Data Virtualization: An Introduction
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Watch Paul's session from Fast Data Strategy on-demand here: https://goo.gl/3veKqw "Through 2020, 50% of enterprises will implement some form of data virtualization as one enterprise production option for data integration" according to Gartner. It is clear that data virtualization has become a driving force for companies to implement an agile, real-time and flexible enterprise data architecture. Attend this session to learn: • What data virtualization actually means and how it differs from traditional data integration approaches • The most important use cases and key patterns of data virtualization • The benefits of data virtualization
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Data Virtualization: An Introduction
- 2. Data Virtualization – An Introduction Paul Moxon VP, Data Architecture and Chief Evangelist, Denodo
- 3. AgendaAgenda 1. Data Virtualization: An Introduction 2. Data Virtualization Platforms – Key Capabilities 3. Next Steps
- 4. Data Virtualization – An Introduction
- 5. 5 Data Integration – “The Way We Were…” Operational Data Stores Staging Area Data Warehouse Data Marts Analytics and Reporting ETLETLETL
- 6. 6 Data Integration – A Modern Data Ecosystem
- 7. 7 The Data Integration Challenge Manually access different systems IT responds with point-to- point data integration Takes too long to get answers to business users MarketingSales ExecutiveSupport Database Apps Warehouse Cloud Big Data Documents AppsNo SQL “Data bottlenecks create business bottlenecks.” – Create a Road Map For A Real-time, Agile, Self-Service Data Platform, Forrester Research, Dec 16, 2015
- 8. 8 The Solution – A Data Abstraction Layer Abstracts access to disparate data sources Acts as a single repository (virtual) Makes data available in real-time to consumers DATA ABSTRACTION LAYER “Enterprise architects must revise their data architecture to meet the demand for fast data.” – Create a Road Map For A Real-time, Agile, Self-Service Data Platform, Forrester Research, Dec 16, 2015
- 9. 9 Data Virtualization 9 “Data virtualization integrates disparate data sources in real time or near-real time to meet demands for analytics and transactional data.” – Create a Road Map For A Real-time, Agile, Self-Service Data Platform, Forrester Research, Dec 16, 2015 Publishes the data to applications Combines related data into views Connects to disparate data sources 2 3 1
- 10. 10 Data Virtualization Reference Architecture
- 11. Source: “Gartner Market Guide for data virtualization – 2016” Data virtualization technology can be used to create virtualized and integrated views of data in memory (rather than executing data movement and physically storing integrated views in a target data structure), and provides a layer of abstraction above the physical implementation of data.
- 12. 12 What Data Virtualization is Not! • It is not ETL ▪ If you want to replicate data from ‘A’ to ‘B’…use an ETL tool – it’s what they are designed for • It is not Data Visualization ( Note the ‘s’) ▪ It complements visualization and reporting tools (e.g. Tableau) • It is not a database ▪ Data Virtualization Platforms don’t store the data…it’s retrieved from the data sources on demand • It has many capabilities such as governance, metadata management, security, etc. ▪ It will work with specialized tools in these areas • It’s great for service-based architectures ▪ But be wary of event-driven architectures…use an ESB (or similar) for this
- 13. Data Virtualization Platforms – Key Capabilities
- 14. 14 Data Virtualization – Five Essential Capabilities 4. Self-service data services 5. Centralized metadata, security & governance 1. Data abstraction 2. Zero replication, zero relocation 3. Real-time information
- 15. 15 1. Data Abstraction …hides data complexity for ease of data access by business Abstracts access to disparate data sources. Acts as a single virtual repository. Abstracts data complexities like location, format, protocols Enterprise architects must revise their data architecture to meet the demand for fast data.” – Create a Road Map For A Real-time, Agile, Self- Service Data Platform, Forrester Research
- 16. 16 2. Zero Replication, Zero Relocation …reduces development time and overall TCO The Denodo Platform enables us to build and deliver data services, to our internal and external consumers, within a day instead of the 1 – 2 weeks it would take with ETL.” – Manager, DrillingInfo Leaves the data at its source; extracts only what is needed, on demand. Diminishes the need for effort-intensive ETL processes. Eliminates unnecessary data redundancy.
- 17. 17 Real Time Information …enables timely decision-making Provisions data in real-time to consumers Creates real-time logical views of data across many data sources. Supports transformations and quality functions without the latency, redundancy, and rigidity of legacy approaches Data virtualization integrates disparate data sources in real time or near-real time to meet demands for analytics and transactional data.” – Create a Road Map For A Real-time, Agile, Self-Service Data Platform, Forrester Research, Dec 16, 2015
- 18. 18 Self-Service Data Services Facilitates access to all data, both internal and external Enables creation of universal semantic models reflecting business taxonomy Connects data silos to provide best available information to drive business decisions Impressively quick turn around time to "unlock“ data from additional siloes and from legacy systems - Few vendors (if any) can compete with Denodo's support of the RESTful /OData standard - both to provide data (northbound) and to access data from the sources (southbound).” – Business Analyst, Swiss Re
- 19. 19 Centralized Metadata, Security & Governance …simplifies data security, privacy, audit Abstracts data source security models and enables single-point security and governance. Extends single-point control across cloud and on- premises architectures Provides multiple forms of metadata (technical, business, operational) to facilitate understanding of data. Our Denodo rollout was one of the easiest and most successful rollouts of critical enterprise software I have seen. It was successful in handling our initial, security, use case immediately, and has since shown a strong ability to cover additional use cases, in particular acting as a Data Abstraction Layer via it's web service functionality.” – Enterprise Architect, Asurion
- 20. 20 Denodo Solution Categories Customer Centricity / MDM ✓ Complete View of Customer Data Services ✓ Data as a Service ✓ Data Marketplace ✓ Data Services ✓ Application and Data Migration Cloud Solutions ✓ Cloud Modernization ✓ Cloud Analytics ✓ Hybrid Data Fabric Data Governance ✓ GRC ✓ GDPR ✓ Data Privacy / Masking BI and Analytics ✓ Self-Service Analytics ✓ Logical Data Warehouse ✓ Enterprise Data Fabric Big Data ✓ Logical Data Lake ✓ Data Warehouse Offloading ✓ IoT Analytics
- 21. 21 Summary • Data Virtualization offers an alternative approach to data integration and provisioning ▪ Very agile, iterative approach – involve business stakeholders at beginning and deliver quickly ▪ Does not use expensive bulk data loads for data consolidation and centralization ▪ Data is delivered on-demand, in real-time • Five key capabilities of a Data Virtualization Platform ▪ Data abstraction, Zero (minimal) replication, Real-time information, Self-service information, Centralized metadata, security, and governance • Wide range of use cases ▪ From Analytics and Big Data to Cloud to Governance and Data Services
- 22. Q&AQ&A
- 23. 23 DOWNLOAD DENODO EXPRESS DENODO FOR AWS DENODO FOR AZURE Download Denodo Express Next Steps Access Denodo Platform in the cloud! 30 day free trial available!
- 24. Thank you! © Copyright Denodo Technologies. All rights reserved Unless otherwise specified, no part of this PDF file may be reproduced or utilized in any for or by any means, electronic or mechanical, including photocopying and microfilm, without prior the written authorization from Denodo Technologies. #FastDataStrategy