Prologis utilizes data virtualization to enhance data scientists' capabilities by enabling cost optimization and improved integration with third-party data sources. The architecture supports flexible data consumption and scalable infrastructure, allowing for faster insights and data access. Future recommendations include designing reusable patterns and leveraging caching for performance improvements.

2. How Data Virtualization enables Data
Scientists at Prologis
Ryan Thompson
Director, Data Virtualization and Architecture, Prologis

3. 3
About Prologis
$1.5TRILLION
is the economic value of goods flowing through
our distribution centers each year, representing:
2.8%
of GDP for the 19 countries where
we do business
%2.0
of the World’s
GDP
1983 100 GLOBAL 768 MSF
Founded Most sustainable corporations
$87B
Assets under management on four continents
MILLION
employees under Prologis’ roofs
1.0

4. 4
Business Need
Cost Optimization
Identify improvements in spend on Capital
Deployments
Caption Placeholder
Easy integration for Data Science Team
Integrate with 3rd party data capture
platform

5. 5
Technical Challenge
Cloud First Philosophy
Minimize ETL
Flexible Data Consumption
Scalable Infrastructure

6. 6
Solution
Architecture

7. 7
Solution
Virtualized – On demand model scoring
Partial Cache – Historical scores backed to DB & real time scoring of new data
Full Cache (Full Refresh) – Large datasets benefit from batched scoring
Full Cache (Incremental Refresh) – Benefit of full cache with optimized refresh processing
Processing Capabilities

8. 8
Benefits
Data Scientists
-Enable access to data in one environment
-Ability to code in languages they already know
-Speed up integration into logical data lake
Capital Deployment
-Validate assumptions with data
-Provide quicker access to insights
-Access/Distribute findings easily

9. 9
Future Roadmap

10. 10
Recommendations
Design re-usable patterns for Data Scientists
-A template for input/output in various
languages you want to integrate speeds up
future integrations
Utilize Standard Integration Patterns
-API Endpoints
-JSON payloads
Utilize the benefits of virtualization
-Use a microservice development pattern
-Swap out new versions without impact
-Input data is not limited to a single source
type (db, api, flat file, etc)
Increase performance with cache
-Full Incremental Cache enables real time
scoring plus the benefit of backed by a
database

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