Big Data and Data Virtualization
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Red Hat's document discusses using JBoss Data Virtualization to gain better insights from big data. It describes challenges with existing data integration approaches as data sources grow in size, type and location. Red Hat's big data strategy is to reduce the information gap by making all data easily consumable for analytics. JBoss Data Virtualization software virtually unifies data across sources and exposes it to applications through standard interfaces. The demonstration shows integrating social media sentiment data from Hadoop with sales data from MySQL to analyze movie ticket and merchandise sales.
![Red Hat is…
“By running tests and executing numerous examples for specific teams, we were able to prove […] not
only would the solution work, but it will perform better & at a fraction of the costs.”
MICHAEL BLAKE, Director, Systems & Architecture
2
RED HAT Confidential](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Big Data and Data Virtualization
- 1. GAIN BETTER INSIGHTS FROM BIG DATA USING RED HAT JBOSS DATA VIRTUALIZATION Red Hat Corporation January 5, 2014
- 2. Red Hat is… “By running tests and executing numerous examples for specific teams, we were able to prove […] not only would the solution work, but it will perform better & at a fraction of the costs.” MICHAEL BLAKE, Director, Systems & Architecture 2 RED HAT Confidential
- 3. Agenda ● Data challenges getting bigger ● Red Hat Big Data Strategy and Platform ● Data Virtualization Overview ● Customer Use Case for Big Data integration using Data Virtualization ● ● 3 Demo Q&A RED HAT Confidential
- 4. Data Driven Economy Data is becoming the new raw material of business: an economic input almost on a par with capital and labor. “Every day I wake up and ask, ‘how can I flow data better, manage data better, analyze data better?” CIO - Wal-Mart 4 RED HAT Confidential
- 5. Data Challenges Getting Bigger Big Data, Cloud, and Mobile Existing Data Integration approaches are not sufficient ● Extracting and moving data adds latency and cost ● Every project solves data access and integration in a different way ● Solutions are tightly coupled to data sources ● Poor flexibility and agility BI Reports Operational Reports Enterprise Applications SOA Applications Mobile Applications Constant Change How to align? Integration Complexity Siloed & Complex Hadoop 5 NoSQL Cloud Apps Data Warehouse & Databases Mainframe RED HAT Confidential XML, CSV & Excel Files Enterprise Apps
- 6. Business Objective Turn Data into Actionable Information Only 28% Users have any meaningful data access Reduce costs for finding and accessing highly fragmented data Over 70% BI project efforts lies in the integration of source data Improve time to market for new products and services by simplifying data access and integration Deliver IT solution agility necessary to capitalize on constantly changing market conditions Transform fragmented data into actionable information that delivers competitive advantage 6 RED HAT Confidential
- 7. Red Hat’s Big Data Strategy ● Reduce Information Gap thru cost effectively making ALL data easily consumable for analytics Process Data to Actionable Information Cycle 7 RED HAT Confidential Analytics Data Capture Integrat e
- 8. Red Hat Big Data Platform Middleware Hadoop Integration JBoss Data Virtualization Platform RHEL Platform Integration & Optimization op ado H n o ra Apache edo F Fedora Big Data SIG Hadoop Hadoop Distributions Hadoop On Red Hat Storage Storage 8 RED HAT Confidential Hadoop On OpenStack Cloud / Virtualization
- 9. Red Hat Big Data Platform Platform RHEL Platform Integration & Optimization Middleware Hadoop Integration JBoss Data Virtualization p doo Ha n o ora Apache Fed Fedora Big Data SIG Hadoop Hadoop Distributions Hadoop On Red Hat Storage Storage 9 RED HAT Confidential Hadoop On OpenStack Cloud / Virtualization
- 10. What does Data Virtualization software do? Turn Fragmented Data into Actionable Information Data Virtualization software virtually unifies data spread across various disparate sources; and makes it available to applications as a single consolidated data source. DATA CONSUMERS BI Reports The data virtualization software implements 3 steps process to bridge data sources and data consumers: ● ● ● 10 Connect: Fast access to data from diverse data sources Compose: Easily create unified virtual data models and views by combining and transforming data from multiple sources. Consume: Expose consistent information to data consumers in the right form thru standard data access methods. SOA Applications Easy, Real-time Information Access Virtual Consolidated Data Source Data Virtualization Software • • • Consume Compose Connect Oracle DW SAP Hadoop DATA SOURCES RED HAT Confidential Salesforce.com Virtualize Abstract Federate Siloed & Complex
- 11. Turn Fragmented Data into Actionable Information Mobile Applications ESB, ETL BI Reports & Analytics SOA Applications & Portals Data Consumers JBoss Data Virtu aliza tion Design Tools Standard based Data Provisioning JDBC, ODBC, SOAP, REST, OData Consume Dashboard Unified Virtual Database / Common Data Model Compose Unified Customer View Unified Product View Easy, Real-time Information Access Unified Supplier View Optimization Caching Virtualize Abstract Federate Security Connect Native Data Connectivity Metadata Data Sources Siloed & Complex Hadoop 11 NoSQL Cloud Apps Data Warehouse & Databases RED HAT Confidential Mainframe XML, CSV & Excel Files Enterprise Apps
- 12. JBoss Data Virtualization: Supported Data Sources Enterprise RDBMS: • Oracle • IBM DB2 • Microsoft SQL Server • Sybase ASE • MySQL • PostgreSQL • Ingres Enterprise EDW: • Teradata • Netezza • Greenplum 12 Hadoop: • Apache • HortonWorks • Cloudera • More coming… Office Productivity: • Microsoft Excel • Microsoft Access • Google Spreadsheets Specialty Data Sources: • ModeShape Repository • Mondrian • MetaMatrix • LDAP RED HAT Confidential NoSQL: • JBoss Data Grid • MongoDB • More coming… Enterprise & Cloud Applications: • Salesforce.com • SAP Technology Connectors: • Flat Files, XML Files, XML over HTTP • SOAP Web Services • REST Web Services • OData Services
- 13. Key New Features and Capabilities ● Data connectivity enhancements – – NoSQL (MongoDB – Tech Preview) and JBoss Data Grid – ● Hadoop Integration (Hive – Big Data), Odata support (SAP integration) Developer Productivity improvements – – Enhanced column level security, – ● New VDB Designer 8 and integration with JBoss Developer Studio v7 VDB import/reuse, and native queries Simplify deployment and packaging – – ● Requires JBoss EAP only; included with subscription Remove dependency with SOA Platform Business Dashboard – 13 New rapid data reporting/visualization capability RED HAT Confidential
- 14. ● JBoss Data Virtualization – Use Cases Self-Service Business Intelligence The virtual, reusable data model provides business-friendly representation of data, allowing the user to interact with their data without having to know the complexities of their database or where the data is stored and allowing multiple BI tools to acquire data from centralized data layer. Gain better insights from Big Data using JBoss Data Virtualization to integrate with existing information sources. 360◦ Unified View Deliver a complete view of master & transactional data in real-time. The virtual data layer serves as a unified, enterprise-wide view of business information that improves users’ ability to understand and leverage enterprise data. Agile SOA Data Services A data virtualization layer deliver the missing data services layer to SOA applications. JBoss Data Virtualization increases agility and loose coupling with virtual data stores without the need to touch underlying sources and creation of data services that encapsulate the data access logic and allowing multiple business service to acquire data from centralized data layer. Regulatory Compliance Data Virtualization layer deliver the data firewall functionality. JBoss Data Virtualization improves data quality via centralized access control, robust security infrastructure and reduction in physical copies of data thus reducing risk. Furthermore, the metadata repository catalogs enterprise data locations and the relationships between the data in various data stores, enabling transparency and visibility. 14 RED HAT Confidential
- 15. Big Data integration use case Retail Customer Use Case Gain Better Insight from Big Data for Intelligent Inventory Management ● Objective: – ● Right merchandise, at right time and price JBoss BRMS Problem: – ● Analytical Apps Data Driven Decision Management Cannot utilize social data and sentiment analysis with their inventory and purchase management system Solution: – Leverage JBoss Data Virtualization to mashup Sentiment analysis data with inventory and purchasing system data. Leveraged BRMS to optimize pricing and stocking decisions. Consume Compose Connect JBoss Data Virtualization Hive Inventory Databases 15 RED HAT Confidential Purchase Mgmt Application Sentiment Analysis
- 16. Better Together - Big Data and Data Virtualization Hadoop not another Silo - Customers Combine Multiple Technologies ● Combine structured and unstructured analysis – ● Combine high velocity and historical analysis – ● Analyze and react to data in motion; adjust models with deep historical analysis Reuse structured data for analysis – 16 Augment data warehouse with additional external sources, such as social media Experimentation and ad-hoc analysis with structured data RED HAT Confidential
- 17. Integrate & Analyze ● Better Together - Big Data and Data Virtualization Capture, Process and Integrate Data Volume, Velocity, Variety BI Analytics SOA Composite Applications (historical, operational, predictive) Capture & Process In-memory Cache JBoss Data Grid Messaging and Event Processing JBoss A-MQ and JBoss BRMS J Structured Data 17 Streaming Data RED HAT Confidential Hadoop Semi-Structured Data Red Hat Storage Red Hat Enterprise Linux & Virtualization Data Integration JBoss Data Virtualization
- 18. Consider... Inconsistent, Incomplete Information Uninformed, Delayed Decisions Costly Business Risk and Exposure How would your organization change… ● ● ● 18 If data were readily reusable in place rather than requiring significant effort to build new intermediary data tiers? If data could be repurposed quickly into new applications and business processes? If all applications and business processes could get all of the information needed in the form needed, where needed and when needed? RED HAT Confidential
- 19. ● Red Hat JBoss Middleware Business Process Management • • JBoss BRMS JBoss BPM Suite Application Integration • • • JBoss A-MQ JBoss Fuse JBoss Fuse Service Works Data Integration Foundation ACCELERATE 19 • • • • JBoss Data Virtualization JBoss EAP JBoss Web Server JBoss Data Grid INTEGRATE RED HAT Confidential AUTOMATE JBoss Operations Network JBoss Developer Studio JBoss Portal • • • Management Management Tools Tools Development Development Toolsh Toolsh User Interaction
- 20. Big Data Integration using JBoss Data Virtualization Demo
- 21. Demo Scenario ● Objective: – ● Cannot utilize social data and sentiment analysis with sales management system Consume Compose Connect Solution: – 21 Determine if sentiment data from the first week of the Iron Man 3 movie is a predictor of sales Problem: – ● Excel Powerview and DV Dashboard to analyze the aggregated data JBoss Data Virtualization Leverage JBoss Data Virtualization to mashup Sentiment analysis data with ticket and merchandise sales data on MySQL into a single view of the data. Hive SOURCE 1: Hive/Hadoop contains twitter data including sentiment RED HAT Confidential SOURCE 2: MySQL data that includes ticket and merchandise sales
- 22. Demonstration System Requirements • JDK – Oracle JDK 1.6, 1.7 or OpenJDK 1.6 or 1.7 • JBoss Data Virtualization v6 Beta – http://jboss.org/products/datavirt.html • JBoss Developer Studio – http://jboss.org/products • JBoss Integration Stack Tools (Teiid) – https://devstudio.jboss.com/updates/7.0-development/integration-stack/ • Slides, Code and References for demo – https://github.com/DataVirtualizationByExample/Mashup-with-Hive-and-MyS QL • Hortonworks Data Platform (A VM for testing Hive/Hadoop) – http://hortonworks.com/products/hdp-2/#install • Red Hat Storage – http://www.redhat.com/products/storage-server/ 22 RED HAT Confidential
- 59. Why Red Hat for Big Data? ● Transform ALL data into actionable information – Cost Effective, Comprehensive Platform – Community based Innovation – Enterprise Class Software and Support Process Integrate Data to Actionable Information Cycle 59 RED HAT Confidential Information Data Capture
- 60. ● Red Hat Big Data Platform Middleware Hadoop Integration JBoss Data Virtualization Platform RHEL Platform Integration & Optimization op ado H n o ra Apache edo F Fedora Big Data SIG Hadoop Hadoop Distributions Hadoop On Red Hat Storage Storage 60 RED HAT Confidential Hadoop On OpenStack Cloud / Virtualization
- 61. Thank You Q&A
Hinweis der Redaktion
- Today the collaboration between Red Hat and SAP continues. Engineers from both companies are working towards a common target — enhancing the interoperability of JBoss Enterprise middleware with the existing SAP landscape. Specifically, Red Hat and SAP are collaborating on development efforts for tools that are designed to simplify the integration of SAP data and business processes with other enterprise data and applications. The aim of such integration, of course, is a more intelligent enterprise — one that can maximize the value of your data assets in accelerating business decisions.
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- To remember the pragmatic definition of big data, think SPA — the three questions of big data: Store. Can you capture and store the data? Process. Can you cleanse, enrich, and analyze the data? Access. Can you retrieve, search, integrate, and visualize the data? <number>
- Easy data accessibility thru standard interfaces e.g SQL, Web Services etc. Exposes non-relational sources as relational Read and write data in place Real time access No data replication/duplication required So lets define what are the attributes of Data Virtualization solution. The first thing that data virtualization product does is virtualizes the data, regardless of where it is. It makes the data look as if it was in one place. So applications don’t need to know where the data is, because the data virtualization software does that for you. The second thing that data virtualization does is federating the data. You’re running a query which spans multiple databases or data warehouses. You want that query to run sufficiently and with optimum performance. So in order to do that, you need a variety of techniques, like caching, like pushdown optimization, you need to have knowledge of the source databases to make this whole environment run as smoothly and efficiently as possible. Thirdly, it abstracts the data into the format of choice. It conforms the data so that it’s in a consistent format, and that’s regardless of the native structure or syntax of the data. And one point I should make here is that you want to be able to – you don’t want a tool which will force you to have a particular format. What you want is a format that suits your business, rather than one which is imposed on you. So you need to have, the data virtualization tool itself needs to be agile and flexible, in the sense of being able to provide a data format that suits you. And then the fourth thing you have a requirement for is to present the data in a consistent fashion. And it doesn’t matter whether it’s a business intelligence application, it’s a mash-up, it’s a regular application; whatever it is, you want to be able to present the data in a consistent format to the business, to participating applications. Imagine if all the up-to-date data you need to take informed action, is available to you on demand as one unified source. This is the capability provided by Data Virtualization software. <number>
- Easy data accessibility thru standard interfaces e.g SQL, Web Services etc. Exposes non-relational sources as relational Read and write data in place Real time access No data replication/duplication required The data virtualization software provides 3 step process to connect data sources and data consumers: Connect: Fast Access to data from disparate systems (databases, files, services, applications, etc.) with disparate access method and storage models. Compose: Easily create reusable, unified common data model and virtual data views by combining and transforming data from multiple sources. Consume: Seamlessly exposing unified, virtual data model and views available in real-time through a variety of open standards data access methods to support different tools and applications. JBoss Data Virtualization software implements all three steps internally while isolating/hiding complexity of data access methods, transformation and data merge logic details from information consumers. This enables organization to acquire actionable, unified information when they want it and the way they want it; i.e. at the business speed.
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- To remember the pragmatic definition of big data, think SPA — the three questions of big data: Store. Can you capture and store the data? Process. Can you cleanse, enrich, and analyze the data? Access. Can you retrieve, search, integrate, and visualize the data? <number>