DATA VIRTUALIZATION PACKED LUNCH WEBINAR SERIES

Sessions Covering Key Data Integration Challenges Solved with Data Virtualization

Data Virtualization: An Introduction

Paul MoxonVP Data Architectures & Chief Evangelist, Denodo

Agenda1. Data Virtualization: An Introduction

2. Denodo’s ‘Packed Lunch’ Webinar Series

3. Q&A

4. Next Steps

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Data Virtualization: An Introduction

Data Integration – “The Way We Were…”

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OperationalData Stores

Staging Area Data Warehouse Data Marts Analytics andReporting

ETLETLETL

Data Integration – A Modern Data Ecosystem

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The Data Integration Challenge

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Manually access different systems

IT responds with point-to-point data integration

Takes too long to get answers to business users

MarketingSales ExecutiveSupport

Database

AppsWarehouse 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

The Solution – A Data Abstraction Layer

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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

Data Virtualization

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“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

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Data Virtualization Reference Architecture

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Confluence of Ecosystems

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Common Data Virtualization Use Cases

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− Gartner, Predicts 2017: Data Distribution and Complexity Drive Information Infrastructure Modernization, Ted Friedman et al.

By 2018, organizations with data virtualization capabilities will spend 40% less on building and managing data integration processes for connecting distributed data assets.

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Case Study

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Leading Semiconductor Firm Accelerates Product Time To Market Positive Revenue Impact Worth $Millions

• Lose $1B for every month delay in product release

• Month lost incrementally (minutes here, days there) by users looking for the right information to make accurate decisions

• Eliminate ETL for operational purposes and use DV to get all data in real time from original sources to accelerate accurate decision making.

• Still store historical data in DW but store much less

• Cut Time to Market (TTM) of delivering data to business by 90%

• Reduced product TTM by days generating $M’s in more revenue.

HQ in Santa Clara, California, is the world’s largest semi-conductor manufacturing firm, In 2015, the company reported a revenue of $55 billion with its 107,000 employees.

Problem Solution Results

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Denodo’s Packed Lunch Webinar Series

A look at what’s coming soon…

Webinar #2 – Big Data Fabric

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Webinar #3 – Complete View of Customer

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Webinar #4 – Centralized Security and Governance

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Webinar #5 – Self-Service Analytics

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Webinar #6 – Data as a Service

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And Even More…Many more topics in more webinars within the ‘Packed Lunch’ series:• Hybrid architectures with Cloud and On premise data• Migrating to the Cloud without disrupting the business• In-memory data grids with Data Virtualization for real-time analytics• Accessing ‘Open Data’ from Government web sites• etc.

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Q&A

Next stepsDownload Denodo Express: www.denodoexpress.com

Access Denodo Platform on AWS: www.denodo.com/en/denodo-platform/denodo-platform-for-aws

Next session

Big Data Fabric: A Recipe for Big Data Initiatives Thursday, February 16, 2017

Paul MoxonVP Data Architectures & Chief Evangelist, Denodo

Chris WaltersSenior Data Solutions Consultant, Denodo

Thank you!

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