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Database Migration in Cloud Migration

Database Migration in Cloud Migration

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This curriculum spans the technical and operational rigor of a multi-workshop cloud migration program, addressing the same depth of planning, execution, and governance tasks typically handled by dedicated database migration teams in large-scale infrastructure modernization efforts.

Module 1: Assessing Source Database Landscape and Readiness

  • Inventory legacy database instances by version, patch level, and dependencies to identify unsupported or end-of-life systems.
  • Evaluate custom extensions, stored procedures, and triggers for compatibility with target cloud database engines.
  • Map data sensitivity classifications across databases to determine regulatory and compliance implications for migration.
  • Profile database workloads using performance baselines to distinguish OLTP, OLAP, and hybrid usage patterns.
  • Identify databases with embedded IP or undocumented business logic that require reverse engineering before migration.
  • Determine ownership and stewardship for each database to establish accountability during migration planning.
  • Assess replication and log-shipping configurations that may interfere with migration cutover timelines.

Module 2: Defining Migration Strategy and Target Architecture

  • Select between rehost (lift-and-shift), refactor (schema conversion), or rebuild (application redesign) based on TCO and technical debt.
  • Choose between managed database services (e.g., RDS, Cloud SQL) and self-managed VM-based deployments based on operational overhead tolerance.
  • Determine target database engine (e.g., PostgreSQL vs. MySQL vs. proprietary) based on feature parity and licensing constraints.
  • Decide on single-tenant vs. multi-tenant database deployment models based on isolation requirements and cost efficiency.
  • Design high availability at the database layer using regional failover, read replicas, or clustering technologies.
  • Plan for cross-region disaster recovery by evaluating asynchronous vs. synchronous replication trade-offs.
  • Integrate database tier into overall cloud network architecture, including VPC peering and subnet placement.

Module 3: Data Transfer and Migration Tooling

  • Select appropriate migration tools (e.g., AWS DMS, Azure Data Factory, Google Database Migration Service) based on source-target compatibility.
  • Configure change data capture (CDC) mechanisms to minimize downtime during cutover for large databases.
  • Implement data validation scripts to verify row counts, checksums, and referential integrity post-migration.
  • Optimize network throughput by tuning batch sizes, parallelization, and compression settings during data transfer.
  • Handle large object (LOB) data types by evaluating in-line transfer vs. external storage offload strategies.
  • Manage migration of databases with active long-running transactions that may block consistent snapshots.
  • Stage data in intermediate storage (e.g., Parquet in cloud storage) when direct connectivity is restricted by firewalls or policies.

Module 4: Schema Conversion and Code Refactoring

  • Convert proprietary SQL dialects (e.g., T-SQL, PL/SQL) to target engine syntax using automated tools and manual review.
  • Refactor identity columns and sequences to accommodate differences in auto-increment behavior across platforms.
  • Adapt indexing strategies to align with target database optimizer requirements and query patterns.
  • Modify partitioning schemes when source range/hash partitioning lacks native support in the target.
  • Rewrite database-linked server queries as application-level joins or ETL pipelines when cross-database access is restricted.
  • Replace deprecated data types (e.g., TIMESTAMP WITHOUT TIME ZONE) to ensure time zone consistency in distributed systems.
  • Update application connection strings and ORM configurations to reflect new schema object names and constraints.

Module 5: Security, Access, and Compliance

  • Reconcile on-premises AD/LDAP integrations with cloud identity providers using federation or hybrid identity solutions.
  • Implement encryption at rest using cloud KMS-managed keys and enforce customer-managed key policies.
  • Enforce TLS 1.2+ for all client connections and disable legacy cipher suites in database endpoints.
  • Apply principle of least privilege by converting broad database roles into granular IAM and database-level grants.
  • Audit data access patterns in the cloud using native logging (e.g., CloudTrail, Audit Logs) and SIEM integration.
  • Mask or anonymize sensitive data in non-production environments using dynamic data masking or synthetic data generation.
  • Validate compliance with regulations (e.g., GDPR, HIPAA) by documenting data residency and retention controls.

Module 6: Performance Optimization and Scalability

  • Right-size compute and memory for database instances based on historical CPU, IOPS, and memory pressure metrics.
  • Tune buffer pool, query cache, and connection pooling parameters to match cloud instance characteristics.
  • Implement connection multiplexing or proxy layers (e.g., PgBouncer) to prevent connection exhaustion in serverless environments.
  • Optimize storage performance by selecting provisioned IOPS, SSD, or NVMe-backed volumes based on workload demands.
  • Monitor query execution plans post-migration to identify performance regressions due to statistics or index changes.
  • Scale read capacity using read replicas and distribute application traffic via DNS or load balancer routing.
  • Plan for auto-scaling strategies that respond to sustained load rather than transient spikes to avoid thrashing.

Module 7: Cutover Planning and Downtime Management

  • Define cutover window based on business impact analysis and stakeholder approval for service interruption.
  • Coordinate application deployment freeze with database cutover to prevent data divergence during migration.
  • Execute final data sync using CDC and validate lag before promoting the target database to primary.
  • Update DNS or service discovery records to redirect applications to the new database endpoint.
  • Implement rollback procedures including reverting DNS, re-enabling source database writes, and data reconciliation.
  • Notify downstream consumers (e.g., reporting, ETL) of endpoint changes and schedule refresh cycles post-cutover.
  • Monitor application error rates and transaction latency immediately after cutover to detect integration issues.

Module 8: Post-Migration Operations and Governance

  • Decommission legacy databases only after confirming no residual dependencies or scheduled jobs.
  • Establish monitoring dashboards for key metrics: replication lag, storage growth, and failed login attempts.
  • Implement automated backup and restore testing using cloud-native snapshot and point-in-time recovery.
  • Enforce tagging standards for cost allocation and resource ownership tracking across database instances.
  • Rotate credentials and regenerate access keys used during migration to reduce standing privileges.
  • Document lessons learned and update migration runbooks for future database modernization initiatives.
  • Conduct periodic license reviews to avoid non-compliance with proprietary database software in cloud environments.
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