JBoss Enterprise Data Services (Data Virtualization)

1
JBoss Enterprise Data Services
Peter Larsen
JBoss Solutions Architect, Red Hat Inc.
plarsen@redhat.com

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
The Business Inflexibility Trap
Inflexibility: The
essential Business
Problem
With agility all
problems are
solvable
“With enough eyes,
all bugs are shallow”

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
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
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
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
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
Integration Technologies
ETL
SOA
EDS
Data ProcessIntegration Style
DataIntegrationTimeliness
Real Time
Batch

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

12
JDBC/ODBC
Query Engine
Data Virtualization, Federation
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

14
Data Services Platform Architecture

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

16
Designer Tooling
Virtual Models
Physical Models representing
actual data sources
• Shows structural
transformations
• Defines
transformations
with
– Selects
– Joins
– Criteria
– Functions
– Unions
– User Defined

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
<a>
</a>
<b>
</b>
…
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

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

20
• 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.
Workflow Rules
JBossESB
Transformation, Routing, Registry
Container services, Hibernate, Web Services stack, Seam, Clustering, Cache,
Messaging, Transactions

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

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

24
A Comprehensive Middleware Portfolio
JBoss Enterprise
Data Services Platform
JBoss Enterprise
SOA Platform
JBoss Enterprise Web Platform
JBoss Enterprise Web Server
Red Hat Enterprise Messaging
JBoss Enterprise Portal Platform
JBoss Enterprise
Business Rules
Management System
JBoss Developer
Studio
Seam
Hibernate
Web Framework
Kit
JBoss
Operations
Network
Red Hat Services
Cloud Implementation Cloud GovernanceCloud Strategy &
Selection
VMWare
Microsoft
Hyper-V
Red Hat Enterprise
Virtualization
PrivatePublic
Amazon EC2
Other
RHEL, Unix, Windows

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

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.

27
Community Version
• Community web site: www.jboss.org/teiid
• Teiid sub-projects: Teiid Runtime, Teiid Designer
• Teiid 7 is built for AS7

28
JBoss Enterprise SOA Platform (SOA-P) and
Enterprise Data Services (EDS) Roadmap
Q4 08 Q1 09 Q2 09 Q3 09
2.8
SOA-P 5.0
Q4 09
3.0
Q1 10
Q4'09 Q1'10 Q2'10 Q3'10 Q4'10 Q1'11 Q2'11
Enterprise
Q3'11 Q4'11 Q1'12
Platform Release
March 2010
Calendar Quarters
Platform Release
January 2011 (target)
SOA-P 5.1
Platform Release
January 2011 (target)
SOA-P/EDS 5.2
EDS 5.1
Platform Releases
Mid-Q2 2011 (target)
MetaMatrix 5.5.4
Platform Release
November 2009

29
Generating New Value with
and Enterprise Data Services

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

31
Common Challenges
 Data
 Decision making
 Inflexible systems
 Manual processes

32
Common Challenges
 Data
 Data sprawl
 Tied up in silos
 Not reconciled/integrated
 Not easily usable
 Decision making

33
Common Challenges
 Data
 Decision making
 Insufficiently informed
 Missing key information
 Stale or out-of-context information

34
Common Challenges
 Data
 Decision making
 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

35
Common Challenges
 Data
 Decision making
 Business processes are manual
 Data entry, swivel-chair integration
 Overly dependent on individuals
 Inconsistent, prone to error, difficult to govern

36
Common Challenges
 Data
 Decision making
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?

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

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/>
<value/>
</ 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

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
<WSDL><WSDL>
(contract)
<WSDL><WSDL>
(contract)
<WSDL><WSDL>
(contract)
Custom Apps
Business Processes
Packaged Apps
Reports, Dashboards
Data warehouses
O/RMappingJDBC/OSOAP/JMS
CRM, Employee
SupplyChain,
Logistics

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

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/>
<value/>
</ sale >

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

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

44
How technologies map to patterns
JDBC/ODBC
Data Virtualization
Data Access, Federation
Metadata
Repository
Repository Services
Workflow Rules
JBossESB
Container services, Hibernate, Web Services stack, Seam, Clustering,
Cache, Messaging, Transactions
1. Data Foundation
2. Information Delivery
3. Externalize Knowledge
4. Automate Decision Making
5. Codify Business Processes

45
JDBC/ODBC
Data Virtualization
Metadata
Repository
Repository Services
Workflow Rules
JBossESB
1. Data Foundation

46
JDBC/ODBC
Data Virtualization
Metadata
Repository
Repository Services
Workflow Rules
JBossESB
1. Data Foundation

47
JDBC/ODBC
Data Virtualization
Metadata
Repository
Repository Services
Workflow Rules
JBossESB
1. Data Foundation

48
JDBC/ODBC
Data Virtualization
Metadata
Repository
Repository Services
Workflow Rules
JBossESB
1. Data Foundation

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

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

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.

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

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
Dashboard
Data Sources
Data Service
One of many projects – part of “data layer”One of many projects – part of “data layer”One of many projects – part of “data layer”One of many projects – part of “data layer”

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
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

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.
WebFocus Portal
Data Sources
Data Service
““Data Firewall” to protect and govern use of dataData Firewall” to protect and govern use of data““Data Firewall” to protect and govern use of dataData Firewall” to protect and govern use of data

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
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

60
CFDB
CSDS DMDC
GSORTS
IDE/AV
NGA
FLIS
CSDS_PL
CSDS_VBL
Facilities_VML
Material_VML
Facilities_VQL
Material_VQL
GDSS
Plans_VQL
PrivateDataandMetadataPublicData
Virtual Mid Layer
(VML)
Virtual Query Layer
(VQL)
(Exposed Views)
JOPES
Classic
JOPES
4.0
Virtual
Base Layer
(VBL)
Physical Layer
(PL)
GTN
Structured Data Services
Plans_VML

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
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

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
BI tools, Portal,
Federated Search
Data Service
Disparate, heterogenous
State/Local databases

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
Portal, ESB,
Federated Search
Data Service
Disparate, heterogenous data
sources with varying
schemas/representations

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
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

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

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.

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.

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

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.

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

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

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

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

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

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

JBoss Enterprise Data Services (Data Virtualization)

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (13)

Similar to JBoss Enterprise Data Services (Data Virtualization)

Similar to JBoss Enterprise Data Services (Data Virtualization) (20)

More from plarsen67

More from plarsen67 (16)

Recently uploaded

Recently uploaded (20)

JBoss Enterprise Data Services (Data Virtualization)

Editor's Notes