Description

This curriculum spans the technical and operational rigor of a multi-workshop cloud migration program, covering the same depth of planning, execution, and governance tasks typically addressed in enterprise advisory engagements for database modernization.

Module 1: Assessing On-Premises Database Workloads for Cloud Migration

Evaluate transactional throughput and latency requirements to determine suitability for cloud-hosted database services.
Inventory existing database dependencies, including ETL pipelines, reporting tools, and application integrations.
Classify databases by criticality, compliance needs, and data sensitivity to prioritize migration sequencing.
Analyze peak usage patterns to identify candidates for reserved instances versus on-demand provisioning.
Document database schema complexity and stored procedure usage to assess refactoring effort.
Conduct stakeholder interviews to capture business continuity expectations during migration cutover.
Map existing backup and recovery SLAs to cloud-native capabilities and identify coverage gaps.

Module 2: Selecting Cloud Database Service Models (DBaaS, PaaS, IaaS)

Compare managed database service SLAs across cloud providers for uptime, patching, and failover automation.
Determine control requirements for database engine versioning and configuration tuning.
Assess licensing implications when migrating proprietary databases to cloud VMs versus managed services.
Decide between self-managed databases on VMs and fully managed services based on operational overhead tolerance.
Validate support for required extensions, plugins, or custom functions in managed environments.
Size IOPS and memory allocations for VM-hosted databases to avoid performance bottlenecks.
Evaluate cross-region replication options in managed services for disaster recovery readiness.

Module 3: Data Governance and Compliance in Cloud Environments

Implement data classification tagging at ingestion to enforce handling policies in cloud storage.
Configure encryption at rest using customer-managed keys for databases containing regulated data.
Define access control policies using attribute-based and role-based models aligned with least privilege.
Integrate database audit logs with SIEM systems to meet compliance monitoring requirements.
Establish data residency rules and enforce them through cloud provider region selection and tagging.
Document data processing agreements (DPAs) with cloud providers for GDPR and similar frameworks.
Conduct third-party penetration testing on database endpoints and review findings for remediation.

Module 4: Performance Optimization and Cost Management

Monitor query execution plans in cloud databases to identify inefficient joins or missing indexes.
Right-size database instances based on CPU, memory, and storage utilization trends over 30-day periods.
Implement connection pooling to reduce overhead from frequent open/close operations in serverless apps.
Use query caching mechanisms to reduce load on primary instances for read-heavy workloads.
Apply auto-scaling policies to read replicas based on predefined lag and throughput thresholds.
Tag database resources by department, project, and environment to allocate costs accurately.
Evaluate cost-benefit of provisioned IOPS versus burstable storage tiers for variable workloads.

Module 5: High Availability and Disaster Recovery Architecture

Design multi-AZ deployment strategies for synchronous replication and automatic failover.
Test RPO and RTO targets using simulated region outages and measure actual data loss and downtime.
Configure cross-region backups with immutable storage settings to protect against ransomware.
Implement health checks and DNS failover mechanisms for application-level continuity.
Document and rehearse manual failover procedures for systems without automated recovery.
Validate backup restoration processes quarterly and track recovery duration metrics.
Balance redundancy costs against business impact of downtime using risk modeling.

Module 6: Secure Database Connectivity and Network Configuration

Enforce encrypted connections (TLS 1.2+) between applications and database endpoints.
Restrict database access via VPC peering or private endpoints to prevent public exposure.
Implement database firewall rules to allow only known application server IP ranges.
Rotate SSL certificates and authentication credentials on a defined schedule.
Use short-lived tokens or IAM roles instead of static credentials for application access.
Monitor for anomalous access patterns, such as off-hours queries or large data exports.
Segment database tiers (production, staging, dev) using separate network zones and routing policies.

Module 7: Data Integration and Synchronization Across Hybrid Environments

Design CDC (Change Data Capture) pipelines to synchronize on-prem and cloud databases with minimal latency.
Select replication tools based on transactional integrity needs and schema evolution support.
Handle identity and sequence conflicts when merging data from multiple sources.
Implement idempotent processing logic to ensure reliability during replication retries.
Monitor replication lag and set alerts for thresholds that impact business operations.
Manage schema changes across environments using version-controlled migration scripts.
Validate data consistency using row counts, checksums, and sample record comparisons.

Module 8: Monitoring, Alerting, and Operational Observability

Deploy database performance monitoring agents to capture query latency, lock waits, and cache hit ratios.
Define alert thresholds for critical metrics such as connection exhaustion and storage utilization.
Correlate database metrics with application performance data to isolate root causes.
Centralize logs from all database instances using a structured ingestion pipeline.
Use anomaly detection to identify deviations from baseline behavior without manual threshold tuning.
Generate weekly performance reports highlighting top resource-consuming queries.
Integrate monitoring alerts with incident response workflows and on-call rotation systems.

Module 9: Managing Database Lifecycle and Schema Evolution

Implement version-controlled schema migrations using tools like Liquibase or Flyway.
Coordinate zero-downtime deployments by supporting backward-compatible schema changes.
Plan for data archiving and purging strategies to manage table growth and retention policies.
Enforce peer review and automated testing for all schema change scripts.
Track dependencies between microservices and shared databases to prevent breaking changes.
Use feature flags to decouple deployment of application code from database schema updates.
Retire deprecated columns and indexes after confirming no active dependencies.