This presentation discusses Impetus Technologies' services for migrating enterprise data warehouses, analytics, and ETL workloads to AWS. It provides an overview of Impetus' cloud migration strategy and methodology, including automating workload transformation through assessment, code conversion, data migration, validation, and execution on AWS. A demo is shown of Impetus' automated tools that can reduce migration efforts by 50% by intelligently transforming queries, data, procedures, scheduling, and more to cloud-native AWS analytics services like Redshift, EMR, and Snowflake.

1. Copyright © 2020 Impetus Technologies, Inc.
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2. The Fastest Way to Convert
ETL, Analytics, and Data Warehouse to AWS
IMP

3. A unified, clear, and present view of your business
Data platform transformation
Legacy data platforms to modern
data platforms
Dataflow and analytics
Data processing, preparation, machine
learning and analytics, real-time and batch
Cloud, big data, analytics, RPA and AI -
Advisory, engineering, design, DevOps, application development and management
Professional services

4. Contents
Cloud migration strategy
How Impetus can help
Automated workload transformation to AWS
Demo
Q&A

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

6. Cloud Migration Strategy

7. Cloud transformation objectives
Faster time-to-market
Optimal solutions
Transparent transition
Versatile modern architectural stack

8. Migration checklist
Reuse
Automate
Optimize
Certify

9. Migration checklist
Reuse
Automate
Optimize
Certify

10. Migration checklist
Reuse
Automate
Optimize
Certify

11. Migration checklist
Reuse
Automate
Optimize
Certify

12. Poll Results

13. How Impetus Can Help

14. Support for multiple architectures
AWS-native stack
• Composite stack
• S3-EMR-Redshift
• Redshift for everything
• Mix of Redshift and other native choices like Spectrum, Athena, or others
Leverage third-party options on AWS
• Snowflake
• Databricks

15. End-to-end Automated Migration

16. Automating the complete migration lifecycle
1
2
3
4
Assess
Transform
Validate
Execute

17. Assessment – Actionable recommendations
Analysis of inventory
Automated discovery
Target-specific optimization strategy
Performance matching
Recommendations and blueprint
Comprehensive reports

18. Transformation
90% automated code conversion
DML, stored proc, scheduler/orchestrator script, ETL graph
Automated data migration
Handling of data types, nested views, loops, UDFs, intervals, triggers, etc.
Code pattern identification, auto-generation of patterns for newer migrations
Query-editing for optimized fixes and performance tuning
Automated packaging to Shell, Python, or cloud-native wrappers

19. Validation and execution
Auto-generation of validation/reconciliation scripts
Execution through cloud-ready orchestration/execution engine
Integrated DevOps, CI, and agile processes setup
Accelerated decommissioning methodology
Multi-cloud ready with security and governance

20. Reducing risk and 50% overall effort
with automation
Assessment/
Planning
5%
40% Reduction
3%
Data Migration/
Code conversion
40%
80% Reduction
8%
Validation/
Testing
15%
50% Reduction
7%
Orchestration/
Performance
Parallel Run/
Production
15% 25%
7% 25%
VALIDATEASSESS EXECUTETRANSFORM
100%
50%
Manual effort
Automated
50% Overall
Reduction50% Reduction
Total Effort

21. Demo

22. Reducing risk and 50% overall effort
with automation
Assessment/
Planning
5%
40% Reduction
3%
Data Migration/
Code conversion
40%
80% Reduction
8%
Validation/
Testing
15%
50% Reduction
7%
Orchestration/
Performance
Parallel Run/
Production
15% 25%
7% 25%
VALIDATEASSESS EXECUTETRANSFORM
100%
50%
Manual effort
Automated
50% Overall
Reduction50% Reduction
Total Effort

23. Automated and intelligent workload transformation
ENTERPRISE
DATA WAREHOUSE
VALIDATE
Query Logs
Script Files
Data
Query
(Syntactic
Semantic)
Query
(Semantic)
BIG DATA
WAREHOUSE
ASSESS EXECUTETRANSFORM
UDF
UDF
Data - S3/Spark, Snowpipe, ETL
SQL,
Procedural
Constructs
Scheduling/
Workflow
LOGIC TRANSFORMATION
Data - S3/Spark, Snowpipe, ETL
HQL, Spark, SQL
SnowSQL
Scala/Python/
Java wrappers
Shell Script

24. Business value
Informed, data-driven decision-making
Zero disruption
Mitigate migration risks
Reuse existing and maximize cloud investments
Faster time-to-market

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

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