The Modern Data Warehouse - A Hybrid Story

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As organizations realize the cost savings and scalability benefits of hybrid solutions, the focus turns to implementing your analytic platforms in these new environments. On-premises vs. cloud is the major decision point, but what about ‘traditional’ disk-based storage and computing vs. in-memory? Join Perficient’s director of Microsoft business intelligence, Duane Schafer, for a discussion on the business benefits of these topics, and discover how SQL Server 2014 and Microsoft Azure can be leveraged to build the modern data warehouse.

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The Modern Data Warehouse

Perficient is a leading information technology consulting firm serving clients throughout
North America.
We help clients implement business-driven technology solutions that integrate business
processes, improve worker productivity, increase customer loyalty and create a more agile
enterprise to better respond to new business opportunities.
About Perficient

BUSINESS SOLUTIONS
Business Intelligence
Business Process Management
Customer Experience and CRM
Enterprise Performance Management
Enterprise Resource Planning
Experience Design (XD)
Management Consulting
TECHNOLOGY SOLUTIONS
Business Integration/SOA
Cloud Services
Commerce
Content Management
Custom Application Development
Education
Information Management
Mobile Platforms
Platform Integration
Portal & Social
Our Solutions Expertise

Duane Schafer, Director of National
Microsoft BI Practice at Perficient
With nearly 20 years in technology consulting, BI
architectures, and solution sales including hybrid
cloud and DW appliance architectures, Duane is
responsible for strategy assessments including
EIM, BI, MDM and governance, solutions
architecture, and management of key client
engagements – as well as BI/DW architecture,
analysis & training in Microsoft BI stack.
Our Speaker

• The Traditional DW
• DW Stressors
• A Modern Alternative
• New Capabilities
Agenda

The Traditional DW - Defined
• Server and DBMS
• Methodology for loading data (ETL vs. ELT)
• “clean stage vs. dirty stage”
• Methodology for storing data (Kimball vs. Inmon)
• DW vs. DM

The Traditional DW - Defined
• Methodology for archiving data (never)
• ODS + DW = HUB
• “Historical and non-volatile”…
• What is history?
“I think… I am history
therefore… I’m history?”

The Traditional DW - Architectures
Logical Architecture – Mostly concerned about the
representation of data back to the end user. Included
abstraction/semantic layers, methods of access, etc.
Physical Architecture – Mostly concerned about the
storage and structuring of data. Included indexing (or
not) plans, schemas for security, auditing for data, etc.
However, the general consensus is that the DW is one
platform that serves a primary workload.
- “Reporting and analytics”
- Schema driven
- Dimensional aspect to it
- “One repository of truth”

Stressors to the Traditional DW
“… data warehousing has reached the most
significant tipping point since its inception. The
biggest, possible most elaborate data
management system in IT is changing. “
- Gartner, “The State of Data Warehousing in 2012”

Stressors to the Traditional DW
- TDWI, “Evolving Data Warehouse Architectures in the Age of Big Data Analytics”, 2014

Check Point
How does this affect our ‘traditional’ DW approach?
• Original work load is still covered
• Staging, ETL, schema based repository
• Original version of the truth is still in place
• No need to rip and replace
• New work load capabilities have been added
• Staging / archiving has been expanded
• ETL has more capacity
• Multi-schema based analysis is available
• The EDW is becoming a hybrid analytic platform

Thank you for your time
and attention today.
Please visit us at Perficient.com

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Dan O'Callahan Resume: Executive leader experienced at transforming technology operations, improving business performance and facilitating revenue growth by architecting and implementing cost-effective, robust and flexible systems and infrastructure. • Customer Service • Sales Support • Project Management • Systems Development • Business Intelligence • Analytics • Databases • Network & Security • Telecommunications • Call Center Support • Data-Center Operations • Business Continuity

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1. facebook.com/perficient twitter.com/Perficient_MSFTlinkedin.com/company/perficient The Modern Data Warehouse

2. Perficient is a leading information technology consulting firm serving clients throughout North America. We help clients implement business-driven technology solutions that integrate business processes, improve worker productivity, increase customer loyalty and create a more agile enterprise to better respond to new business opportunities. About Perficient

3. • Founded in 1997 • Public, NASDAQ: PRFT • 2013 revenue ~$373 million • Major market locations throughout North America • Atlanta, Boston, Charlotte, Chicago, Cincinnati, Columbus, Dallas, Denver, Detroit, Fairfax, Houston, Indianapolis, Los Angeles, Minneapolis, New Orleans, New York City, Northern California, Philadelphia, Southern California, St. Louis, Toronto and Washington, D.C. • Global delivery centers in China, Europe and India • >2,100 colleagues • Dedicated solution practices • ~85% repeat business rate • Alliance partnerships with major technology vendors • Multiple vendor/industry technology and growth awards Perficient Profile

4. BUSINESS SOLUTIONS Business Intelligence Business Process Management Customer Experience and CRM Enterprise Performance Management Enterprise Resource Planning Experience Design (XD) Management Consulting TECHNOLOGY SOLUTIONS Business Integration/SOA Cloud Services Commerce Content Management Custom Application Development Education Information Management Mobile Platforms Platform Integration Portal & Social Our Solutions Expertise

5. Our Microsoft Practice

6. Duane Schafer, Director of National Microsoft BI Practice at Perficient With nearly 20 years in technology consulting, BI architectures, and solution sales including hybrid cloud and DW appliance architectures, Duane is responsible for strategy assessments including EIM, BI, MDM and governance, solutions architecture, and management of key client engagements – as well as BI/DW architecture, analysis & training in Microsoft BI stack. Our Speaker

7. • The Traditional DW • DW Stressors • A Modern Alternative • New Capabilities Agenda

8. The Traditional DW - Defined • Server and DBMS • Methodology for loading data (ETL vs. ELT) • “clean stage vs. dirty stage” • Methodology for storing data (Kimball vs. Inmon) • DW vs. DM

9. The Traditional DW - Defined • Methodology for archiving data (never) • ODS + DW = HUB • “Historical and non-volatile”… • What is history? “I think… I am history therefore… I’m history?”

10. The Traditional DW - Architectures Logical Architecture – Mostly concerned about the representation of data back to the end user. Included abstraction/semantic layers, methods of access, etc. Physical Architecture – Mostly concerned about the storage and structuring of data. Included indexing (or not) plans, schemas for security, auditing for data, etc. However, the general consensus is that the DW is one platform that serves a primary workload. - “Reporting and analytics” - Schema driven - Dimensional aspect to it - “One repository of truth”

11. Stressors to the Traditional DW “… data warehousing has reached the most significant tipping point since its inception. The biggest, possible most elaborate data management system in IT is changing. “ - Gartner, “The State of Data Warehousing in 2012”

12. Stressors to the Traditional DW - TDWI, “Evolving Data Warehouse Architectures in the Age of Big Data Analytics”, 2014

13. Stressors to the Traditional DW

14. A Modern Alternative

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21. Check Point How does this affect our ‘traditional’ DW approach? • Original work load is still covered • Staging, ETL, schema based repository • Original version of the truth is still in place • No need to rip and replace • New work load capabilities have been added • Staging / archiving has been expanded • ETL has more capacity • Multi-schema based analysis is available • The EDW is becoming a hybrid analytic platform

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