Description

This curriculum spans the technical, operational, and governance dimensions of enterprise data migrations, comparable in scope to a multi-phase advisory engagement supporting large-scale data lake modernizations across hybrid environments.

Module 1: Assessing Source Systems and Data Landscape

Identify all operational databases, data warehouses, and legacy flat-file systems requiring inclusion in the migration scope based on business criticality and data lineage.
Document data ownership, update frequency, and SLAs for each source system to inform migration scheduling and downtime planning.
Evaluate data encoding formats (e.g., EBCDIC, UTF-16) and character set inconsistencies across sources to prevent corruption during extraction.
Map dependencies between source systems and downstream reporting or analytical platforms to prioritize migration sequences.
Conduct performance profiling of source queries to avoid overloading production systems during bulk extraction.
Negotiate access permissions with data stewards and IT operations, including read-only roles and audit logging requirements.
Classify data sensitivity levels (PII, PCI, PHI) across sources to enforce compliance controls early in the migration design.
Assess source system uptime windows and coordinate with business units to schedule extraction during off-peak hours.

Module 2: Designing Migration Architecture and Data Pipelines

Select between batch, micro-batch, or real-time ingestion patterns based on source system capabilities and target latency requirements.
Choose appropriate transport mechanisms (e.g., Sqoop, Kafka Connect, custom JDBC/ODBC scripts) based on source database type and network constraints.
Design staging layer schema in the target data lake to preserve raw source fidelity, including metadata like extraction timestamps and row-level source identifiers.
Implement retry logic and dead-letter queues in pipeline workflows to handle transient network or authentication failures.
Define partitioning and bucketing strategies in the target storage layer to optimize query performance and reduce scan costs.
Integrate pipeline monitoring hooks (e.g., Prometheus exporters, CloudWatch metrics) to track throughput, latency, and error rates.
Architect idempotent pipeline steps to enable safe reprocessing without data duplication.
Size cluster resources (CPU, memory, disk I/O) for ETL workers based on historical data volume and projected growth.

Module 3: Schema Mapping and Data Transformation

Resolve data type mismatches (e.g., Oracle NUMBER to Parquet DECIMAL) while preserving precision and scale.
Handle surrogate vs. natural key conflicts when merging data from multiple sources with inconsistent key strategies.
Implement type 2 slowly changing dimension logic for historical tracking in dimensional models during migration.
Standardize date, currency, and address formats across sources to ensure consistency in the target environment.
Apply data masking or tokenization rules during transformation for sensitive fields to meet compliance requirements.
Design transformation logic to handle schema drift, such as new columns or dropped fields, without pipeline failure.
Validate referential integrity between migrated fact and dimension tables post-transformation.
Log transformation decisions and exceptions for auditability and reconciliation with business stakeholders.

Module 4: Data Quality and Validation Frameworks

Define and automate row count reconciliation checks between source and target systems for each migration batch.
Implement null rate, value distribution, and uniqueness assertions to detect data corruption or truncation.
Set up field-level checksums (e.g., SHA-256 on concatenated key fields) to verify data integrity end-to-end.
Develop sampling strategies for manual validation of high-risk or complex transformations.
Integrate data profiling tools (e.g., Great Expectations, Deequ) into CI/CD pipelines for regression testing.
Establish thresholds for acceptable variance in aggregated metrics (e.g., sum, count) between source and target.
Document false positive cases in validation rules to refine thresholds and avoid alert fatigue.
Coordinate with business analysts to validate semantic accuracy of transformed business metrics.

Module 5: Performance Optimization and Scalability

Tune Spark executor memory and parallelism settings based on data skew and cluster node specifications.
Optimize file sizing in cloud storage (e.g., 128MB–1GB Parquet files) to balance query performance and metadata overhead.
Implement predicate pushdown and column pruning in extraction queries to reduce data movement.
Use broadcast joins judiciously for small dimension tables to avoid driver memory pressure.
Monitor shuffle spill to disk and adjust configurations to minimize I/O bottlenecks.
Precompute and store frequently used aggregations in materialized views to accelerate validation and reporting.
Partition large tables by time or region to enable efficient incremental processing and archival.
Conduct load testing with production-scale data volumes to identify pipeline bottlenecks before cutover.

Module 6: Security, Access Control, and Compliance

Enforce encryption in transit (TLS) and at rest (KMS-managed keys) for all data movement and storage layers.
Implement fine-grained access controls (e.g., Apache Ranger, AWS Lake Formation) based on role-based access policies.
Audit all data access and pipeline execution events for compliance with SOX, GDPR, or HIPAA requirements.
Mask or redact sensitive data in non-production environments used for migration testing.
Validate that personally identifiable information (PII) is logged or stored only in approved, secured zones.
Rotate credentials and API keys used in pipeline jobs on a scheduled basis with automated secret management.
Conduct third-party security scans on pipeline code and infrastructure as code templates.
Document data residency requirements and ensure target storage complies with geographic constraints.

Module 7: Change Management and Cutover Strategy

Develop a phased cutover plan with parallel run periods to validate target system accuracy before decommissioning sources.
Coordinate application reconfiguration timelines with development teams to switch from legacy to new data sources.
Implement feature toggles to enable rollback to source systems in case of critical data defects post-migration.
Freeze write operations on source systems during final delta sync to ensure point-in-time consistency.
Communicate data downtime windows to business users and support teams with precise start and end times.
Validate upstream and downstream dependencies (e.g., BI tools, ML models) against the migrated dataset before full cutover.
Archive source system snapshots for a defined retention period to support post-migration audits or rollbacks.
Update data catalog entries and lineage documentation to reflect new source locations and ownership.

Module 8: Post-Migration Operations and Monitoring

Deploy automated anomaly detection on data freshness, volume, and schema to alert on pipeline failures.
Establish SLAs for pipeline recovery time and data availability, and integrate with incident management systems.
Conduct root cause analysis on data discrepancies reported by business users and update transformation logic.
Optimize storage costs by implementing lifecycle policies to archive or delete stale data.
Refresh statistics and metadata in the metastore after large data loads to maintain query planner efficiency.
Review and refine pipeline performance quarterly based on usage patterns and data growth trends.
Document operational runbooks for common failure scenarios and assign on-call responsibilities.
Integrate data observability tools (e.g., Monte Carlo, DataDog) to monitor pipeline health and data quality trends.

Module 9: Governance, Metadata, and Long-Term Sustainability

Implement automated metadata extraction to capture technical lineage from source to target across all pipeline stages.
Standardize naming conventions and tagging policies for datasets, pipelines, and cloud resources.
Establish data ownership and stewardship roles for migrated datasets with documented escalation paths.
Integrate with enterprise data catalog tools to enable searchability and impact analysis.
Define retention and archival policies for raw, staged, and transformed data layers based on legal and business needs.
Conduct periodic data quality scorecard reviews with business units to prioritize improvement initiatives.
Version control all pipeline code, configuration, and schema definitions using Git with peer review workflows.
Plan for schema evolution by implementing schema registry tools (e.g., Confluent, AWS Glue Schema Registry).