Multi Cloud Adoption

🧭 Theme: “Driving Cloud Adoption Across AWS, GCP & On-Prem (Hybrid Model) for BFSI”

Q1. How do you decide which workloads should remain on-prem vs move to AWS or GCP?

Step-by-Step Answer:

1. Inventory & Classification

   • List all workloads: CBS, payments, risk, AML, KYC, data warehouse, APIs.

   • Tag by criticality, sensitivity, integration dependency.

2. Regulatory Assessment

   • RBI/SEBI guidelines restrict PII or core ledger data leaving India.

   • Hence, core banking ledger & PII master DB → on-prem/private cloud (India DC).

3. Technical Fitment

   • High elasticity workloads (e.g., batch reporting, fraud ML) → public cloud.

   • Latency-critical systems (e.g., CBS APIs) → on-prem or near-edge.

4. Workload Placement Matrix

Workload	Data Sensitivity	Latency	Cloud Fit	Deployment
Core Banking	High	Low	No	On-Prem
Loan Origination	Medium	Medium	Yes	Azure India Cloud
Fraud Analytics	Medium	Medium	Yes	GCP India Region
Marketing Data	Low	High	Yes	AWS Singapore Region

5. Governance

   • Document placement rationale in EA repository.

   • Review quarterly with Risk & Compliance board.

Q2. How do you enable secure connectivity between on-prem CBS and cloud-native lending apps?

Step-by-Step Answer:

1. Identify Integration Need

   • Lending microservices on Azure must create customer loan accounts in CBS.

   • Integration is asynchronous via Kafka or REST APIs.

2. Network Connectivity

   • Setup ExpressRoute / Direct Connect / VPN between bank DC and Azure VNet.

   • Extend private IPs via VNet peering or SD-WAN.

3. Integration Pattern

   • Publish “LoanCreated” event in Azure Kafka → replicate to on-prem Kafka cluster.

   • CBS consumes event locally → triggers Finacle Adapter.

4. Security

   • mTLS, firewall whitelisting, IP-based access.

   • All data encrypted in transit (TLS 1.3) and at rest.

5. Monitoring

   • Hybrid Observability: Azure Monitor + Splunk.

   • Track latency, event lag, error counts.

Q3. How is data moved securely from on-prem to cloud for ML model training?

Step-by-Step Answer:

1. Data Classification

   • PII and confidential data stay on-prem.

   • Only derived / tokenized / anonymized data moves to cloud.

2. Data Preparation

   • Use on-prem DWH to generate anonymized training dataset.

   • Strip PII and store metadata mapping locally.

3. Data Transfer

   • Use Azure Data Factory / GCP Data Transfer / AWS DataSync.

   • Transfer over private ExpressRoute or VPN.

4. Storage on Cloud

   • Data lands in cloud-native storage (S3, GCS, or ADLS).

   • Encrypted with CMK (Customer Managed Key).

5. ML Model Lifecycle

   • Model trained in cloud.

   • Model artifact (.pkl) moved back on-prem for inferencing (due to RBI compliance).

Q4. What’s your approach to designing a multi-cloud strategy for Deutsche Bank?

Step-by-Step Answer:

1. Define Drivers

   • Avoid vendor lock-in.

   • Leverage best-of-breed services (e.g., GCP AI, AWS analytics).

   • Regulatory segmentation across geographies.

2. Architectural Layers

   • Use Kubernetes + Service Mesh (Istio) across clouds.

   • Abstract platform with Terraform + GitOps for standardization.

3. Data Governance

   • Data classified and segmented per region.

   • Tokenization or data virtualization used where cross-cloud sharing is required.

4. Inter-Cloud Connectivity

   • Private interconnect (AWS ↔ GCP via Equinix).

   • Global DNS + API Gateway federation.

5. Centralized Governance

   • Unified monitoring, FinOps dashboard, and compliance scanning.

Q5. How would you manage deployment across on-prem and multi-cloud environments?

Step-by-Step Answer:

1. Common CI/CD Platform

   • Azure DevOps or Jenkins deployed centrally.

   • Multi-agent runners (one per environment).

2. Infrastructure as Code

   • Terraform for provisioning.

   • Separate state files per environment (on-prem, AWS, GCP).

3. Artifact Repository

   • Central Nexus/Artifactory accessible from all environments.

4. Deployment Strategy

   • Blue-green or canary deployments.

   • Helm for Kubernetes workloads.

5. Compliance Validation

   • Pre-deployment security scanning (SonarQube, Checkov).

   • Automated audit trail storage in central repository.

Q6. What’s your strategy for enforcing architecture governance across AWS, GCP, and on-prem?

Step-by-Step Answer:

1. Define Cloud Reference Architectures

   • Cloud-native blueprints for API, event-driven, and data workloads.

   • Standardized templates with security controls.

2. Architecture Review Boards

   • Monthly clinics with app teams.

   • Validate new designs against standards.

3. Policies as Code

   • OPA, Azure Policy, AWS Config for continuous compliance.

4. Central EA Repository

   • Maintain approved patterns and reusable components.

5. KPI Tracking

   • Compliance % by team, exceptions, cost deviations.

Q7. How do you ensure regulatory compliance across multi-cloud deployments?

Step-by-Step Answer:

1. Region Selection

   • Only India-region cloud for workloads with PII.

   • Segregate global workloads (Singapore, Frankfurt) for non-sensitive use cases.

2. Data Residency

   • PII stored locally, tokenized data for analytics moved across regions.

3. Audit & Logging

   • Unified log aggregation with retention policy as per RBI/SEBI norms.

4. Security Controls

   • Encryption (KMS, HSM-backed CMK).

   • Cloud-native IAM + centralized AD integration.

5. Continuous Monitoring

   • Automated posture checks via CSPM (Prisma, Defender for Cloud).

Q8. How do you handle failover and DR across multi-cloud and hybrid systems?

Step-by-Step Answer:

1. Criticality Assessment

   • Classify workloads by RPO/RTO.

2. DR Setup

   • Active-Active across cloud regions or between cloud and on-prem.

   • For CBS (on-prem), maintain hot standby in secondary DC.

3. Cross-Cloud Replication

   • Use object replication between S3 ↔ GCS.

   • Kafka MirrorMaker for event replication.

4. Failover Automation

   • DNS-based routing via Traffic Manager / Cloudflare.

5. Regular DR Drills

   • Quarterly DR testing and certification.

Q9. How do you enable observability and FinOps across multi-cloud hybrid environments?

Step-by-Step Answer:

1. Observability

   • Central monitoring stack (Grafana, Prometheus, ELK).

   • Unified dashboards pulling metrics via cloud APIs.

2. Tracing

   • OpenTelemetry agents across all services.

   • Correlate traces across cloud boundaries.

3. FinOps

   • Cloud cost data ingestion from AWS, GCP billing APIs.

   • AI-based cost anomaly detection.

4. Chargeback

   • Tag resources by BU/project.

   • Monthly reports to CFO/CTO.

Q10. How do you measure success of your cloud adoption strategy?

Step-by-Step Answer:

1. Define KPIs

   • % workloads modernized.

   • Cloud cost optimization (vs baseline).

   • Reduction in time-to-market for new services.

   • Compliance adherence rate.

2. Track Business Impact

   • Revenue growth from digital channels.

   • Improved customer experience metrics.

3. Operational Efficiency

   • Fewer incidents, improved MTTR.

4. Cultural Adoption

   • Cloud skill maturity index for teams.

5. Quarterly Review

   • Present EA metrics to CTO & Cloud Steering Committee.

🧩 Summary: Enterprise Architect’s Cloud Adoption Blueprint

Dimension	Focus Area	EA Action
Strategy	Cloud-first + hybrid where needed	Define roadmap, principles
Architecture	Multi-cloud reference patterns	Build reusable blueprints
Connectivity	Hybrid secure channels	VPN/ExpressRoute
Security	End-to-end encryption, IAM	Enforce via policies
Governance	Architecture reviews	Continuous compliance
FinOps	Cost optimization	Unified monitoring
People	Cloud training & alignment	Cloud CoE, guilds