Description

This curriculum spans the technical and governance workflows typical of a multi-phase cloud migration program, covering data assessment, pipeline design, and post-go-live monitoring as seen in large-scale data modernization engagements.

Module 1: Assessing Data Quality Pre-Migration

Define data quality thresholds for completeness, accuracy, and consistency using source system profiling metrics.
Identify orphaned records and referential integrity violations in legacy databases before initiating cloud ETL pipelines.
Quantify duplication rates across customer, product, and transaction tables using fuzzy matching algorithms on production data.
Map data lineage from operational systems to downstream reporting tools to isolate high-impact data sets for cleansing prioritization.
Collaborate with business stakeholders to classify data elements by criticality and sensitivity for tiered cleansing approaches.
Document data anomalies in schema definitions, such as inconsistent date formats or null handling in key fields.
Establish baselines for data quality KPIs to measure cleansing efficacy before and after migration.

Module 2: Designing Cloud-Native Data Ingestion Pipelines

Select ingestion patterns (batch, micro-batch, or streaming) based on source system capabilities and target data freshness requirements.
Configure secure, encrypted connections between on-premise databases and cloud storage using private endpoints or VPC peering.
Implement schema-on-read strategies in data lakes using Parquet or ORC formats with partitioning optimized for query performance.
Embed data validation rules at ingestion to reject or quarantine records that fail type, range, or format checks.
Design idempotent ingestion workflows to support reprocessing without introducing duplicates during pipeline failures.
Integrate metadata extraction tools to capture source system schema versions and timestamps during each load cycle.
Apply data masking or tokenization during ingestion for PII fields to comply with data residency policies.

Module 3: Schema Harmonization and Standardization

Resolve conflicting business definitions (e.g., “active customer”) across departments through cross-functional data governance sessions.
Reconcile disparate naming conventions (e.g., Cust_ID vs. CustomerID) using a centralized data dictionary and transformation rules.
Standardize date, currency, and unit-of-measure formats across source systems using canonical reference tables.
Map heterogeneous product categorization systems to a unified taxonomy before merging datasets.
Handle missing or ambiguous codes in legacy lookup tables by implementing fallback logic or manual curation workflows.
Design schema evolution strategies in cloud data warehouses to accommodate future field additions without breaking pipelines.
Validate referential integrity between parent and child entities after merging data from multiple ERP systems.

Module 4: Duplicate Detection and Record Linkage

Configure probabilistic matching algorithms (e.g., Fellegi-Sunter) with tuned thresholds to balance precision and recall.
Use blocking strategies (e.g., phonetic hashing on name fields) to reduce pairwise comparison load in large datasets.
Integrate deterministic rules (e.g., same SSN and address) with machine learning models to improve match accuracy.
Design survivorship rules to determine which attributes to retain when merging duplicate records (e.g., most recent, longest history).
Implement audit trails to log merge decisions for compliance and rollback capability.
Handle cross-system identity conflicts (e.g., same individual with different IDs in CRM and billing systems).
Deploy deduplication logic incrementally, starting with high-value entities like customers and suppliers.

Module 5: Data Validation and Rule Enforcement

Develop domain-specific validation rules (e.g., order amount > 0, country code in ISO list) within pipeline orchestration tools.
Integrate data quality rules with CI/CD pipelines to prevent deployment of flawed transformations to production.
Configure alerting mechanisms for data quality rule breaches using cloud-native monitoring and logging services.
Implement data reconciliation checks between source and target systems post-load to detect data loss or corruption.
Use statistical profiling to detect outliers and anomalies in numerical fields post-cleansing.
Define and enforce data type consistency across environments (e.g., VARCHAR length limits in cloud warehouse).
Apply conditional validation logic based on business context (e.g., required fields vary by customer type).

Module 6: Governance and Metadata Management

Establish ownership and stewardship roles for critical data elements in a cloud-based data catalog.
Automate metadata tagging during pipeline execution to track data origin, transformations, and business definitions.
Implement access control policies on sensitive data assets using attribute-based or role-based permissions.
Document data cleansing rules and decisions in a searchable knowledge repository for audit purposes.
Enforce data retention and archival policies in cloud storage to align with legal and regulatory requirements.
Integrate data quality metrics into executive dashboards to maintain visibility across business units.
Conduct periodic data governance reviews to update policies based on evolving business needs.

Module 7: Scalable Cleansing Infrastructure in the Cloud

Select serverless compute options (e.g., AWS Glue, Azure Databricks) based on data volume and processing complexity.
Optimize cluster configurations for memory-intensive cleansing tasks like fuzzy matching on large tables.
Implement auto-scaling policies for data processing jobs to handle peak migration workloads.
Use caching mechanisms for reference data (e.g., country codes, product hierarchies) to reduce I/O latency.
Partition and index cloud data warehouse tables to accelerate cleansing and validation queries.
Monitor and optimize data transfer costs between storage and compute layers in multi-region deployments.
Design fault-tolerant workflows with retry logic and dead-letter queues for failed record processing.

Module 8: Post-Migration Validation and Continuous Monitoring

Execute reconciliation reports comparing key aggregates (e.g., total customers, revenue) pre- and post-migration.
Deploy automated data quality monitors to detect regressions in cleansed datasets after go-live.
Establish SLAs for data refresh cycles and cleansing job completion in production environments.
Conduct root cause analysis on recurring data issues to refine cleansing logic and prevent recurrence.
Integrate feedback loops from business users to identify residual data quality issues in reporting.
Implement version control for data cleansing scripts to support rollback and auditability.
Transition from one-time migration cleansing to ongoing data quality operations in the cloud environment.