Assessing analytical workloads is the first step towards successful cloud migration. However, an assessment typically provides a non-actionable list of inventories. An intelligent automation-based workload assessment offered by Impetus’ Workload Transformation Solution can help you get actionable insights. It profiles workloads and maps their compatibility with your target cloud environment. As a result, you are prepared to avoid common pitfalls and ensure a successful cloud transition of your ETL and analytics workloads. In this session, our experts will share insights on how this solution can help you: Identify workload complexities, patterns, and technical debt Map existing workloads to your target cloud stack Create a blueprint for future-state architecture based on an automation-based intelligent assessment Implement best practices to de-risk your cloud transition We will also share success stories of how Impetus has helped Fortune 500 enterprises make the right decisions for a seamless EDW transformation. To learn more view our webinar here - https://bit.ly/37zSwML

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2. IMP
Automated EDW assessment and actionable
recommendations for moving analytics to
the cloud

3. Our Mission
Enabling a unified, clear, and present view of your business

4. ~Gartner, Inc.
By 2022, 75% of all databases will be deployed or migrated to a cloud platform,
with only 5% ever considered for repatriation to on-premise

5. Donald Feinberg, distinguished Research Vice President at Gartner
“We also believe this begins with systems for data management solutions for
analytics (DMSA) use cases — such as data warehousing, data lakes, and other use
cases where data is used for analytics, artificial intelligence (AI)
and machine learning (ML)”

6. Agenda
Industry’s current and future view
Common pitfalls
How Impetus approach can help
Workload assessment
Demo
Business outcomes

7. Poll

8. Hari Kodakalla
Director, Cloud and Data Strategy
Barry Tuthill
VP Field Operations
Speakers

9. Common pitfalls

10. Pitfall #1
Buying into the simplicity hype

11. Pitfall #2
Lifting and shifting your on-premise stack to the cloud as-is

12. Pitfall #3
Lacking a cross-reference roadmap of current features/functions with the
continuously evolving cloud landscape

13. How Impetus approach can help

14. End-to-end automated transformation lifecycle
A 4-step iterative process
• Assess
• Transform
• Validate
• Execute

15. Impetus workload assessment
Prescriptive and not reactive
• Actionable recommendations
based on insights
Automation and acceleration
through IP-based experience
Analysis of inventory
Automated discovery,
patterns in code
Logical segregation
into migration units
Workloads optimization
for performance
Recommendations and
blueprint for future-
state architecture
Comprehensive reports
and phased migration
plan

16. Analysis
Profiling of entire inventory
• Scheduler scripts, procedures, DML and DDL scripts, ETL graphs,
database logs, application logs
Complex interdependencies/lineage
Rich visualizations

17. Discovery
Identifying different code patterns
Resource utilization
Assessing complexity and frequency of use

18. Segregation
Workload segregation based on patterns and complexity
• As-is migration
• Optimization
• Rearchitecting

19. Optimization
Identifying technical debt
Parallel workload execution
Target schema optimization recommendations

20. Future-state
Mapping compatibility
Identifying tech stack components
Target architecture

21. Poll results

22. Demo

23. Business outcomes
Informed, data-driven decision-making
Non-disrupted migration
Mitigate migration risks beforehand
Reuse existing and maximize cloud investments
Faster time-to-market

24. Assessment is not the end, it’s the beginning

25. End-to-end automated transformation lifecycle
Assess and plan ExecuteTransform Validate
ENTERPRISE
DATA WAREHOUSE
Live Query Logs
Offline Query
Logs
DML/DDL Scripts
ETL Graphs
Scheduler Scripts
MODERN DATA
PLATFORM
UDF
UDF
Schema, Data, Metadata, View, Report
SQL, PL/SQL
BTEQ, KSH,
Reports,
ETL Graphs
LOGIC TRANSFORMATION
HQL, Spark SQL,
Shell Script,
Scala, Java,
Tableau Report,
Python
Number/Count
of Rows
Min/Max Value,
Distinct Count
Sum/Average of
Row Values
Table/Query
Specific

26. Q&A
Visit www.impetus.com or get in touch with us at inquiry@impetus.com

27. Thank You
Visit www.impetus.com or get in touch with us at inquiry@impetus.com