Comprehensive Proposal Plan for insfrastructure migration from on prem to cloud

1. Revolutionizing Data
Infrastructure:
A Comprehensive Proposal for Oracle to
Snowflake Migration, Modernization, and AI
Integration
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Contents
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Ghadi Group Overview
Proposal Overview
High-level Architecture Diagram
Migration Approach (Roadmap)
Current System Analysis
Migration Details
Training Plan
Benefits and ROI
Risk Assessment
Testing Plan
Resources and Budget
Case Studies
What Our Customers Say
Partners
Our Team
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3. Ghadi group Overview
Ghadi Group, a distinguished global manufacturing entity
operating across France, the UK, Germany, the Netherlands, and
the USA, stands at the forefront of innovation in the watch
industry.
Currently, Ghadi Group's data architecture integrates Oracle EBS
ERP, Data Warehouse, Informatica ETL, and OBIEE reporting for
its global operations.
However, recognizing dynamic technology shifts, Ghadi eyes
data modernization through cloud adoption, notably with
Snowflake. Overcoming scalability issues, enhancing agility, and
integrating emerging technologies are priorities.
This move positions Ghadi to leverage the latest Generative AI/ML
use cases, transforming their data architecture into an
innovation catalyst. Embracing cloud solutions promises
increased insights and agility in data analytics and artificial
intelligence.
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4. Key Metrics
Warehouse size: 1 TB 30+ FACT Tables ~10M Records 400 ETL Jobs
45 DIM Tables
600 Reports in OBIEE 20+ Dashboards
Current System Analysis
Known
Challenges
1. Long-running reports in OBIEE
2. Data refresh exceeding 12
hours daily
3. Limitations in building
Generative AI Use Cases
Other Challenges
Oracle EBS
ERP
Informatica
ETL
Oracle DW
Dashboards
Reports
OBIEE
Outdated Technology Stack: Unable to meet today’s business
user’s needs, such as unlimited concurrency and performance.
1.
Limited Scalability: Challenges in scaling with growing data
volumes and increasing user loads.
2.
Integration Complexity: Integrating with newer data sources,
applications, or cloud environments require customized
solutions.
3.
Inflexibility in Data Formats: Legacy systems struggle with
challenging to adapt to the variety of data sources available
today.
4.
Limited Support for Real-time Processing: Not be well-suited
for real-time data processing and analytics, impacting the ability
to make timely business decisions.
5.
Security Vulnerabilities: Outdated security protocols and
features may expose legacy architectures to potential
cybersecurity risks and compliance issues.
6.
High Maintenance Costs: Needs specialized skills familiar with
outdated technologies.
7.
Business
Requirements
Data migration to Snowflake
1.
Data Warehouse Modernization
2.
Integration of AI Solutions
3.
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5. 3. Power smart manufacturing initiatives:
The Manufacturing Data Cloud
enables the ingestion and
convergence of IT and OT data—a key
requirement for smart
manufacturing. You can leverage
Snowflake’s powerful analytics
and AI/ML capabilities to generate
insights and predictions to improve
production quality and efficiency,
reduce waste and downtime, and
automate processes.
Adopting A Unified Data Management Platform
with the new
Snowflake Manufacturing Data Cloud
A single, fully managed, multi-cloud platform for data consolidation, governance, and
performance.
Build a secure, scalable data foundation:
1.
Easily incorporate both IT (Information
Technology) and OT (Operational
Technology) data from various sources,
such as ERP systems, sensors, machines,
and cloud services, into a single source
of truth
2. Boost supply chain performance:
Near real-time visibility into the
operations and performance of your
end-to-end supply chain, data to
identify potential bottlenecks and
risks, and insights to optimize
inventory and logistics
4. Collaborate with suppliers, and customers:
Improve supply chain performance,
product quality and factory efficiency
The Snowflake Manufacturing Data Cloud is designed to help you deliver improved supply chain performance and embrace Industry 4.0 with data-driven innovation and agility.
DATA ANALYTICS USE CASES
PREDICTIVE
MAINTENANCE
BIG DATA
ANALYSIS
MAXIMIZING
THROUGHPUT
SUPPLY CHAIN
OPTIMIZATION
ACCURATE
DEMAND
FORECAST
WAREHOUSE
MANAGEMENT
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6. High-Level Architecture Diagram
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7. Migration Approach (ROADMAP)
Discovery Phase:
Comprehensive understanding of existing
systems and challenges.
Activities:
Stakeholder interviews for insights.
In-depth analysis of current data
architecture.
Identify key pain points and
opportunities.
Data Migration Planning:
Strategize seamless transition to Snowflake.
Activities:
Assess data volume, complexity,
and dependencies.
Develop a phased migration plan.
Define data validation and quality
assurance measures.
Snowflake Implementation:
Establish a robust foundation for
modernized data warehousing.
Activities:
Deploy Snowflake architecture.
Migrate data according to the
planned phases.
Verify and validate data
integrity.
ETL Refactoring for Snowflake:
Optimize ETL processes for Snowflake
compatibility.
Activities:
Review and enhance existing
ETL workflows.
Integrate Snowflake-specific
optimizations.
Conduct rigorous testing to
ensure efficiency.
Power BI Integration:
Enhance reporting capabilities and user
experience.
Activities:
Assess existing OBIEE reports for
migration.
Modify and optimize reports using
Power BI.
Implement user training for Power BI
adoption.
AI Integration Strategy:
Incorporate AI solutions for
advanced analytics.
Activities:
Identify AI use cases
aligned with business
goals.
Assess data readiness
for AI integration.
Implement and test AI
models for forecasting
and insights.
User Training and Adoption:
Ensure seamless transition and user proficiency.
Activities:
Develop comprehensive training
materials.
Conduct user training sessions.
Provide ongoing support and resources.
Monitoring and Optimization:
Continuous improvement and
performance monitoring.
Activities:
Establish monitoring
tools for Snowflake
and AI solutions.
Conduct regular
performance reviews.
Implement
optimization
strategies as needed.
Documentation and Knowledge Transfer:
Document and transfer knowledge for long-term
sustainability.
Activities:
Create comprehensive documentation
for the new system.
Facilitate knowledge transfer sessions.
Ensure documentation is accessible for
future reference.
Post-Implementation Review:
Evaluate project success and
gather feedback.
Activities:
Conduct a thorough
review of the entire
implementation.
Collect feedback from
end-users and
stakeholders.
Identify lessons learned
and areas for further
enhancement.
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8. Migration Details
MIGRATE
SCHEMA
MIGRATE
DATA
BUILD DATA
PIPELINE
BUILD
METADATA
CATALOG
MIGRATE
USERS
Scalable
compute to
power data
transformation
Role-based
security
Pay-as-you-
go model
Easy schema
migration
Automated
query
optimization
STEP 1: Load initial data sets
STEP 2: Test the process end-to-end with a subset of data
STEP 3: Migrate the data and check performance
STEP 4: Run the Oracle and Snowflake Systems in parallel
STEP 5: Redirect tools to Snowflake
STEP 6: Cut over to Snowflake
STEP 7: Use Power BI’s Native Snowflake Connector for BI purposes (using Composite Model for both Fact
and DIM tables)
STEP 8: Create Data Model (STAR Schema)
STEP 9: Set up Azure AD SSO to Snowflake for data to use the security rules configured in Snowflake
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9. Project Phase Objective Responsibility Levels of Testing Testing Activities Number of Days Prerequisites
1. Discovery Phase
Understand current systems, identify
potential issues, and define scope.
Project Manager, Data Analysts System Testing, Acceptance Testing - Stakeholder interviews for insights. 5 Project documentation
- Analysis of current data architecture.
2. Data Migration Planning
Develop a detailed plan for a seamless
transition to Snowflake.
Data Migration Specialist Integration Testing, System Testing
- Assess data volume, complexity, and
dependencies.
10 Completed Discovery Phase
- Define data validation and quality
assurance measures.
3. Snowflake Implementation
Establish Snowflake architecture and migrate
data accordingly.
Database Administrator System Testing, Performance Testing, Security Testing - Deploy Snowflake architecture. 15 Completed Data Migration Planning
Data Migration Specialist
- Migrate data according to the planned
phases.
4. ETL Refactoring
Optimize ETL processes for compatibility
with Snowflake.
ETL Specialist Integration Testing, System Testing
- Review and enhance existing ETL
workflows.
10 Completed Snowflake Implementation
- Integrate Snowflake-specific
optimizations.
5. Power BI Integration
Modify and optimize reports for enhanced
reporting capabilities.
Reporting Specialist System Testing, User Acceptance Testing (UAT)
- Assess existing OBIEE reports for
migration.
7 Completed ETL Refactoring
- Modify and optimize reports using Power
BI.
6. AI Integration Strategy
Implement and test AI models for advanced
analytics.
AI Specialist
System Testing, Performance Testing, User
Acceptance Testing
- Identify AI use cases aligned with business
goals.
12 Completed Power BI Integration
- Assess data readiness for AI integration.
7. User Training and Adoption
Ensure users are proficient in using the new
system.
Training Specialist User Acceptance Testing (UAT) - Develop comprehensive training materials. 8 Completed AI Integration Strategy
- Conduct user training sessions.
8. Monitoring and Optimization
Continuous improvement and performance
monitoring.
System Administrator Performance Testing, Security Testing
- Establish monitoring tools for Snowflake
and AI solutions.
7 Completed User Training and Adoption
- Conduct regular performance reviews.
Testing Plan
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10. Training Phase Objective Training Activities Deliverables
1. Project Overview Ensure stakeholders understand the project scope and goals. - Conduct a kickoff meeting to present the project overview and objectives. Kickoff meeting presentation
- Distribute project documentation for stakeholders to review. Project documentation distributed
- Q&A session to address any initial questions or concerns. Q&A session conducted
2. Technology Training Familiarize stakeholders with the new technologies used. - Provide hands-on training sessions on Snowflake, Power BI, and AI integration. Hands-on training sessions completed
- Conduct workshops for practical application and problem-solving. Workshops conducted
3. Data Migration Training Train stakeholders on data migration processes. - Demonstrate the data migration process using Snowflake. Data migration demonstration completed
- Provide guidelines on data validation and quality assurance. Guidelines on data validation shared
- Conduct hands-on exercises for data migration practices. Hands-on exercises completed
4. Reporting and Analytics Train users on creating reports and utilizing analytics. - Conduct Power BI training sessions for report creation. Power BI training sessions completed
- Guide users on interpreting and utilizing AI-driven insights. AI insights interpretation training completed
- Provide access to training datasets for practical exercises. Access to training datasets granted
5. System Monitoring Educate stakeholders on monitoring system performance. - Explain the monitoring tools and how to interpret performance metrics. Monitoring tools explained
- Conduct training sessions on system performance reviews. Training on system performance reviews completed
6. Troubleshooting Equip stakeholders with basic troubleshooting skills. - Outline common issues and their resolutions. Troubleshooting guidelines shared
- Conduct Q&A sessions for specific concerns and issues. Q&A sessions for troubleshooting completed
7. Feedback and Improvement Encourage stakeholders to provide feedback for refinement. - Set up a feedback mechanism for continuous improvement. Feedback mechanism established
- Plan for periodic refresher training based on feedback. Refresher training plan developed
Training Plan
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11. Accelerated Decision-Making
Real-time insights enable
swift decision-making,
enhancing overall
business agility
Dynamic and Interactive
Reporting
Power BI implementation offers
dynamic, visually appealing
dashboards, fostering a more
engaging and insightful
reporting experience
Benefits And ROI
Cost Savings and Operational
Efficiency
Optimized ETL workflows
reduce costs and streamline
data processing, maximizing
operational efficiency
Improved Data Refresh
Timelines
Streamlined processes ensure
timely data updates, providing
up-to-the-minute information
for strategic planning
Empowered AI-Driven
Insights
Integrated AI technologies
unlock advanced analytics,
offering predictive insights for
informed decision-making
Enhanced User Productivity Future-Proofing and
Scalability
Competitive Edge and
Strategic Value
Enhanced Customer
Experience
Measurable Return on
Investment (ROI)
Faster query performance
and responsive reporting
empower users, boosting
overall productivity
Snowflake integration
provides a scalable and
future-ready architecture,
ensuring adaptability to
evolving business needs
AI integration positions
the organization at the
forefront, adding strategic
value and staying ahead in
the competitive landscape
Access to real-time
customer insights enables
personalized services,
improving overall
customer satisfaction
Reduced operational costs and
streamlined processes
Improved user productivity and
faster decision-making translate
into tangible returns.
AI-driven insights add strategic
value, providing a long-term return
on investment.
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12. Risk Assessment
Data Security and Privacy Concerns: Unauthorized access or data breaches during the
migration process.
1.
Mitigation: Implement robust security measures, encryption, and access controls. Conduct
thorough security audits.
Data Integrity Issues: Data corruption or loss during the migration process.
2.
Mitigation: Implement data validation checks, conduct pilot migrations, and maintain backups.
Integration Challenges: Compatibility issues between Snowflake, Power BI, and existing
systems.
3.
Mitigation: Thoroughly test integrations, involve vendor support, and have a contingency plan
for any unexpected issues.
4. ETL Refactoring Complexity: Challenges in refactoring existing ETL processes for Snowflake.
Mitigation: Conduct a detailed analysis of existing ETL workflows, involve ETL specialists, and
perform incremental refactoring.
5. User Resistance and Training Adoption: Resistance from users to adapt to new reporting tools
or AI integration.
Mitigation: Provide comprehensive training, communicate benefits clearly, and address user
concerns through change management.
6. Project Scope Creep: Expanding the project scope beyond the initial requirements.
Mitigation: Clearly define project scope, establish change control procedures, and obtain
stakeholder approvals for any scope changes.
7. Dependency on External Systems: Delays or issues arising from dependencies on external
systems or vendors.
Mitigation: Clearly define dependencies, communicate effectively with external partners, and
have contingency plans for potential delays.
8. Performance Issues in Production: Unforeseen performance bottlenecks or issues in the live
environment.
Mitigation: Conduct thorough performance testing, simulate real-world scenarios, and have
rollback plans in case of issues.
9. AI Model Accuracy and Interpretability: Challenges in achieving accurate AI model predictions or
difficulty in interpreting results.
Mitigation: Use high-quality training data, validate AI models rigorously, and involve domain
experts in interpreting results.
10. Lack of Stakeholder Involvement: Insufficient engagement and feedback from stakeholders.
Mitigation: Establish clear communication channels, conduct regular progress reviews, and
involve stakeholders in key decision-making processes.
11. Regulatory Compliance Issues: Failure to comply with data protection regulations during
migration.
Mitigation: Conduct a thorough compliance audit, ensure adherence to data protection laws, and
seek legal advice if needed.
12. Unforeseen Technical Challenges: Discovery of unexpected technical challenges during
implementation.
Mitigation: Conduct thorough technical assessments, engage with subject matter experts, and
be prepared with contingency plans.
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13. Resources And Budget
Project Manager
Data Migration
Specialist
Database Administrator
ETL Specialist
Reporting Specialist
AI Specialist
System Administrator
Training Specialist
Documentation
Specialist
Technical Support
Personnel
Snowflake subscription
or licensing costs
Power BI licensing
costs
Microsoft Azure
services (if applicable)
AI tools and
frameworks (e.g., Azure
Machine Learning,
TensorFlow)
Security and
monitoring tools
ETL tools (e.g.,
Informatica)
Development and
testing environments
Collaboration tools
(e.g., project
management software,
communication tools)
External training
programs for personnel
Documentation and
training material
development
Hardware for testing
and development
environments
Cloud infrastructure
costs (compute,
storage, etc.)
Reserve for unforeseen
circumstances or
additional
requirements
Personnel Costs:
$XXX,XXX
Snowflake
Subscription:
$XX,XXX
Power BI Licensing:
$XX,XXX
Azure Services:
$XX,XXX
Hardware and
Cloud Services:
$XX,XXX
Contingency
Reserve (10% of
Total Budget):
$X,XXX
External Training
Programs: $X,XXX
Documentation
Development:
$X,XXX
Software Costs:
$XXX,XXX
Personnel
Technology
Tools
And
Software
Infrastructure
Training
Contingency
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14. OUR TEAM
Alex Morgan
Data Migration Specialist
Jordan Taylor
Cloud Architecture Lead
Cameron Reed
ETL Optimization Expert
Riley Parker
AI Integration Strategist
15+ years executing 50+
flawless migrations.
Successfully led projects
in manufacturing,
finance, and healthcare
sectors.
Trained 200+ team
members globally.
Trusted by Fortune 500
clients.
18+ years optimizing
ETL for 15+ industry
accolades.
Scaled frameworks for
Fortune 100 giants.
Collaborated on 20+
global projects.
Trusted by leading
tech enterprises.
Pioneer with 10+ years
in AI.
Delivered patent-worthy
applications for retail
and logistics.
Aligned AI with revenue
goals, boosting profits
by 30%.
Improved models for
15+ satisfied clients.
Triple cloud-certified
with 12+ years.
Award-winning
architectures for 10+
global projects.
Optimized costs, saving
$2 million annually.
Trusted by top-tier
multinational clients.
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15. case studies
READ MORE... READ MORE... READ MORE...
In a transformative collaboration, Shoonya overhauled a
U.S. management consulting firm's revenue management
system. Tasked with harmonizing 1 million monthly billing
records, Shoonya utilized Microsoft Azure to centralize data
silos. Addressing decentralization challenges, incomplete
records, and manual workflows, Shoonya established a
centralized data platform, automating reporting. The result:
amplified financial reporting cycles, predictive analytics,
and standardized reporting. Shoonya’s Azure proficiency
maximized operational reporting, reducing risk, setting the
stage for a self-serving reporting platform. This success
underscores Shoonya’s prowess in transforming intricate
data landscapes, paving the way for data-driven business
excellence.
Cloud Optimization for Global Manufacturing
Powerhouse
Tasked with taming data sprawl across multiple global
sites, a Fortune 500 manufacturing giant partnered
with Shoonya for a cloud optimization overhaul.
Implementing Azure's robust suite, Shoonya seamlessly
centralized data from disparate sources, significantly
improving data accessibility and actionable insights.
The results were transformative: streamlined global
operations, a remarkable 30% reduction in operational
costs, and fortified data security. The manufacturing
powerhouse now thrives on real-time analytics, a
testament to Shoonya’s unparalleled expertise in
harnessing cloud solutions for a resilient and scalable
global expansion.
AI-Driven Customer Engagement Revolution in
E-commerce
In the competitive e-commerce arena, a leading
player collaborated with Shoonya to revolutionize
customer engagement through AI. Leveraging Azure's
advanced AI capabilities, Shoonya implemented a
dynamic personalized recommendation engine and an
efficient chatbot system. The outcome was nothing
short of remarkable: a substantial 20% surge in
customer satisfaction, a notable 15% increase in
sales conversion rates, and the seamless optimization
of customer support operations. This success story
underlines Shoonya’s exceptional skill in harnessing AI
for customer-centric solutions, elevating the e-
commerce experience to new heights.
Data Revolution: Shoonya's Azure-Led
Transformation in Management Consulting
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16. What Our Customers Say
The Data Engineering team has been crucial in cultivating a data-driven
culture within our organization by constructing robust pipelines,
automating workflows, and deploying advanced analytics tools. These
initiatives have fundamentally transformed our business operations. We
eagerly anticipate the exciting possibilities ahead as we continue on this
data-driven journey.
Director - Analytics
Industry: Life Sciences
The Salesforce Practice team has played a vital role in maximizing the
capabilities of Salesforce for us. Their platform expertise and skill in
tailoring solutions to our specific requirements have proven
invaluable. We appreciate their partnership and eagerly anticipate
ongoing collaboration.
Sr. Vice President
Industry: Digital Engineering Services
The invaluable contingent workforce support provided by the Shoonya team has
not only assisted us in scaling our business but has also significantly enhanced
our operational efficiency. Their proactive approach to identifying and
onboarding top-tier professionals has streamlined our talent acquisition
process, ensuring that we have the right people in place to meet the dynamic
demands of our industry.
Moreover, the collaborative synergy with Shoonya's team has not only met but
exceeded our expectations. Their commitment to understanding our unique
business needs has resulted in a tailored approach, fostering a seamless
integration of their professionals into our organizational culture.
As we look ahead, we are not just optimistic but enthusiastic about the positive
impact that the continued partnership with Shoonya's talented professionals will
have on our company's trajectory. With their support, we anticipate not only
achieving our current goals but also unlocking new opportunities for innovation
and sustained success.
Sr. Manager Talent Acquisition
Industry: Retail & E-Commerce
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Partners
HOONYA

18. Disclaimer
This record comprises confidential data and is meant solely for the
privileged utilization by SHOONYA. Every detail enclosed herein must be
treated with discretion and is prohibited from being shared with any
external entity without the explicit written approval from SHOONYA.
Unauthorized duplication will be deemed a violation of copyright.
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19. THANK YOU
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https://shoonya.co
sales@shoonya.co
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