mt32 how relational (sql) and unstructured data (hadoop) learned to get along

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MT32 How relational (SQL) and unstructured data (Hadoop) learned to get along David Leibowitz, Dell EMC Stephen Kyriakos, North Texas TollwayAuthority Larry Levy, Microsoft

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Page 1: MT32 How relational (SQL) and unstructured data (Hadoop) learned to get along

MT32How relational (SQL) and unstructured data (Hadoop) learned to get along

David Leibowitz, Dell EMC

Stephen Kyriakos, North Texas Tollway Authority

Larry Levy, Microsoft

Page 2: MT32 How relational (SQL) and unstructured data (Hadoop) learned to get along

“The computer isn’t the thing. The computer’s the thing that gets us to the thing”- Joe MacMillan

Page 3: MT32 How relational (SQL) and unstructured data (Hadoop) learned to get along

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Big Data

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What Can I Do About It?

When Will it Happen?

Why Did it Happen?

What Happened?

Questions asked of Big Data

Innovation

Complex implementations

Spreadmarts

Siloed data

Transactional systems

Valu

e

Enterprise data warehouse

OLAPETL

Operational Reporting

Machine learning

Any dataIn-memory

Internet of Things

Optimization &

StimulationHadoop

DashboardsAd hoc analysis

Predictive Analytics

Data mining

Page 5: MT32 How relational (SQL) and unstructured data (Hadoop) learned to get along

Example of the Harmony of Structured & Unstructured Data:

Healthcare

Page 6: MT32 How relational (SQL) and unstructured data (Hadoop) learned to get along

Case Study:

Fullerton Health

Page 7: MT32 How relational (SQL) and unstructured data (Hadoop) learned to get along

Example of the Harmony of Structured & Unstructured Data:

Public

Page 8: MT32 How relational (SQL) and unstructured data (Hadoop) learned to get along

Case Study:

North Texas TollwayAuthority

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BI System – 0 to 70 MPH

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Theirs Theirs

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Ours

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Everyone drives on toll roads

$9.5 Billion in new projects

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First to use transponder as

a method for toll collection

First to convert

entire system to ETC

Paving the way

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• Public entity / Private bonds

• Multiple / diverse audiences

• Blind to 20% of customers

• Capture data at 70 MPH

It’s complicated

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BI solves the complicated

7.4 million customers

2.1 million daily transactions

97% customer satisfaction

One of the fastest growing regions in the U.S.

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It’s complicated. How we did it

Evaluated resources

Talked to the business

Reviewed infrastructure

Made a plan

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Why BI? Inventoried data

VarietyVelocity

Venue

Volume

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Define the “to be” state

Manage hybrid workloads

Integrate with business partners

Self-Service and Full-Service Analytics

Extend the solution

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Delivered CARS

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Become Your

Own Architect

We can purchase this photo if you like it.

It speaks to finding a solution that works – cutting through

layers in order to get what you need.

Defined our “To Be” stateExtend the BI team

Empower usersBe your own architect

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Our Next Steps

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Example of the Harmony of Structured & Unstructured Data:

Education

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Demo:

Student Analytics with Education Data Management

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Getting Started

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Stakeholders

Procurement

PMO

It takes a village

Lessons Learned

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Data Modernization & Becoming Future Ready

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Key takeaways

Complicated is doable

Serve the business

Build a flexible, scalable solution

Plan your work, work your plan

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