Description

This curriculum spans the full lifecycle of enterprise data migration to the cloud, equivalent in depth to a multi-workshop technical advisory program, covering assessment, architecture, extraction, transformation, secure transfer, loading, validation, decommissioning, and governance across complex, regulated environments.

Module 1: Assessing Source Systems and Data Inventory

Conduct schema analysis across heterogeneous databases (e.g., Oracle, SQL Server, legacy flat files) to identify data types incompatible with target cloud platforms.
Map ownership and stewardship of data assets across business units to resolve ambiguity in data governance accountability.
Classify data based on sensitivity (PII, PHI, financial) to determine compliance requirements and migration handling protocols.
Document dependencies between applications and data sources to prevent breaking integrations during cutover.
Quantify data volume and growth rates per system to project cloud storage costs and transfer timelines.
Identify redundant, obsolete, or trivial (ROT) data for archival or deletion prior to migration to reduce scope.
Validate data lineage for critical reporting tables to ensure downstream analytics remain accurate post-migration.
Engage application owners to confirm uptime windows and data freeze periods during extraction.

Module 2: Defining Migration Strategy and Target Architecture

Select between rehost, refactor, or rebuild approaches based on source system technical debt and long-term cloud roadmap alignment.
Choose target cloud data services (e.g., BigQuery, Redshift, Synapse) based on query patterns, concurrency needs, and existing skill sets.
Determine whether to use cloud-native ETL tools (e.g., AWS Glue, Azure Data Factory) or retain on-premises ETL infrastructure temporarily.
Design data partitioning and clustering strategies in the target environment to optimize query performance and cost.
Decide between batch, near-real-time, or continuous replication based on business tolerance for data latency.
Establish naming conventions and metadata standards consistent with enterprise data governance policies.
Define data residency requirements and select cloud regions accordingly to meet legal and regulatory mandates.
Plan for hybrid connectivity (e.g., ExpressRoute, Direct Connect) to support phased migration and coexistence.

Module 3: Data Extraction and Pre-Migration Validation

Develop extraction scripts that handle large LOBs and binary data without memory overflow or timeout errors.
Implement change data capture (CDC) mechanisms for high-velocity transactional systems to minimize data drift.
Encrypt data at rest and in transit during extraction to prevent exposure on untrusted networks.
Validate row counts, checksums, and aggregate metrics between source and extracted datasets to confirm completeness.
Handle time zone and timestamp normalization when migrating data from globally distributed systems.
Address character encoding mismatches (e.g., EBCDIC to UTF-8) to prevent data corruption.
Log extraction failures and retries with sufficient context for root cause analysis and audit trails.
Coordinate with DBAs to schedule extraction during off-peak hours to avoid performance degradation.

Module 4: Data Transformation and Cleansing

Standardize address formats, phone numbers, and email addresses using rule-based and probabilistic matching.
Resolve duplicate records across source systems using deterministic and fuzzy matching algorithms.
Reconcile conflicting business definitions (e.g., “active customer”) across departments prior to transformation.
Map legacy codes and deprecated classifications to modern taxonomies used in the target system.
Apply data masking or tokenization to sensitive fields during transformation for non-production environments.
Handle null values and default logic consistently to prevent misinterpretation in analytics.
Preserve audit fields (created_by, updated_at) during transformation to maintain data provenance.
Document transformation logic in executable code (e.g., SQL, PySpark) for reproducibility and version control.

Module 5: Secure Data Transfer and Landing

Configure secure file transfer protocols (SFTP, HTTPS) with mutual TLS for data movement to cloud storage.
Use temporary, time-bound credentials with least-privilege access for transfer processes.
Validate data integrity upon landing using hash comparisons between source and destination files.
Implement server-side encryption (SSE-S3, SSE-KMS) on cloud storage buckets immediately upon data arrival.
Monitor transfer throughput and latency to detect network bottlenecks or throttling.
Set up automated alerts for failed transfers or incomplete file uploads.
Quarantine incoming data in a staging zone before promoting to curated layers for quality checks.
Enforce retention policies on landing zones to automatically purge stale or failed transfers.

Module 6: Data Loading and Schema Alignment

Design idempotent load processes to allow safe re-runs without duplicating records.
Handle schema evolution by implementing versioned schemas or schema-on-read patterns.
Partition large tables by date or region to optimize load parallelism and query efficiency.
Validate referential integrity after load, especially when migrating normalized databases to denormalized targets.
Index critical columns post-load to support query performance without slowing ingestion.
Manage auto-increment key conflicts when merging data from multiple source databases.
Load slowly changing dimensions (SCD Type 2) with effective date logic to preserve historical accuracy.
Log load durations and row counts per table for performance benchmarking and SLA tracking.

Module 7: Post-Migration Validation and Reconciliation

Run automated reconciliation scripts to compare record counts, sums, and unique key distributions.
Validate business KPIs (e.g., monthly revenue, active users) in source and target systems for consistency.
Engage business stakeholders to sign off on sample data sets for accuracy and usability.
Compare query results from legacy and cloud reports to detect logic or data discrepancies.
Verify that all indexes, constraints, and triggers are correctly implemented in the target.
Test backup and restore procedures on migrated databases to confirm operational readiness.
Conduct performance testing under expected concurrency loads to identify bottlenecks.
Document variances and resolution actions for audit and future migration waves.

Module 8: Decommissioning and Operational Transition

Establish a data freeze and cut-over timeline with application owners and business units.
Redirect applications and reports to the new cloud endpoints using DNS or configuration updates.
Monitor data drift post-cutover to confirm no residual writes are occurring on source systems.
Archive source databases with retention tags and access controls before decommissioning.
Update data catalog entries and business glossaries to reflect new system of record locations.
Transfer ownership of data pipelines and monitoring to cloud operations teams.
Disable network access and credentials to decommissioned systems to reduce attack surface.
Conduct a post-mortem to capture lessons learned and refine migration playbooks.

Module 9: Governance, Monitoring, and Continuous Improvement

Implement data quality rules (completeness, validity, consistency) with automated monitoring and dashboards.
Set up alerts for anomalies in data volume, freshness, or pipeline execution failures.
Integrate lineage tracking tools to map data flow from source to consumption layers.
Enforce data access policies using cloud IAM roles and attribute-based access controls (ABAC).
Conduct periodic access reviews to remove orphaned or excessive permissions.
Measure and report on data migration ROI using metrics like downtime, error rates, and cost per GB.
Standardize pipeline deployment using CI/CD practices with rollback capabilities.
Update disaster recovery and business continuity plans to reflect new cloud data architecture.