Description

This curriculum spans the technical and operational rigor of a multi-workshop program for data platform teams, covering the design, governance, and lifecycle management of enterprise data systems at the scale of long-term advisory engagements in regulated, petabyte-scale environments.

Module 1: Strategic Data Infrastructure Planning

Selecting between cloud-native data lakehouses and hybrid on-premises architectures based on regulatory constraints and latency requirements.
Negotiating SLAs with cloud providers for data egress, compute burst capacity, and backup retention policies.
Evaluating data gravity implications when colocating analytics workloads with storage tiers.
Defining data ownership models across business units to prevent siloed ingestion pipelines.
Implementing infrastructure-as-code templates for reproducible data environments across regions.
Designing multi-zone failover strategies for mission-critical data ingestion services.
Assessing total cost of ownership for managed vs. self-hosted streaming platforms.
Establishing capacity forecasting processes for petabyte-scale growth over 18-month horizons.

Module 2: Real-time Data Pipeline Engineering

Choosing between Apache Kafka, Pulsar, or Kinesis based on message ordering guarantees and consumer backpressure tolerance.
Configuring exactly-once semantics in Flink pipelines with idempotent sinks and checkpoint alignment.
Implementing schema evolution strategies using Schema Registry with backward compatibility checks.
Designing dead-letter queue routing with automated alerting for malformed records.
Balancing throughput and latency in microbatch processing windows under variable load.
Instrumenting end-to-end latency tracing across distributed pipeline stages using OpenTelemetry.
Enforcing rate limiting at ingestion APIs to prevent downstream system saturation.
Managing consumer group rebalancing storms during rolling deployments.

Module 3: Enterprise Data Governance Frameworks

Implementing column-level lineage tracking across ETL transformations using OpenLineage.
Enforcing data classification policies through automated PII detection and tagging at rest and in motion.
Configuring role-based access control for data products with attribute-based overrides.
Integrating data quality rules into CI/CD pipelines with automated test gate enforcement.
Establishing data stewardship workflows with audit trails for schema change approvals.
Mapping data processing activities to GDPR Article 30 recordkeeping requirements.
Deploying data retention policies with automated archival and deletion triggers.
Conducting quarterly access certification reviews for high-sensitivity datasets.

Module 4: Scalable Data Modeling and Storage

Choosing between Delta Lake, Iceberg, and Hudi based on time travel frequency and merge performance.
Designing partitioning and clustering strategies to minimize query scan costs on cloud data warehouses.
Implementing Z-Order indexing for multi-dimensional query optimization on large fact tables.
Managing file size and count in object storage to avoid listing performance degradation.
Defining schema change protocols for backward and forward compatibility in shared tables.
Optimizing Parquet compression and page size settings for analytical versus transactional access patterns.
Implementing soft deletes with tombstone markers in immutable storage layers.
Designing slowly changing dimension strategies for SCD Type 2 in streaming environments.

Module 5: Advanced Analytics and ML Integration

Versioning training datasets using data catalog snapshots for reproducible model training.
Implementing feature store consistency across batch and real-time serving environments.
Designing model monitoring pipelines for data drift detection using statistical process control.
Managing compute isolation between interactive analytics and model training workloads.
Integrating model explainability outputs into business decision dashboards.
Orchestrating retraining pipelines triggered by data quality or performance degradation thresholds.
Securing model artifact storage with signed URLs and short-lived credentials.
Implementing A/B test routing at inference time with consistent user assignment.

Module 6: Cloud Cost Management and Optimization

Right-sizing warehouse clusters based on historical query concurrency and memory utilization.
Implementing auto-pause and auto-resume policies for development and staging environments.
Applying query tagging to attribute costs to business units and chargeback models.
Optimizing data placement between hot, cool, and archive storage tiers.
Negotiating committed use discounts for predictable workloads with cloud providers.
Identifying and eliminating orphaned tables and unused views in data catalogs.
Enforcing query timeouts and result limits to prevent runaway costs.
Conducting quarterly cost anomaly reviews using cloud billing APIs and custom dashboards.

Module 7: Data Platform Security Architecture

Implementing end-to-end encryption for data in transit using mTLS between pipeline components.
Managing credential rotation for service accounts with automated key cycling.
Enforcing VPC service controls to prevent data exfiltration to external endpoints.
Configuring audit logging for all data access events with immutable log storage.
Implementing dynamic data masking for sensitive fields in reporting interfaces.
Validating third-party data connectors for security compliance before integration.
Designing zero-trust access models for data platforms using short-lived tokens.
Conducting penetration testing on public-facing data APIs with red team exercises.

Module 8: Cross-functional Data Operations

Establishing incident response playbooks for data pipeline outages with escalation paths.
Implementing canary deployments for schema changes with automated rollback triggers.
Coordinating data migration windows with business stakeholders to minimize disruption.
Managing technical debt in legacy pipelines through incremental refactoring sprints.
Standardizing monitoring dashboards across teams using shared metric definitions.
Conducting blameless postmortems for data quality incidents with action tracking.
Integrating data platform alerts into centralized incident management systems.
Developing runbooks for routine operational tasks like compaction and vacuuming.

Module 9: Data Product Lifecycle Management

Defining SLAs for data freshness, availability, and accuracy for each data product.
Implementing consumer feedback loops for data product usability and reliability.
Managing version deprecation schedules for backward-incompatible data APIs.
Documenting data product contracts with schema, SLA, and ownership metadata.
Conducting quarterly data product health assessments using usage and quality metrics.
Designing self-service onboarding for new consumers with sandbox environments.
Establishing data product retirement processes with consumer notification timelines.
Integrating data product discovery into enterprise search and metadata portals.