Greater agility, scalability, and lower total cost of ownership made the decision to move key elements of your organization’s data capability to the cloud easy. The real challenge is migrating data from your legacy systems to your new cloud platform so you can unleash its potential and value while minimizing the migration risks. Combining erwin‘s data modeling, governance, and intelligence solutions with Snowflake’s modern cloud data platform, organizations can realize a scalable, governed, and transparent enterprise data capability. In this session, we’ll show you how enterprise stakeholders with different skills and needs can work together to accelerate and assure the success of cloud migration projects of any size. You’ll learn how to: • Reduce costs and mitigate risks when migrating legacy applications to Snowflake with erwin’s model-driven schema design and transformation capabilities • Increase the precision, speed, and agility of Snowflake deployments with erwin data automation • Assure transparency, compliance, and governance for Snowflake data and processes • Increase the efficiency and accuracy of analytics and other data usage on the Snowflake Cloud Platform

1. Assure and Accelerate The Adoption of
Cloud Data PlatformsD A N N Y S A N D W E L L , D I R E C T O R P R O D U C T M A R K E T I N G , E R W I N I N C .
CONFIDENTIAL

2. erwin Data Literacy Suiteerwin Data Catalog Suite
Business User Portal
Business Glossary
Manager
Mapping Manager Lifecycle Manager
Reference Data
Manager
Data Profiling
Data Intelligence SuiteEnterprise Modeling
erwin Evolve
erwin Data Modeler
Data Automation
Standard Data Connectors Smart Data Connectors
erwin Enterprise Modeling & Data Intelligence Software
Solutions Focused On Enabling A Data-Driven Approach
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AI Match Workflow Manager

3. The Drivers for Cloud Adoption and Data Platform
Modernization
Digital
Transformation
Business
Continuity
Data Driven
Innovation
Financial
Optimization
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4. The Data Dilemma – IDC 2020
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IDC estimates 45
zettabytes of data
created in 2019 and
expected to grow at 26%
compounded annually
over five years to 2024.
95% of organizations
integrating up to six
different types of data
across 10 different types
of data management
technologies as they
manage operations, seal
strategic insights and
make business decisions.
In 2019, 94% of
organizations were
integrating data across
hybrid cloud
environments.

5. The Need For Data Intelligence – IDC 2020
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Effects of data
environment complexity
and the state of
intelligence about data
are being seen in the
efficiency and
effectiveness of data-
native workers.
The 80/20 rule stating the
percentage of time spent
in data discovery and
preparation compared to
the percentage of time
spent in analytics is
getting worse, now
approaching 85/15 as per
the results of the 2019
IDC DII survey.
This survey also told us
that on average, data-
native workers are more
unsuccessful than
successful in their tasks
as they search for,
prepare, govern and
analyze data.

6. Benefits
• Performance and Scalability
• Elasticity and Agility
• Lower TCO and Future Proof
• More Value From Data
Capabilities
• High Performance Data Store
• Hybrid DBMS Modalities
• Agile Data Integration
• Integrated BI & Analytics
6
Cloud Data Platform Benefits and Capabilities
© 2020 erwin, Inc. All rights reserved.

7. Data Governance and Intelligence
Migration
Transparency
Documenting
cutting edge
technologies
Data
democratization
enablers
Migrating Legacy Deployments
Time To Value
Conversion
Accuracy
Cost
Containment
7
Challenges To Realizing Modernization Benefits
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8. e
Modernizing Data Architecture
Automate Key Tasks to Accelerate and Assure
Transform &
Deploy Schema
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Bulk Load Data
Data
Re-Point Data
Movement
Re-Platform
Data Movement
Repeatable
Dev/Ops
Time To Value Accuracy
GovernanceReduced Costs

9. Modernizing Data Architecture
Migrate RDMBS structures to the cloud with data modeling
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Reverse Engineer Transform Forward Engineer

10. Data Mapping Documents:
Activating Metadata For Maximum Utility
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Data Movement Capture
Abstracted Mapping
Documents
Mapping Exploration
and Activation
Scan and Auto-
Document Code
Import Mappings from
Delimited Files
Manually Specify Mappings
Import Data Model Mappings
Source
Transformation
Target
Lineage Rendering
Impact Analysis
Automated Code Generation

11. Data Mapping: The “Logical Model” for Data Movement
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12. Modernizing Data Architecture
Automated ETL conversion process
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Automated ETL migration complexity assessment
Reverse engineer legacy ETL mappings
Forward engineer target ETL equivalent mappings
Unit test for completeness

13. Modernizing Data Architecture
Automated ETL migration complexity assessment
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StratificationComponentFrequency
LoadDesignPatternsProposedTimeline

14. Technology Integration
• Cloud-based ETL
• Spark-based ETL
• Big Data initiatives
Modernizing Data Architecture
Legacy ETL mapping conversion to cloud native tools
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Automation Benefits
• Consistent, machine-generated code
• Load design pattern standardization
• Significant time and cost savings

15. Cloud Governance
Enabling democratization of technical assets with a Contextual Business Asset Framework
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Mind Map Associations
Technical Assets
Business Terms
Policies & Procedures
Custom Associations

16. Cloud Governance
Leverage AI to Automate Governance and Intelligence Framework Configuration
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17. 17© 2020 erwin, Inc. All rights reserved.
Cloud Governance
Automate the Discovery and Rendering of Detailed Lineage

18. Cloud Governance
Automate the Classification of Sensitive Data
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19. erwin Smart Connectors
Optional connectors that enable you to harvest metadata from a wide array of other sources, generate
code, and integrate with ecosystem environments.
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Reverse Engineering
Code Generation
Ecosystem Integrations
Testing Automation
Connectors auto-document (reverse engineer) mappings from ETL, BI
Tools, and procedural code.
Connectors to generate (forward engineer) mappings for ETL, ELT
Tools, and procedural code.
Connectors to integrate ecosystem applications both from a process
and meta and/or meta data perspective.
Connectors to connect Test tools (HP ALM/Quality Center), Generate
Test Cases, Generate Test SQL, Generate Validation and Test.

20. Realize Maximum Business Value From Cloud Data
Platforms
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Reduce costs and
mitigate risks when
migrating legacy
applications and data
to the cloud
Increase the precision,
speed, agility and
understanding of
cloud data
deployments
Assure transparency,
compliance and
governance for cloud
data and processes
Increase stakeholder
literacy and optimize
the efficiency and
accuracy of analytics
and other data usage
Cloud Data Platforms

21. Questions and Discussion