Description

This curriculum spans the equivalent of a multi-workshop technical engagement, covering the end-to-end workflow of a cloud analytics migration as it would be executed across data assessment, architecture design, pipeline implementation, governance alignment, and operationalization in a large-scale enterprise environment.

Module 1: Assessing Data Readiness for Cloud Migration

Evaluate source system data quality by profiling completeness, consistency, and schema drift across operational databases and data warehouses.
Identify dependencies between legacy ETL pipelines and downstream reporting systems that may break during migration.
Classify data sensitivity levels to determine which datasets require masking, encryption, or air-gapped handling pre-migration.
Map existing data ownership and stewardship roles to cloud IAM policies and accountability frameworks.
Quantify data volume growth trends to project cloud storage requirements and cost implications over 24 months.
Document metadata lineage from source systems to current analytics outputs to preserve auditability post-migration.
Assess compatibility of existing data formats (e.g., COBOL copybooks, mainframe VSAM) with cloud ingestion tools.

Module 2: Designing Cloud-Native Data Architectures

Select between data lakehouse, data warehouse, and federated query models based on query performance SLAs and concurrency needs.
Define partitioning and clustering strategies in cloud storage (e.g., S3, ADLS) to optimize query cost and latency.
Implement medallion architecture with raw, cleansed, and curated layers using version-controlled DDL scripts.
Choose between batch, micro-batch, and streaming ingestion based on business latency requirements and source system capabilities.
Design schema evolution mechanisms using schema registries or Delta Lake to handle changing data structures.
Integrate data catalog tools (e.g., AWS Glue, Azure Purview) with CI/CD pipelines for automated metadata updates.
Architect cross-region replication for analytics workloads requiring disaster recovery or low-latency regional access.

Module 3: Data Ingestion and Pipeline Orchestration

Configure change data capture (CDC) from on-prem databases using tools like Debezium or native log shipping with latency monitoring.
Implement idempotent ingestion pipelines to handle retry scenarios without data duplication.
Orchestrate multi-source data loads using Airflow or Prefect with dependency-aware scheduling and alerting on SLA breaches.
Encrypt data in transit between on-prem systems and cloud ingestion endpoints using mutual TLS or IPsec tunnels.
Scale ingestion workers dynamically based on queue depth, balancing cost and throughput.
Validate payload structure and size at ingestion entry points to prevent pipeline failures downstream.
Log rejected records with context for root cause analysis and reprocessing workflows.

Module 4: Security, Compliance, and Data Governance

Enforce attribute-based access control (ABAC) on datasets using cloud-native policies synchronized with HR directories.
Implement dynamic data masking for PII fields in query results based on user role and data classification.
Configure audit logging for all data access and query activities, routing logs to a secured SIEM system.
Align data retention policies with legal holds and GDPR right-to-erasure obligations using automated tagging.
Conduct quarterly access certification reviews for high-sensitivity datasets using workflow-integrated tools.
Integrate data classification tools with DLP systems to detect and block unauthorized exfiltration attempts.
Negotiate data processing agreements (DPAs) with cloud providers covering sub-processor transparency and breach notification.

Module 5: Performance Optimization and Cost Management

Right-size compute clusters for analytics workloads using historical utilization metrics and auto-scaling policies.
Implement materialized views or aggregate tables for high-frequency queries to reduce scan costs.
Apply storage tiering policies (e.g., S3 Standard vs Glacier) based on data access frequency and recovery SLAs.
Monitor and alert on query cost outliers using tagging and chargeback models by team or project.
Optimize file formats and compression (e.g., Parquet with Z-Ordering) to reduce I/O and query duration.
Use workload management (WLM) rules to prioritize critical reporting queries over ad hoc exploration.
Conduct cost-benefit analysis of reserved capacity vs on-demand pricing for steady-state analytics workloads.

Module 6: Migration Cutover and Data Validation

Execute parallel run of legacy and cloud analytics systems to compare output consistency for key reports.

Develop automated reconciliation scripts to validate row counts, aggregates, and distribution metrics across environments.

Freeze legacy system writes during final cutover and verify completeness of last ingestion batch.

Implement blue-green deployment for analytics dashboards to minimize user disruption during switch.

Validate referential integrity across joined datasets post-migration, especially for slowly changing dimensions.

Document data gap analysis and resolution steps for any discrepancies found during validation.

Establish rollback procedures with time-bound decision gates if data quality thresholds are not met.

Module 7: Operational Monitoring and Incident Response

Define SLOs for pipeline latency, data freshness, and query response time with corresponding error budgets.
Deploy distributed tracing across ingestion, transformation, and serving layers to isolate failure points.
Integrate anomaly detection on data distributions to flag upstream source system issues.
Configure alerting thresholds that balance signal-to-noise ratio and operational urgency.
Establish runbooks for common failure scenarios, including credential expiration and quota limits.
Rotate service account credentials and secrets using automated vault integration and audit usage.
Conduct quarterly disaster recovery drills for analytics environments, measuring RTO and RPO.

Module 8: Continuous Improvement and Scaling Analytics

Instrument user query patterns to identify underutilized datasets for archival or decommissioning.
Refactor legacy SQL code for cloud data warehouse optimization (e.g., avoiding nested loops, leveraging CTEs).
Standardize data modeling patterns (e.g., dimensional, anchor modeling) across teams via shared templates.
Implement feature stores for ML pipelines to ensure consistency between training and inference data.
Integrate analytics outputs with business process systems (e.g., CRM, ERP) using secure APIs.
Evaluate adoption of serverless query engines for sporadic workloads to reduce idle costs.
Conduct technical debt assessments of data pipelines every six months to prioritize refactoring.