Connecting Silos in Real Time with Data Virtualization
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The document discusses data virtualization as a solution to integrate disparate data sources in real-time. It outlines challenges with traditional data integration approaches and describes how a data abstraction layer using data virtualization can provide a single access point for all data while supporting security, governance and self-service. Key benefits include reducing data silos, faster data access, lower integration costs and enabling real-time decisions.
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Connecting Silos in Real Time with Data Virtualization
- 1. Connecting Silos in Real-time With Data Virtualization Becky Smith, Sr. Product Marketing Manager November, 2018
- 2. 1 Data Integration – “The Way We Were…” Operational Data Stores Staging Area Data Warehouse Data Marts Analytics and Reporting ETLETLETL
- 3. 2 Data Exploration Data Integration – A Modern Data Ecosystem Governance Platforms Security, Compliance & Business Continuity Information Access Actionable Insight Business Outcomes Data Integration Streaming Computing Operational and Analytical Repositories Shared Reference Information Data Sources and Data Acquisition Data Repositories Sandboxes New Insight In-Memory DB/Grid “Fit for Purpose” Data Marts EDW Event Detection and Action CRM Marketing Automation HR Finance ERP Logistics ………… Data reservoir & Refinery Discover data Parse & Refine Transform & Cleanse ODS Reports Dashboards Discovery Visualization Advanced Analytics
- 4. 3 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
- 5. 4 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
- 6. 5 Consume in business applications Combine Right information at right time 2 3 DATA CONSUMERS Enterprise Applications, Reporting, BI, Portals, ESB, Mobile, Web, Users, IoT/Streaming Data Connect Any source, any format 1 DISPARATE DATA SOURCES Databases & Warehouses, Cloud/Saas Applications, Big Data, NoSQL, Web, XML, Excel, PDF, Word... Less StructuredMore Structured Multiple protocols, formats Linked data services query, search, browse Request/Reply, event driven Secure delivery Library of wrappers Web automation Any data or content Read & Write DATA VIRTUALIZATION DATA CONSUMERSAnalytical Operational CONNECT COMBINE CONSUME Share, Deliver, Publish, Govern, Collaborate Discover, Transform, Prepare, Improve Quality, Integrate Normalized views of disparate data Agile Development Performance Resource Management Lifecycle Management Data Services Data Catalog Governance & Metadata Security & Data Privacy Denodo Data Virtualization Architecture
- 7. 6 Modern Data Architectures are much more complex than the architectures of just 10 years ago Replicating (copying) data into a central repository doesn’t work at scale or complexity needed today Data Virtualization can provide access to all of your data, in real-time, and supporting self-service with a common data model (in the context of the business users) Let’s find out how…
- 8. Logical Data Warehouse “The Logical Data Warehouse (LDW) is a new data management architecture for analytics combining the strengths of traditional repository warehouses with alternative data management and access strategy.” 7 - Gartner Hype Cycle for Enterprise Information Management, 2012
- 9. 8 Data Warehouse + Cloud Dimensional Data Time Dimension Fact table (sales) Product Dimension Customer Dimension CRM SFDC Customer EDW
- 10. 9 Multiple Data Warehouse Integration Time Dimension Sales fact Product Dimension Region Finance EDW City Marketing EDW Customer Fidelity facts Product Dimension *Real Examples: Nationwide POC, IBM tests Store
- 11. 10 Horizontal Partitioning Data Warehouse Historical Offloading Time Dimension Fact table (sales) Product Dimension Retailer Dimension Current Sales Historical Sales EDW
- 12. 11 Providing access to integrated data in real time Big Data Analytics Framework Benefits ▪ Enhanced insight across the business without physically moving data ▪ Simplified data consumption with a single endpoint for all data access ▪ Faster integration of new data sources ▪ Smarter decision making via additional information-enrichment capabilities ▪ Increased speed and agility of both business and IT, significantly increasing customer satisfaction
- 13. 12 Logical Data Warehouse at Autodesk Benefits ▪ For the first time, Autodesk can do single-point security enforcement and have uniform data environment for access. ▪ Reduced replication of data with less use of ETL processes ▪ Single point of enforcement for security ▪ Uniform environment for data access in place ▪ Development flexibility to understand what is needed to build before actually building
- 14. 13 Summary The Logical Data Warehouse (LDW) is an evolution and augmentation of DW practices, not a replacement A repository-only style data warehouse contains a single ontology/ taxonomy, whereas in the LDW a semantic layer can contain many combination of use cases, many business definitions of the same information The LDW permits an IT organization to make a large number of datasets available for analysis via query tools and applications
- 15. Query Optimization in the Logical Data Warehouse 14
- 16. 15 - Gartner, Magic Quadrant for Data Integration, 2017 The Denodo Platform ... incorporates dynamic query optimization as a key value point. This capability includes support for cost-based optimization specifically for high data volume and complexity;... it has also added an in-memory data grid with Massively Parallel Processing(MPP) architecture to its platform.
- 17. 16 Obtain Total Sales By Customer Country in the Last Two Years Query Optimization: Example (1) Naive Strategy (BI Tools, BDI Tools, Simple federation engines): join union group by Customers (3M) Sales previous years (38) Sales this year (290M) 290M rows 300M rows (sales previous year) 3M rows 593M rows through the network System Execution Time Optimization Technique No Rewriting 20 min None
- 18. 17 Obtain Total Sales By Customer Country in the Last Two Years Query Optimization: Example (2) Denodo Strategy – Aggregation push-down join union group by Customers (3M) Sales previous years (3B) Sales this year (290M) 3M rows (sales by customer this year) 3M rows (sales by customer previous year) 3M rows 9 M rows through the network group by customer group by customer System Execution Time Optimization Technique No Rewriting 20 min None Denodo 6 51 sec Aggregation push-down
- 19. 18 Obtain Total Sales By Customer Country in the Last Two Years Query Optimization: Example (3) union group by 3M rows (sales by customer this year) 3M rows (sales by customer previous year) 3M rows (customers) Aggregation pushdown group by customer group by customer join Integrated MPP processing System Execution Time Optimization Technique No Rewriting 20 min None Denodo 6 51 sec Aggregation push-down Denodo 7 13 sec Aggregation push-down + MPP integration Customers (3M)
- 20. 19 You can achieve excellent performance in Logical Analytic Architectures Key techniques needed: Advanced Dynamic Optimization to minimize network traffic and leverage the power of data sources In-memory MPP processing to speed operations at the Data Virtualization layer Advanced incremental caching for reusing commonly used data and complex calculations
- 21. Security and Governance with a Data Abstraction Layer
- 22. 21 Different Data Sources – Different Security Models Databases/EDW – Mature RBAC model Hadoop – Kerberos ▪ Cloudera – Apache Sentry and Knox ▪ Hortonworks – Apache Ranger and Atlas Cloud – OAuth 2.0 (?) Files – Binary – Read access or none Web Services – Multiple models In many cases, the consumer has to deal with these different security models and technologies
- 23. 22 Abstracting Data Source Security Provide single data model to consumers ▪ Role-based Access to data on need basis ▪ Removes data silo security Hide complexity of different security models and maturities Integrate with existing authentication system (e.g. LDAP/AD) Single point for monitoring/auditing ▪ Who, what, when, how, … Ensure compliance with corporate policies Data access and privacy rules enforced ‘on the fly’
- 24. 23 Security in a Hybrid Environment Moving data to Cloud can exacerbate security and privacy problems SaaS and Cloud data sources often have different security models Not integrated to corporate authentication mechanisms Potential for recreating authentication model in Cloud Data Virtualization abstraction layer means Cloud sources can use same security mechanism and access controls as on premise sources
- 25. 24 Customer Use Case - Asurion International Expansion - moving into different privacy and data protection jurisdictions New products – need for different data types and sources ▪ Mixing structured, multi-structured, streaming, text, video, voice, geo- location, etc. Moving to Cloud for increased speed and agility ▪ Easier to spin up new virtual servers for new data sets Competing pressures for securing data and providing access to data sets Security Constraints Geographical Constraints Contractual Client Obligations PII Protection Departmental Restrictions Fast Changing Hadoop & Cloud Technologies Hive, Spark, Redshift Maintaining different code base Discover, Co-relate, Enable Predictive Analytics Text, CSV, Voice, JSON, Streaming, 3rd Party Data 60TB+ structured, 200TB+ telemetry & unstructured data
- 26. 25 Asurion – Hybrid Architecture After implementing hybrid Data Virtualization layer, Asurion was able to: ▪ Control security across entire infrastructure from a single point ▪ Easily meet regional security and privacy requirements ▪ Keep client data separate as contractually required – but allow analytics over all (anonymized) data ▪ Perform complete audits of data access, as needed ▪ Quickly add new, compliant data sources to system
- 27. 26 Governance… Governance features are pervasive in Denodo Platform: ▪ Users can inspect catalog of virtualization objects through catalog search to find data combinations for reuse ▪ Data lineage helps users to understand where data has come from and how it has changed from the source ▪ Impact analysis helps architects understand the consequences of changes in the data source schemas ▪ Propagate changes selectively with a single click.
- 28. 27 Data Lineage Graphical view for showing data lineage for any field in any virtual view. Trace source of any field: • Includes any functions applied to field contents. Trace source of calculated fields: • View calculations used to create new fields.
- 29. 28 “Used by” “Big picture” view of usage Useful for seeing impact of changes on whole system
- 30. 29 Single point for security and governance Extends single point of control across Cloud and on premise architectures catalog search helps users find data combinations for reuse Data lineage helps users to understand where data has come from and how it has changed from the source Impact analysis helps architects understand the consequences of change
- 32. 31 Self-Service Challenges……. Tools are designed for data analysts (or power users) ▪ Users who are happy finding, wrangling, cleansing data ▪ Creating calculations, aggregations within the data What about the other business users? ▪ People who don’t want to spend hours fighting the spreadsheet… Will they use common definitions for key business entities and metrics? ▪ Or will they pick and choose their own? Ultimately, can you trust the numbers? ▪ Where did the data come from? ▪ How has is been manipulated? 31
- 33. 32 Self-Service with Guardrails Don’t build just for the ‘data cowboys’ Create a common and consistent semantic layer ▪ Everyone is using the same definitions and metrics Create pre-integrated, pre-calculated data services ▪ Save the user having to do this themselves ▪ Ensures consistency of calculations, etc. But allow the cowboys to ‘roam and wrangle’ ▪ Even the cowboys can only access ‘approved’ data sources
- 35. 34 Logical Data Warehouse Improves Information Agility Benefits ▪ Diverse data spread across the entire enterprise can now be accessed instantaneously and securely with a proper authorization structure ▪ Core Business Intelligence logic is becoming centralized, reducing duplication of effort and enhancing development efficiency ▪ Searchable data dictionary helps report writers find the data they need and help improve the self- service experience
- 36. 35 “Get it Real-time and Get it Fast!” The Benefits of Data Virtualization Complete enterprise information, combining Web, cloud, streaming, and structured data ROI realization within 6 months, with the flexibility to adjust to unforeseen changes An 80% reduction in integration costs, in terms of resources and technology Real-time integration and data access, enabling faster business decisions
- 37. 36 Denodo The Leader in Data Virtualization DENODO OFFICES, CUSTOMERS, PARTNERS Palo Alto, CA. Global presence throughout North America, EMEA, APAC, and Latin America LEADERSHIP ▪ Longest continuous focus on data virtualization – since 1999 ▪ Leader in 2018 Forrester Wave – Big Data Fabric ▪ Winner of numerous awards CUSTOMERS ~500 customers, including many F500 and G2000 companies across every major industry have gained significant business agility and ROI DENODO AUSTRALIA L13 – Macquarie House, 167 Macquarie Street NSW Sydney 2000