function support, etc. should be picked up along the work in 7.1-3
by the next service pack release.
24. 27 Community Version Community web site:
www.jboss.org/teiid Teiid sub-projects: Teiid Runtime, Teiid
Designer Teiid 7 is built for AS7
26. 29 Generating New Value with JBoss Enterprise SOA Platform
and Enterprise Data Services
27. 30 The context Organizations Significant assets already
deployed or otherwise in use Applications, databases, services,
spreadsheets, file extracts, manual processes, tribal knowledge Not
realizing full benefit Mandate Remove business impediments, improve
status quo Control/reduce costs Derive greater value from the
assets you already have
28. 31 Common Challenges Data Decision making Inflexible
systems Manual processes
29. 32 Common Challenges Data Data sprawl Tied up in silos Not
reconciled/integrated Not easily usable Decision making Inflexible
systems Manual processes
30. 33 Common Challenges Data Decision making Insufficiently
informed Missing key information Stale or out-of-context
information Inflexible systems Manual processes
31. 34 Common Challenges Data Decision making Inflexible
systems Logic hard-coded into applications Redundant logic, not
standardized or shared Changes require development cycle,
resources, time Unable to react quickly to business, market, IT
changes Manual processes
32. 35 Common Challenges Data Decision making Inflexible
systems Manual processes Business processes are manual Data entry,
swivel-chair integration Overly dependent on individuals
Inconsistent, prone to error, difficult to govern
33. 36 Common Challenges Data Decision making Inflexible
systems Manual processes But... These data, systems, applications,
decision-making processes, business processes and logic are your
current assets waiting to be improved and put to better, more
effective use. How?
34. 37 Solution Patterns 1. Pattern: Data Foundation 2.
Pattern: Information Delivery 3. Pattern: Externalize Knowledge 4.
Pattern: Automate Decision Making 5. Pattern: Codify Business
Processes
35. 38 Solution Patterns: Data Foundation Liberate, integrate,
mediate, transform data Tap silos, gain control over data sprawl
Create foundation data layer through data virtualization xml
databases warehouses spreadsheets services sale > files
applications ExistingExisting sources andsources and silos of
datasilos of data Integrated setIntegrated set of canonicalof
canonical data objectsdata objects CRM, Employee SupplyChain,
Logistics
36. 39 Solution Patterns: Information Delivery Provide
consistent information in the form required by different
information consuming applications, processes, services. Ensure
complete information through all delivery modes/formats. Forms:
Relational Tables/Views Star schema Procedures Schema-compliant XML
Access Modes: JDBC, ODBC SOAP Web Services POJO XML over HTTP, JMS
(contract) (contract) (contract) Custom Apps Business Processes
Packaged Apps Reports, Dashboards Data warehouses
O/RMappingJDBC/OSOAP/JMS CRM, Employee SupplyChain, Logistics
37. 40 Solution Patterns: Externalize Knowledge Externalize key
business logic from application code Isolate and standardize rules
that govern business decisions and operations Enable business
analysts and development to collaborate in defining functional
behavior Rule sets possibilities: Pricing Fraud detection
Regulatory compliance Productivity/Efficiency Control systems
Product configuration ... Insurance Rules: Age Sex Health
Occupation = $ Price
38. 41 Solution Patterns: Automate Decision Making Move beyond
reports to active analysis and decision making Extend rule sets to
analyze information provided through earlier patterns. Process
information on scheduled basis or dynamically as data is flowing
through applications and on the bus. Raise alerts, initiate
corrective actions, seize opportunities sale >
39. 42 Solution Patterns: Codify Business Processes Codify the
processes actually followed by your organization Create
standardized, reusable workflows/orchestrations Eliminate
unnecessary manual steps, keep human tasks only where appropriate.
Identify common business patterns both standard normal processes
and exception remediation processes Extend automated decision
making with business processes and vice versa
40. 43 Solution Patterns 1. Pattern: Data Foundation 2.
Pattern: Information Delivery 3. Pattern: Externalize Knowledge 4.
Pattern: Automate Decision Making 5. Pattern: Codify Business
Processes
41. 44 How technologies map to patterns JDBC/ODBC Data
Virtualization Data Access, Federation JBoss Enterprise Data
Services Metadata Repository Repository Services Workflow Rules
JBossESB Transformation, Routing, Registry JBoss Enterprise
Application Platform Container services, Hibernate, Web Services
stack, Seam, Clustering, Cache, Messaging, Transactions Red Hat
Enterprise Linux Windows, UNIX, other Linux JBoss Enterprise SOA
Platform 1. Data Foundation 2. Information Delivery 3. Externalize
Knowledge 4. Automate Decision Making 5. Codify Business
Processes
42. 45 How technologies map to patterns JDBC/ODBC Data
Virtualization Data Access, Federation JBoss Enterprise Data
Services Metadata Repository Repository Services Workflow Rules
JBossESB Transformation, Routing, Registry JBoss Enterprise
Application Platform Container services, Hibernate, Web Services
stack, Seam, Clustering, Cache, Messaging, Transactions Red Hat
Enterprise Linux Windows, UNIX, other Linux JBoss Enterprise SOA
Platform 1. Data Foundation 2. Information Delivery 3. Externalize
Knowledge 4. Automate Decision Making 5. Codify Business
Processes
43. 46 How technologies map to patterns JDBC/ODBC Data
Virtualization Data Access, Federation JBoss Enterprise Data
Services Metadata Repository Repository Services Workflow Rules
JBossESB Transformation, Routing, Registry JBoss Enterprise
Application Platform Container services, Hibernate, Web Services
stack, Seam, Clustering, Cache, Messaging, Transactions Red Hat
Enterprise Linux Windows, UNIX, other Linux JBoss Enterprise SOA
Platform 1. Data Foundation 2. Information Delivery 3. Externalize
Knowledge 4. Automate Decision Making 5. Codify Business
Processes
44. 47 How technologies map to patterns JDBC/ODBC Data
Virtualization Data Access, Federation JBoss Enterprise Data
Services Metadata Repository Repository Services Workflow Rules
JBossESB Transformation, Routing, Registry JBoss Enterprise
Application Platform Container services, Hibernate, Web Services
stack, Seam, Clustering, Cache, Messaging, Transactions Red Hat
Enterprise Linux Windows, UNIX, other Linux JBoss Enterprise SOA
Platform 1. Data Foundation 2. Information Delivery 3. Externalize
Knowledge 4. Automate Decision Making 5. Codify Business
Processes
45. 48 How technologies map to patterns JDBC/ODBC Data
Virtualization Data Access, Federation JBoss Enterprise Data
Services Metadata Repository Repository Services Workflow Rules
JBossESB Transformation, Routing, Registry JBoss Enterprise
Application Platform Container services, Hibernate, Web Services
stack, Seam, Clustering, Cache, Messaging, Transactions Red Hat
Enterprise Linux Windows, UNIX, other Linux JBoss Enterprise SOA
Platform 1. Data Foundation 2. Information Delivery 3. Externalize
Knowledge 4. Automate Decision Making 5. Codify Business
Processes
46. Architecture
47. 50 Architecture Socket transport and query engine have
separate work queues and thread pools Deep integration with JBoss
AS MC, Profile Service, JCA, JTA, Web Services (consume and
produce), JAAS, standard logging
48. 51 Teiid Connector Architecture Teiid splits connectivity
concerns into: Data Sources standard JCA based pooled resources
configured on the server Translators a Teiid specific CCI (common
client interface) that accesses a particular Data Source and is
configured as part of the VDB Extended metadata from the translator
directs the optimizer source query formation. In addition to out of
the box offerings, our JDBC translator is easily extended. Can be
thought of as a JDBC/ODBC toolkit since the end result is
consumable through JDBC/ODBC
49. 52 Teiid Clustering Clustering is enabled in the SOA
production/all profile Teiid does not require clustering, but will
use it when available Clients will re-authenticate as needed in
load-balancing/fail- over scenarios The default strategy for
determining cluster members is by just using the URL. Deployments
and jar updates need to happen on all nodes. Farming should help
with this. The result set cache and internal materialized views can
be replicated.
50. 53 Other Extension Points Logging (Log4j), specific
contexts for audit and commands Configurable security domains for
admin/query access Can utilize any container supported LoginModule
User defined functions both source specific and for source/runtime
execution via a Java method Groovy scripting through AdminShell
Client discovery of Teiid instances Customizable WARs generated for
Web Service access
51. 54 Questions?Questions?
52. 55 Use Cases
53. 56 Credit Suisse: Derivatives Trading Dashboard Challenge
Monitor derivatives security trades to prevent rogue trades and
financial loss Trading data spread across many databases/systems
Solution Consolidate all trading data into single view Real-time
access Transformation of data differences Business Benefit Prevent
financial loss, lower risk Saved time and cost to develop Easier to
manage data changes Data Services Platform Dashboard Data Sources
Data Service One of many projects part of data layerOne of many
projects part of data layerOne of many projects part of data
layerOne of many projects part of data layer
54. 57 Smith Barney: Unified Customer View Challenge Branch
Managers account notes in two very different applications
(centralized DB2 on mainframe, and distributed SQL Server (600
servers) Cannot access extended account information from other
offices. Cannot manage customer only individual accounts. Two years
behind schedule in making all notes avail in one application
Solution Enable CRM application to easily find customer information
across all databases Real-time access Business Benefit Better
management of customer, improved customer service Data Services
Platform Brokerage CRM Application 600 MS SQL DBs - geographically
distributed Data Service Single view of customer key component in
new data architectureSingle view of customer key component in new
data architectureSingle view of customer key component in new data
architectureSingle view of customer key component in new data
architecture
55. 58 Large Bank: Data Security/Governance Challenge VISA PCI
mandates protection of card holder info Difficult to maintain
common security policy across multiple data stores Solution Create
data firewall across many data sources Federate rather than
replicate Common access policy and common data definitions across
sources Audit trail Business Benefit Single, central set of data
security policies Prove to auditors and regulators that data
protection requirements are being met. Data Services Platform
WebFocus Portal Data Sources Data Service Data Firewall to protect
and govern use of dataData Firewall to protect and govern use of
dataData Firewall to protect and govern use of dataData Firewall to
protect and govern use of data
56. 59 DISA GCSS-J: Unified Logistics Portal Challenge
Combatant commanders need timely logistics Data spread across many
databases/systems; each system owned & managed by different
agency Solution Provide a single capability to monitor and manage
personnel, equipment, and supplies across all databases Real-time
access Networked environment allows DoD users to access shared data
& applications regardless of location Business Benefit Single
portal for integrated logistics Isolation/abstraction from silos
Easier to manage units, personnel, equipment Data Services Platform
Multiple Logistics Tracking Applications Data Sources Data Service
Consolidated Logistics Information Deployed in TheaterConsolidated
Logistics Information Deployed in TheaterConsolidated Logistics
Information Deployed in TheaterConsolidated Logistics Information
Deployed in Theater
59. 62 Global Insurer: SOA Data Services Layer Challenge
Deploying SOA reference architecture Want common data model across
sources Don't want tightly bound data sources Need to change
sources without breaking applications Solution Data is accessed via
data services DSP provides federation and consistent logical data
model Data model exposed through Web Services and SQL Business
Benefit All applications get the same data through use of common
model Easier to consume data with new applications Easier to
change/add data sources to architecture Data Services Platform
Applications Data Sources Data Service Service-enabled, consistent
data model for SOAService-enabled, consistent data model for
SOAService-enabled, consistent data model for SOAService-enabled,
consistent data model for SOA Data Service Common Data Model SOA
Platform
60. 63 DISA ADNET: Anti-Drug Network Challenge
Counter-narcotics and counter-narcoterrorism Statutory detection
and monitoring Data is heterogenous & on multiple systems
Solution MetaMatrix provides an abstracted view across multiple
State/Local Law enforcement agencies. The virtual Database enables
BI tools to get a complete picture of a "person of interest" from
any history, warrants, jail, crimes, vehicles, etc... Also, MM is
used as a federated search layer looking for possible persons of
interest given general details (cars, addresses, license, aliases,
etc...) Benefit Enable ADNET to deliver on its mission Data
Services Platform BI tools, Portal, Federated Search Data Service
Disparate, heterogenous State/Local databases
61. 64 HQ/Langley: MDM and SOA Enablement Challenge Need to
find Person of Interest among disparate systems Adherence/mapping
to common schema Data integration for SOA enablement Solution
Created abstracted view of a Enterprise Schema that is focused on
Master Data Entities (Domains) like Person, Organizations, etc.
Provide data services layer of the SOA stack, feeding the ESB ESB
facilitates sync/async capabilities and provides integrated
enterprise data efficiently and rapidly to multiple consumers
Benefit Simplified data access and decoupled services and apps from
the underlying complex data infrastructure Single view of data
enables migration of external sources into the Enterprise
repository seamlessly and without application impact Data Services
Platform Portal, ESB, Federated Search Data Service Disparate,
heterogenous data sources with varying schemas/representations
62. 65 Intelligence Agency: Signal Analysis Portal Challenge
Intelligence analysts have to navigate multiple systems to try to
assess SIGINT Underlying data consists of both managed data assets
and live feeds Security is mission-critical Solution Provide a
single capability to monitor and analyze signal intelligence data
across databases Metadata repository allows for
metadata-aware/driven application Business Benefit Single portal
for analysis end of swivel-chair integration Federated data also
put on DCGS ESB Data Services Platform Metadata-driven Analyst
Portal Data Service Consolidated SIGINT Data - DeployedConsolidated
SIGINT Data - DeployedConsolidated SIGINT Data -
DeployedConsolidated SIGINT Data - Deployed Over a dozen unique
geospatial DBs mix of live & managed data
63. 66 Intel Architecture Data Services and the ESB Metadata
Discovery Service SIGINT Gateway Service Metadata Publishing
Service Metadata Catalog Alert Subscription Service Event
Assessment Service Weather Effect Service IMETS (IWEDA) E-Space
Services Weather Effects EW Data Alert Criteria Alerts / Events
Metadata Metadata Searches InfrastructureInfrastructure
ServicesServices ISR Data Listener Service Async Callbacks Filters
Workflow Engine Service Management HUMINT Data Service(s) HDWS
(CHAMS) Map / Coverage Google Earth Rich Client Handheld NCES
Service Discovery Transformation Engine BC Gateway Service Force
Tracking MIP Blue Force Tracking Google Earth Rich Client DCGS-A
Services Network EnterpriseServiceBus
64. 67 Backup Slides
65. 68 Caching Overview
66. 69 ResultSet Caching Caching of user query results. Scoping
of results is automatically determined to be either VDB
(replicated) or session level. Configurable number of cache entries
and time to live. Caching of XML document model results.
Administrative clearing.
67. 70 CodeTable Caching Short cut to creating an internal
materialized view table via the lookup function Way to get a value
out of a table when a key value is provided. Example:
Lookup(ISOCountryCodes,CountryName, CountryCode,US) Limitations
(why use Materialized Views): No option to use the lookup function
and not perform caching. No mechanism is provided to refresh code
tables.
68. 71 Materialized Views Transformations are pre-computed and
stored just like a regular table When queries are issued against
the views, the cached results are used Improve Performance/Cost of
accessing all the underlying data sources and re-computing the view
transforms each time a query is executed Supports no cache
queries(Fresh Data full or partial) SELECT * from vg1, vg2, vg3
WHERE ... OPTION NOCACHE Internal materialization creates Teiid
temporary tables to hold the materialized table
69. 72 When to Use Materialized Views? Underlying data does not
change rapidly It is acceptable to retrieve data that is "stale"
within some period of time Access staged data rather than placing
additional query load on operational sources.
70. 73 Cache Hints How They Are Used Indicate that a user query
is eligible for result set caching Set the result set query cache
entry memory preference or time to live Set the materialized view
memory preference, time to live, or updatability
/*+cache[([pref_mem][ttl:n][updatable])]*/ pref_mem - if present
indicates that the cached results should prefer to remain in memory
ttl - if present n indicates the time to live value in milliseconds
updatable - if present indicates that the cached results can be
updated
71. 74 Why JBoss Enterprise Data Services Platform Data
Virtualization Technology Real-time integration of diverse data,
federation Break down existing data silos, avoid creating new ones
Decouple applications from data stores through data services
Maintain control and security of information Value Maximize ROA -
Return on Assets - get the most out of your existing information
and data stores. Faster route to deployment, rapid prototyping,
little/no coding Savings in long-tail maintenance costs Leverage
skills/knowledge widely available in the industry (SQL/Eclipse)
Open source community Available through JBoss subscription,
includes JBoss SOA Platform
72. 75 Why Data Services Platform cont'd Flexibility Support
for standards like JDBC, ODBC, SOAP make it easy to integrate with
existing COTS applications and IT infrastructures. Numerous
extension points available to meet varying customer needs:
Connector API Custom User-defined Functions, language extensions
Administrative API Maturity Based on MetaMatrix technology acquired
by Red Hat in 2009. Industry leader in the space Technology under
development for over 11 years. Many iterations, improvements,
refinements Deployed in demanding production environments
73. 76 Repository: Metadata and more ModeShape Data Service
metadata Rules repository SOA repository Includes: JCR Engine
RESTful service WebDAV service JDBC driver Eclipse plug-in JBoss
AS/EAP kit Sequencers JON plugin DB or file system storage
74. 77 Customer-Related Data Single customer view requires
unified view of Customers and Accounts And Services and
Transactions and Reference Data Data Volume Low High Frequency of
Change, Complexity Static Dynamic Customer Master Data Customer
Organization Customer Demographics Claims Transactions Call Center
Transactions Benchmark Data Product/ Service Catalog Market Prices
Transaction History Customer Accounts Customer Documents Text,
Image Customer Contacts Customer Data Services Reference Data 360
view of the customer relationship
75. 78 Map Data Sources to XML and Deploy Model XML Docs,
Schemas Build XML Doc. models from XML Schemas Map XML Doc. models
to other data models Enable data access via XML Designer Tooling
for XML-centric Data Services