Modernizing Integration with Data Virtualization
0 likes248 views
The webinar discusses the benefits and implementation of data virtualization, highlighting the contributions of Fusion Alliance and Denodo in modernizing data management. Key topics include the importance of treating data as an asset, building a data strategy, and utilizing data virtualization to streamline access and improve business insights. Speakers present various services and solutions offered to organizations across multiple industries to enhance their data capabilities.
![42
Thank you!
[Article] Deep Dive on Data Virtualization Use
cases
[Get aligned] Data Virtualization Discovery
Workshop
[Explore] Fusion Data Consulting Services
[Learn more] Fusion’s Partnership with Denodo
Additional resources
Saj Patel
Vice President, Data Practice
sajid.patel@fusionalliance.com
Mike Mappes
Senior Strategic Data Management & Analytics Consultant
mmappes@fusionalliance.com
Get in touch](https://image.slidesharecdn.com/fusion-denodo-webinar-event-2022-final-v2-002869538-220831111306-eb1989a0/85/Modernizing-Integration-with-Data-Virtualization-39-320.jpg)
Modernizing Integration with Data Virtualization
- 1. Modernizing Integration with Data Virtualization Fusion Alliance & Denodo WEBINAR
- 2. Speakers 2 Keath Lewin Technology Advocate Customer Success Denodo Saj Patel Vice President, Data Practice Fusion Alliance Mike Mappes Senior Strategic Data Management & Analytics Consultant Fusion Alliance
- 3. 1. Introduction to Fusion Alliance 2. Data Virtualization Platform and Overview 3. Building the case for Data Virtualization 4. The Fusion Data Virtualization Discovery Workshop 5. Questions 6. Additional Resources Agenda 3
- 4. 4 About Fusion & The Data Practice
- 5. Fusion is your digital transformation partner We leverage data insights, experience design, and technology solutions to reimagine how you connect with your customers. 5
- 6. Who is Fusion Alliance 6 INDIANAPOLIS, IN CINCINNATI, OH COLUMBUS, OH 3 OFFICES WE’LL MEET YOU WHERE YOU ARE HEALTHCARE INSURANCE FINANCIAL RETAIL GOVERNMENT EDUCATION ENERGY SERVING NATIONAL AND GLOBAL BUSINESSES ACROSS MULTIPLE INDUSTRIES
- 7. Overview of Fusion Services 7 Technology • Technology Strategy • Application Development • API Consulting • Emerging Technologies • Software Testing Cloud • Cloud Strategy • Cloud Development • Cloud Infrastructure • Identity & Access Management • Dynamics & Infrastructure Data • Strategic Data Management • Data Integration & Architecture • BI & Analytics • AI & Machine Learning Digital • Customer Experience Consulting • Marketing Operations • Web Platform Development • Mobile App Development
- 8. 8 Where Fusion Helps with Data Management
- 9. The Future of Data Management Trending topics are causing a rethinking of what is deemed essential for data management. 9 360° CUSTOMER 360 Requires organizations to embrace ‘Data as an Asset’ and assess data capabilities broadly.
- 10. How we support your data evolution 10 Establish a big-picture data strategy and a roadmap to get there. Jumpstart your organizational capabilities with data governance, stewardship, quality, and metadata management. Strategize Evaluate and implement a modern data platform. Establish your enterprise data architecture. Rationalize the right data management technologies to meet your needs. Solution Design, develop, build, and deploy the right solutions. Deploy data integration pipelines, data platforms, BI reporting & analytics solutions, and machine learning models. Deliver
- 11. Data Practice Services 11 Information Strategy • Power Alignment Facilitation • Data Maturity Assessment • Data Strategy & Roadmap • Business & Technology Advisory Consulting Data Management Jumpstart • Data Governance Jumpstart • Data Stewardship Jumpstart • Data Catalog Jumpstart • Data Quality Enablement • Modern Data Platform Evaluation • Data Architecture Assessment • Master Data Management Assessment • Solution Architecture • Data Architecture Design • Cloud Data Platform Jumpstart • Data Integration Development Services • Data Virtualization Jumpstart BI & Analytics Jumpstart Services • Dashboard Jumpstart • Self-Service BI Jumpstart • Data Science/Advanced Analytics Enablement BI & Analytics Acceleration & Enablement • Dashboard & Report Services: Use Case Definition, Design & Development • BI Tools Rationalization • Self-Service CoE Enablement • Machine Learning – POC, Model development BI & Analytics Data Integration & Architecture Strategic Data Management “Trust Data” “Deliver Data” “Harvest Data”
- 12. Our proprietary Strategic Data Management & Analytics (SDM&A) framework to help you develop & accelerate strategies to achieve maturity across the 7 Domains of Data Management. 12 Key differentiators
- 13. 13 Strategic partners More competencies Data Partners & product ecosystem Strategic partner alliances and competencies with market leaders and market changers allow us to help you execute on your strategy and identify transformative opportunities to take your business to the next level.
- 14. 14
- 15. 17 About Denodo OUR COMPANY Data Management Leader OUR PRODUCT Leading Data Integration, Management, and Delivery Platform OUR APPROACH Logical First (Powered by Data Virtualization) OUR USE CASES Hybrid/Multi-Cloud Data Integration, Self-Service BI, Data Science, Enterprise Data Services, Data Fabric, Data Mesh
- 16. 18 Long focus in data integration, management, delivery – since 1999 Denodo: Leader in Data Management DENODO OFFICES, EMPLOYEES Global presence – 25 offices in 20 countries; 500+ employees. New offices in 2021 – Netherlands, Belgium, Sweden, South Korea. CUSTOMERS and PARTNERS 1000+ customers, including many F500 and G2000 companies across every major industry. 300+ active and engaged partners, worldwide. FINANCIALS ~50% annual growth 108% Net Retention; 4% Churn $0 debt; Profitable Leader: Gartner Magic Quadrant for Data Integration Tools, 2021 Leader: Forrester 2022 Wave – Enterprise Data Fabric, Q2 2022 Leader: Forrester 2017 Wave – Data Virtualization, Q4 2017 LEADERSHIP Customers’ Choice: 2022 Gartner Peer Insights for Data Integration Tools (2nd year in a row)
- 17. 19 ▪ Data Virtualization is a technology which abstracts data consumers from where data is located and how it is represented in the source systems. ▪ It allows building a business semantic layer on top of multiple distributed data sources of any type without the requirement of replicating data into a central repository. ▪ This semantic layer can be accessed in a secure and governed manner by consumers using a variety of access methods such as SQL, REST, OData, GraphQL or MDX. ▪ It’s the foundation for distributed and logical architectures What is Data Virtualization
- 18. 20 Denodo Platform: ONE Logical Platform for All Your Data Logically Integrate, Manage, Monitor; and Deliver Distributed Data ANY DATA SOURCE ANY DATA CONSUMER Data Governance Tools BI Dashboard Report and Tools Data Science & Machine Learning Apps Mobile & Enterprise Apps Microservices Apps DB, DW & Data Lakes Files Cloud DB & SaaS Streaming Data & IoT Cube Smart Query Acceleration AI/ML Recommendations & Automation Advanced Semantics & Active Data Catalog Unified Security & Governance Logical Data Abstraction Real-Time Data Integration ANY PLATFORM ENVIRONMENT On-Premises | Cloud | Multi-Location | Containerzed
- 19. 21 What is a Data Fabric? Data Fabric Location Customer Products Architecture design pattern that serves as an integrated layer of data over all available data assets. ▪ Continuous analytics over all metadata assets to provide actionable insights and recommendations on data management. ▪ Results in faster, more informed, and, in some cases, completely automated data access and sharing ▪ Strongly supported by both Gartner and Forrester ▪ Business centric relationships and terminology Supplier
- 20. What is Data Mesh? Distributed Ownership Paradigm proposed by the consultant Zhamak Dehghani in 2019
- 21. 23 Data Mesh Concepts Data Accessibility across Enterprise • Eliminate data silos by making data accessible in unified fashion regardless of its origin • Foster Self-Service culture by enabling all users to achieve their business goals Data Sharing Culture • Enable data sharing culture within your organization to optimize the value of the data assets • Team work and collaboration made easier with accessible data, and elimination of IT hurdles Domain Data Is Key • Business owns and drives the data needs and requirements • Domain data comes first, the Integration and Processing will follow Distributed Ownership • Flexible decentralization capable of aligning with all business needs. • Distributed compute, store, and ownership of data assets ensures rapid adoption Data as a product • Turn the data into a product to be used internally, externally, or both • Data is your most valuable asset, time to treat is as such
- 22. 24 • Lack of domain expertise in centralized data teams ▪ Centralized data teams are disconnected from the business ▪ Need to deal with data and business needs they may not understand • Lack of flexibility of centralized data repositories ▪ Data infrastructure of big organizations is very diverse and changes frequently ▪ Modern analytics needs may be too diverse to be addressed by a single platform: one size never fits all. • Slow data provisioning and response to changes ▪ Extracting, ingesting and synchronizing data in the centralized platform is costly ▪ Centralized IT becomes a bottleneck What Challenges is a Data Mesh Trying to Address?
- 23. 25 ▪ To ensure that domains do not become isolated data silos, the data exposed by the different domains must be: ▪ Easily discoverable ▪ Understandable ▪ Secured ▪ Usable by other domains ▪ The level of trust and quality of each dataset needs to be clear ▪ The processes and pipelines to generate the product (e.g. cleansing and deduplication) are internal implementation details and hidden to consumers Key Concept: Data as a Product
- 24. Enabling a Data Mesh with Data Virtualization
- 25. 27 ▪ Business guides, controls, and owns domain-centric data ▪ Virtual Data Fabric enabled decentralized architecture ▪ Data Interfaces and Unified Data Sharing Platform ▪ Enables Self-Services & Data sharing culture ▪ Scalable, adoptable, and responsive Break technology silos, while keeping data ownership at the domain level Data Mesh Concepts with Data Virtualization Data Virtualization - Logical Data Fabric - Data Share Framework Partner Data Business Domains Corporate Data External Data Data Virtualization
- 26. 28 Data Virtualization for Data Mesh: Data Product Creation With a Web-based Design Studio, abstract data sources of any format and location into a business friendly and optimized data asset for streamlined consumption and creation of data product ▪ All data assets accessible as relational models regardless of the nature of origin ▪ Metadata driven with zero data replication, unless required by the use-case ▪ Business driven semantics layer ▪ Top-down or bottom-Up approach ▪ Real-time on demand data access ▪ Robust query optimization with ▪ Caching, MPP, Remote tables ▪ Cost-based optimizations ▪ Smart Query acceleration ▪ Query push-down, and others…
- 27. 29 Data Virtualization for Data Mesh: Data Product Creation With a Web-based Design Studio, abstract data sources of any format and location into a business friendly and optimized data asset for streamlined consumption and creation of data product ▪ All data assets accessible as relational models regardless of the nature of origin ▪ Metadata driven with zero data replication, unless required by the use-case ▪ Business driven semantics layer ▪ Top-down or bottom-Up approach ▪ Real-time on demand data access ▪ Robust query optimization with ▪ Caching, MPP, Remote tables ▪ Cost-based optimizations ▪ Smart Query acceleration ▪ Query push-down, and others…
- 28. 30 Data Virtualization for Data Mesh: Data Services Enables a single point of access for all consumers, self-service, and applications to access the data assets via a business driven semantics layer ▪ Native Denodo connectors in major BI tools such as Tableau, MicroStrategy, Cognos, PowerBI, etc. ▪ Multiprotocol support including JDBC/ODBC, OData, SOAP/REST/GraphQL ▪ Human or machine consumption via XML/JSON/HTML ▪ Enables Self-Service applications and microservices ▪ Single source of truth across multiple consumers ▪ Centralized, secure, and governed access ▪ Integrated notebook for data scientist Cache DATA VIRTUALIZATION Cloud Data Lake EDW Application Database
- 29. 31 Data Virtualization for Data Mesh: Self-Service capabilities Enterprise-wide directory of data products available for consumption for business users, developers, data scientists, and data stewards ▪ Discover and document data products across your enterprise, with AI/ML driven recommendations ▪ Graphical Query & Smart Auto-complete enables quick query creation & customization ▪ Integrated Delivery layer, ensures on-demand data access with full Data Lineage ▪ Secure and audited data access ▪ Statistics on data product use ▪ Team Collaboration Features ▪ Integration with external tools ▪ Different roles for catalog access
- 30. 32 Data Virtualization for Data Mesh: Self-Service capabilities Enterprise-wide directory of data products available for consumption for business users, developers, data scientists, and data stewards ▪ Discover and document data products across your enterprise, with AI/ML driven recommendations ▪ Graphical Query & Smart Auto-complete enables quick query creation & customization ▪ Integrated Delivery layer, ensures on-demand data access with full Data Lineage ▪ Secure and audited data access ▪ Statistics on data product use ▪ Team Collaboration Features ▪ Integration with external tools ▪ Different roles for catalog access
- 31. 33 Data Virtualization for Data Mesh: Operations and Management Solution Manager enables streamlined deployment for on premise, cloud, container, or hybrid architectures which are key to a distributed ecosystem. ▪ Centralized Solution Manager provides for management and monitoring across all Denodo environments, while ensuring a secure access for various personas ▪ Designed for the hybrid deployment, it can facilitate seamless cloud migration ▪ Diagnostics & Monitoring ▪ Scalable and Secure ▪ Deployment Lifecycle ▪ Automatic AWS/Azure deployment
- 32. 34 Data Virtualization for Data Mesh: Operations and Management Solution Manager enables streamlined deployment for on premise, cloud, container, or hybrid architectures which are key to a distributed ecosystem.
- 33. 35 Conclusions • Data Mesh is a new paradigm for data management and analytics ▪ It shifts responsibilities towards domains and their data products ▪ Trying to reduce bottlenecks, improve speed, and guarantee quality • Data lakes alone fail to provide all the pieces required for this shift • Data Virtualization tools like Denodo offer a solid foundation for Data Mesh ▪ Easy learning curve so that domains can use it ▪ Can leverage domain infrastructure or direct them towards a centralize repository ▪ Simple yet advanced graphical modeling tools to define new products ▪ Full governance and security controls
- 34. August 12, 2022 Building the case for Data Virtualization Presented by Mike Mappes Senior Strategic Data Management & Analytics Consultant
- 35. 38 Value Proposition with Data Virtualization 1. Zero replication, zero relocation – No physical movement or data integration of data required to make it useful 2. Location-agnostic architecture – Hide the complexity of multi- cloud, hybrid environments 3. Data is abstracted – Data and relationships are represented logically as defined by the business rather than physically as it exists across the ecosystem. 4. Faster time to market – Direct connectivity to system-of- record data as it is produced and updated 5. Faster enablement of self-service – Access to broad range of data to support business-specific needs and workflows 6. Centralized metadata, security and governance – Integrated view of all data allowing for standardization and enforcement of core principles of access, understanding and use
- 36. Modern Data Platform – Reference Architecture 39
- 37. Approach 40 The collaborative and interactive 2-3 hour workshop, involving business and technical stakeholders, is organized around three discussion topics: Analysis & Information Gathering • Gain understanding of key business & technical factors leading to interest in data virtualization or integration platforms • Identifying constraints, limitations and pain points with current architecture Problem Statement & Recommendations • Capturing use cases for integration solutions • Understand how virtualization addresses use cases and integrates with architecture • Discuss recommendations on data virtualization and data management based on discussion findings Next Steps & Roadmap • Identify next steps for proving and showcasing data virtualization; Proof of Value, Pilot, specific use cases for value & validation • Potential roadmap for an implementation approach
- 38. 41 Questions?
- 39. 42 Thank you! [Article] Deep Dive on Data Virtualization Use cases [Get aligned] Data Virtualization Discovery Workshop [Explore] Fusion Data Consulting Services [Learn more] Fusion’s Partnership with Denodo Additional resources Saj Patel Vice President, Data Practice [email protected] Mike Mappes Senior Strategic Data Management & Analytics Consultant [email protected] Get in touch