Getting Started with Data Virtualization – What problems DV solves
0 likes1,206 views
The document discusses data virtualization (DV) as a solution to common IT architecture challenges such as data silos and high latency due to increasing data complexity and new data trends. It emphasizes the benefits of DV, including real-time data access, reduced storage needs, minimized data replication, and improved governance and security. Additionally, it highlights industry trends and the extensive connectivity of DV to various data types and platforms, positioning it as a vital tool for enterprises looking to enhance data integration and analysis capabilities.
Getting Started with Data Virtualization – What problems DV solves
- 1. Getting Started with Data Virtualization – What problems DV solves Richard Walker, VP of Enterprise Sales, Denodo
- 2. 2 Problem: IT Architecture is Unmanageable Traditional Issues Hi-Data Growth, IT Complexity, Data Silos, Hi - Latency New Trends Real Time, Big Data, Unstructured Data, External Data, Move to Cloud Log files (.txt/.log files) CRM (MySQL) Billing System (Web Service - Rest) Big Data, Cloud (Hadoop, Web) Inventory System (MS SQL Server) Product Catalog (Web Service -SOAP) Customer Voice (Internet, Unstruc) Product Data (CSV) ETL
- 3. 3 Solution: Data Virtualization (DV) = Middleware + DB Technology Bus. Models - DV is the set of rules and mappings that converts Raw Data into Business Information and Links Info together. Optimized Query Process - Queries are done in real time so that data is always current & eliminates data replication. Ent. Governance & Security - Data is filtered based on roles & privileges.
- 4. 4 4 Key Industry Trends Guides Denodo • Discovery and self- service BI • Access to data and metadata in business terms • Canonical data models • BigData • New data types • New sources: Hadoop, Spark, etc. • In addition to increased agility for traditional BI, needs, etc. • SaaS • Data Distribution • Multiple delivery styles, formats • REST Services • Logical data abstraction layer • Lineage and change impact • Metadata and data services sharing API • Data services catalog V6.0
- 5. 5 Unlimited Connectivity to Any Data Type Relational DB’s: Oracle, DB2, Sybase, MS SQL Server, MySQL, PostgreSQL, Informix, MS Access… Parallel DB’s & Appliances: Teradata, Netezza, Oracle Exadata, Sybase IQ, Greenplum, ParAccel… Multidimensional OLAP Engines: SAP BW, MS SQL Server Analysis Services, Mondrian, Essbase… SOAP / REST Web Services and Data Feeds: XML, RSS, ATOM, JSON, Odata, Delimited Files – CSV, log files, device feeds, ... Enterprise Applications: SAP R3 / ECC, Oracle E-Business suite,, Siebel, PeopleSoft, SAS... Content Management Sys (CMS): MS SharePoint, IBM FileNet, Documentum… Modeling Tools: Erwin, Rochade, ER Studio… MDM & Mapping: IBM Initiate, ontologies, taxonomies… Mainframe / Legacy Connectivity: Adabas, IMS, DB2, TN5250 / TN3270. Plug-in architecture: third party Mainframe / Legacy Adapters... Semantic repositories in Triple Stores / RDF accessed via SPARQL endpoints LDAP and Active Directory: as source data & security access Big Data / NoSQL: Hadoop, Hive, HCatalog, Impala, Scoop, HBase, PIG, HDFS, MapReduce, AVRO, HDFS, Mongo DB, CouchDB, Neo4J, Cassandra, MarkLogic… Cloud, SaaS: Salesforce, Google, Amazon, LinkedIn, Facebook, Twitter via APIs; Any Website, Form, any Web based Apps… Enterprise Service Bus: JMS message queues, WebSphere MQ, Sonic, ActiveMQ… Custom Connector SDK: access any application via API and procedural interfaces. Semi-Structured Data: Web sites, Forms, applications, PDF, MS Word, MS Excel Unstructured Data: websites, file systems, Email servers, databases, knowledgebase, indexes (Lucene, MS FAST, HP Autonomy…), RSS Feeds …
- 6. 6 Unified Data Layer in the 3 tier Architecture User interface Business logic Data access/data infrastructure SOA-based services Data Virtualization SOA/ESB On-premise Cloud External Transaction Data Analytics Logical Business Views Pervasive user interactions Web Mobile Pervasive Web
- 7. 7 Complementary Data Integration Architectures On Demand - DB to App Data Federation from multiple sources Event Based - App to App Message-based, transaction processing Scheduled – DB to DB Bulk data integration DV EAI/ESB ETL DV DV Target/ Data WH ESB ESB
- 8. 8 DV Platform: Business Drivers Accelerate Project Timelines Abstraction Layer: De-couple Applications from Enterprise Data – Protect daily operations Deploy Faster Use Less Resources Unified Access to disparate data sources Minimize data replication, less storage, no ETL Reuse Canonical Model for multiple projects Easy Big Data/NoSQL data integration with enterprise data sources Operational ‘Real-time’ Reporting 360 Degree View of all business data entities Offer secure business views filtering data by role and privileges Real time data access, read & write Support for operational applications Business Controlled Self Service & Data Discovery Ad hoc query, browse, and search for Business Users in a secure way Data presented in a ‘business friendly’ informational format Drill down through all business associations via links & subscriptions
- 9. 9 Denodo ROI Customer Reported Project Savings by Percentage • Data Integration Cost Reduction • 60% - 67% Savings • Traditional, Call Centers, Portals • 30-70% Savings • BI & Reporting • 40-60% Savings • ETL & DW • Project timelines reduced from 6-12 months to 3- 6 weeks • 85% Time Reduction
- 10. Thanks! www.denodo.com [email protected] © 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.