Red Hat JBoss Data Services Platform: Overview & Roadmap. Ken Johnson Director, Product Management Red Hat June 12, 2013

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2 Red Hat JBoss Data Services Platform: Overview & Roadmap Ken Johnson Director, Product Management Red Hat June 12, 2013

3 Agenda Data Virtualization What it is How can you benefit JBoss Data Services Product Overview Demo Roadmap/Future Direction Wrap-up, Q&A

4 Data Virtualization: Overview

5 Data Virtualization Overview Data Virtualization Process Accessing data from disparate systems (databases, files, services, applications, etc.) Integrating and transforming the data, creating canonical abstraction Exposing information as a single-source through a variety of data access methods to support different tools and applications access is in real-time from original sources Hide transformation and connectivity details from information consumers

6 Data Virtualization Simplistic View Data Virtualization

7 Data Virtualization What it is Not Replication/ETL Hard Coded Time consuming difficult/costly Data not fresh No re-use of data logic Costly additional licenses Any changes break the application More copies of data = more silos

8 Data Virtualization Benefits Why Data Virtualization? Quickly derive value from your data assets Present it to your users in the form they want through the tools they use. Build a flexible, agile information infrastructure that lets you quickly respond to change Complements existing technologies and approaches (SOA, ETL, EAI) Gain control of your information sprawl Data Virtualization

9 Data Virtualization Common Use Cases Architectural/Technical Business Driven Data Layer/Data Hub Customer service, Single view SOA/Data Services Compliance, governance Analytics, Business Intelligence Mergers and Acquisitions Mobile enablement Operational Dashboards Virtual data mart/data warehouse extension MDM augmentation Cloud data integration

10 Red Hat JBoss Data Services

11 Red Hat JBoss Data Services Platform

12 Usage pattern: Connect and Compose Connect to sources, integrate, federate, transform data Create foundational data views services warehouses Integrated set of standard data views databases Finance, HR spreadsheets <sale/> <value/> </ sale > xml files Inventory, Logistics applications Existing sources and silos of data

13 Usage pattern: Compose and Consume Provide consistent information in the form required by different information-consuming BI/analysis applications, processes, services. Ensure complete and consistent information through all delivery modes/formats. Business Process Forms: (contract) Packaged Apps Access Modes: JDBC, ODBC SOAP, REST Services POJO XML over HTTP Custom Apps Reports, Dashboards T SE R/ P A OS Analytics O/ CBDJ gni ppa M R/ O Relational Tables/Views Star schema Procedures Schema-compliant XML <WSDL> <WSDL> (contract) <WSDL> (contract) Finance, HR Inventory, Logistics

14 Red Hat JBoss Data Services Platform Turn the data you have into the information you want. Standards-based read/write access to heterogeneous data stores in real time. Speeds application development by simplifying access to distributed data. Transforms data structure and semantics through data virtualization. Consolidates data into a single view without the need for more data. Centralized access control, auditing through robust security infrastructure. Creates services that provision data to business process in your SOA. Enterprise-proven flexible, scalable, high-performance. SQL Data View Web Services Data Service Data Service JBoss Enterprise Data Services

15 System view and work flow Tooling VDB Engine Server

16 System view and work flow - Tooling Tooling VDB Engine Server Users create data models based on metadata: Imported from data sources Supplied via DDL Provided by Engine Specified by user Models are packaged in a Virtual Database (VDB)

17 System view and work flow - VDB Tooling VDB Engine Server Virtual Databases (VDBs) are deployment archives similar to.war. VDBs contain Source metadata and models View metadata and models System metadata Connection information, which is bound to sources at deployment time VDBs are deployed to the query engine (Teiid).

18 System view and work flow Tooling Teiid Query Engine is core data virtualization functionality: Federating relational query engine. Rule and cost based optimizer, advanced query planner, caching, hint processing. Query Engine hosts VDBs, binds to data sources, performs query execution and results processing. VDB Engine Server

19 System view and work flow Tooling VDB The server runtime environment is JBoss EAP. The Teiid Query engine is hosted in EAP and uses key container-provided services: Transaction manager JAAS security framework Container managed data sources EAP management infrastructure EAP deployment The Server: exposes views/services to consumers and managed connections and connection pools for data sources. Engine Server

20 Caching/Materialization for Performance Multiple levels of caching to meet performance requirements and manage load on source systems. Fully automatic to fully user controlled.

21 Repository/Governance S-RAMP: SOA Repository Artifact Model and Protocol OASIS specification that defines: a common data model for repositories, as well as an interaction protocol to facilitate the use of common tooling and sharing data. S-RAMP repository capabilities: Store and retrieve content and metadata Classification of artifacts (e.g. XSD, WSDL, VDB,...) ATOM API

22 Repository/Governance Core Models storing standard metadata and the file content itself ATOM BINDING (REST) Core Model Documents Derived Models are read only and data is derived from the content Derived Models (Read Only) XPath2 based QL Content extractors/derivers Clients interact via: JCR Storage (Modeshape + Infinispan) ATOM/REST Java via S-RAMP client S-RAMP browser (web-based) Maven VDB (query/read)

23 Demo

24 Demo Scenario Text with no bullets Bullets layer one Bullets layer two Bullets layer three

25 Product Roadmap and Future Direction

26 Data Services Roadmap: JBoss DS Platform v5 -> JBoss DS Platform v6 Current: DS-P v5 JBoss DS Platform v5 Next: DS-P v6 Managed JBoss DS Platform v6 JDBC/ODBC Dev Studio + SOA + Data tools Data Virt/Teiid BPEL Rules JBoss ESB Repos jbpm3 UDDI JON + EDS Plugins + SOA Plugins Dev Studio + Data Tools JDBC/ODBC Data Virt/Teiid Repos EAP v6 EAP v5 Simplify packaging: bundle with EAP rather than SOA EAP 5 base --> EAP 6 base Honor customer investment, provide SOA-P to existing users Timing of EDS-P v6: Alpha: July 2013 Beta: Aug 2013 GA: Q Managed JON + EDS Plugins

27 Select Upcoming New Features DDL Based View Definitions Procedure Exception Handling VDB import/reuse NoSQL sources VDB deployment updates OBJECTTABLE Native Queries JSON Production OData Support Column masking Source temp tables Teiid Designer 8.x Source security domains Dashboard Builder You can access these features in Teiid community releases

28 DDL-based Dynamic View Creation Unique among data virtualization technologies/vendors Programmatic configuration In Teiid 7.7/EDS 5.x, DDL could only specify integration between multiple physical sources. In Teiid 8/EDS 6, DDL can be used to specify view definitions.

29 DDL based Dynamic View Creation <?xml version="1.0" encoding="utf 8" standalone="yes"?> <vdb name="twitter" version="1"> <model name="twitter"> <source name="twitter" translator name="ws" connection jndi name="java:/twitterds"/> </model> <model name="twitterview" type="virtual"> <metadata type="ddl"><![cdata[ CREATE VIRTUAL PROCEDURE gettweets(query varchar) RETURNS (created_on varchar(25), from_user varchar(25), source varchar(25), text varchar(140)) AS select tweet.* from (call twitter.invokehttp(action => 'GET', endpoint =>querystring('',query as "q"))) w, XMLTABLE('results' passing JSONTOXML('myxml', w.result) columns created_on string PATH 'created_at', from_user string PATH 'from_user', source string PATH 'source', text string PATH 'text') tweet; CREATE VIEW Tweet AS select * FROM twitterview.gettweets; ]]> </metadata> </model> </vdb>

30 DDL Support Creation of Views Creation of Virtual Procedures Creation of Functions Like SQL/MED, but more feature rich

31 VDB Import/Reuse Feature RDBMS VDB 1 combined VDB1 VDB2 Flat File Flat File VDB2 Services Client Application

32 VDB Import/Reuse RDBMS Flat File Flat File VDB 1 combined VDB2 Services Client Application

33 VDB Imports/Reuse Enables and promotes enterprise wide canonical data model Hides the complexity from application developers Improves performance Treats it as single source Materialized/cached data from individual VDBs is shared with others in combined VDB

34 Native Query Support Allow pre-defined or canned queries to be passed to sources directly. Take advantage of source-specific behavior. Two Forms Source Table metadata Native() procedure

35 Native Query Support Source Table Metadata Specified as metadata property on source table: native query="select c from g" Produces source query with inline view: "SELECT c FROM (select c from g) as x" Native() Procedure Allow ad-hoc native SQL to be passed to source. Metadata structure defined at execution. SELECT x.* FROM (call pm1.native('select * from g1')) w, ARRAYTABLE(w.tuple COLUMNS "e1" integer, "e2" string) AS x

36 OData Support OData (OASIS Open Data Protocol) open.org/committees/tc_home.php?wg_abbrev=odata Objective: OASIS OData TC works to simplify the querying and sharing of data across disparate applications and multiple stakeholders for re-use in the enterprise, Cloud, and mobile devices. A REST-based protocol, OData builds on HTTP, AtomPub, and JSON using URIs to address and access data feed resources. It enables information to be accessed from a variety of sources including (but not limited to) relational databases, file systems, content management systems, and traditional Web sites. Data Services v6 supports Odata in two ways: Connect to and access Odata sources Act as an Odata server to client applications

37 Dashboard Builder New component/application from the Polymita acquisition Generalized beyond BPM and BAM use cases Planned for inclusion in Data Services v6 Provides the ability to create dashboards for visualizing data

38 Dashboard Builder

39 Product Interoperability

40 Data Services and BRMS: Reasoning: with Business Rules Capture and codify key decision-making logic in business rules Facilitate collaboration between business analysts and IT Process information actively Rule sets possibilities: Pricing Fraud detection Regulatory compliance Productivity/Efficiency Control systems Product configuration... Insurance Rules: Age Sex Health Occupation Lifestyle = $ Price

41 Data Services and BRMS: Reasoning: with Business Rules Expose virtual data views through JDBC and Hibernate. Rule engine accesses Hibernate session to retrieve records and turn them into facts over which the engine can reason. Facts Results CBDJ et a nr e bi H Finance, HR Inventory, Logistics

42 Data Services and JBoss Data Grid: Distributed cache as a data source Treat JBoss Data Grid as a data source Combine that with other data using standard product principles services warehouses Integrated set of standard data views databases Inventory, Customers JBoss Data Grid applications Existing sources and silos of data

43 Wrap-up

44 Red Hat JBoss Data Services Business Value Increased ROA Improved utilization of data assets Derive more value from existing investments Complements existing systems Greater agility, faster time to solution Better/faster than hand coding Faster, less costly than batch data movement Data virtualization provides loose coupling Improved organizational performance Right data at the right time to the right people Decision support, BI with a complete view of information Better control of information Powerful security, Auditing, Data Firewall Avoid data silo proliferation Central data access and policy, Compliance

45 Red Hat JBoss Middleware MIDDLEWARE User Interaction JBoss Portal Business Process Management JBoss BRMS Application Integration JBoss Fuse JBoss A-MQ Jboss SOA Data Virtualization JBoss Data Services Foundation JBoss EAP JBoss Web Server JBoss Data Grid Accelerate JBoss Operations Network Management Tools JBoss Developer Studio Development Tools Integrate Automate

46 Related Sessions Wednesday 10:40 - Mainframe & Database Legacy Modernization 3:40 - Red Hat Storage & Big Data 4:50 - Get Mobile-ready Fast with Red Hat JBoss Data Services Platform 4:50 - Town Hall Discussion: Big Data & Traditional Databases Thursday 10:40 - When Big Data Goes Beyond Hadoop 3:40 - Hadoop on Red Hat Storage Server: A Reference Architecture Friday 9:45 - Interoperability Results from SAP & Red Hat Collaboration 9:45 - Design Business Intelligence Appliances Using Red Hat Technologies

47 Q&A

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