Description

This curriculum spans the technical and organizational complexity of a multi-workshop data platform modernization program, addressing the same architectural decisions, operational trade-offs, and cross-team coordination challenges encountered in large-scale data mesh and lakehouse implementations.

Module 1: Defining Data Ecosystem Architecture and Scope

Selecting between centralized, federated, or hybrid data architectures based on organizational maturity and regulatory constraints.
Mapping data ownership across business units to resolve conflicting stewardship models in multi-domain environments.
Establishing criteria for data product boundaries to prevent duplication and ensure interoperability across pipelines.
Integrating legacy data stores into modern ecosystems without disrupting operational reporting systems.
Assessing data gravity implications when deciding between cloud migration and on-premises retention.
Defining SLAs for data freshness and availability across source systems with varying update frequencies.
Aligning data domain boundaries with organizational structure to support data mesh implementation.
Documenting data lineage at the ecosystem level to support auditability and impact analysis.

Module 2: Ingestion Framework Design and Implementation

Choosing between batch, micro-batch, and streaming ingestion based on downstream latency requirements and source system capabilities.
Implementing change data capture (CDC) for transactional databases while managing log retention and performance overhead.
Designing schema evolution strategies for Avro or Protobuf in Kafka topics to support backward and forward compatibility.
Handling authentication and authorization when ingesting from third-party SaaS platforms with OAuth2 or API keys.
Configuring retry and backpressure mechanisms in ingestion pipelines to prevent data loss during downstream outages.
Validating data quality at ingestion points using schema conformance checks and null-value thresholds.
Partitioning and compressing ingested data to balance query performance and storage cost in data lakes.
Monitoring ingestion pipeline health through metrics such as lag, throughput, and error rates.

Module 3: Data Storage and Lakehouse Patterns

Selecting file formats (Parquet, ORC, Delta Lake) based on query patterns, update requirements, and compute engine compatibility.
Designing partitioning and bucketing strategies to optimize query performance for high-cardinality dimensions.
Implementing time-travel and versioning using Delta Lake or Apache Iceberg for audit and rollback capabilities.
Managing metadata tables and statistics to prevent query performance degradation over time.
Enforcing data retention and archival policies in compliance with legal and business requirements.
Securing access to storage layers using bucket policies, ACLs, and encryption at rest with customer-managed keys.
Optimizing storage tiering between hot, cold, and archive tiers based on access frequency and cost targets.
Handling schema drift in unstructured or semi-structured data during ingestion into structured lakehouse tables.

Module 4: Data Processing and Orchestration

Selecting processing engines (Spark, Flink, Beam) based on stateful processing needs and fault tolerance requirements.
Designing idempotent transformations to ensure reproducibility in the event of pipeline restarts.
Implementing dynamic resource allocation in Spark clusters to balance cost and execution time.
Orchestrating interdependent workflows using Airflow or Prefect with proper failure handling and alerting.
Managing dependencies between cross-domain data products using semantic versioning and contract testing.
Instrumenting processing jobs with custom metrics for monitoring skew, spill, and garbage collection.
Handling late-arriving data in windowed aggregations using watermarking and allowed lateness policies.
Validating output data distributions against expected baselines to detect processing anomalies.

Module 5: Metadata Management and Discovery

Integrating technical metadata from ingestion, processing, and storage systems into a centralized catalog.
Automating metadata extraction from ETL code and SQL scripts using parsing and tagging rules.
Implementing business glossary integration to link technical assets with business definitions and KPIs.
Configuring access controls on metadata to align with data classification and sensitivity policies.
Enabling full-text and faceted search across datasets using Elasticsearch or native catalog capabilities.
Tracking data lineage from raw sources to curated datasets, including transformation logic and ownership.
Using metadata to drive automated data quality rule generation based on historical anomaly patterns.
Managing metadata lifecycle to archive or deprecate datasets no longer in active use.

Module 6: Data Quality and Observability

Defining data quality dimensions (accuracy, completeness, consistency) per dataset based on use case requirements.
Implementing automated data profiling to detect schema deviations and value distribution shifts.
Setting up threshold-based alerts for null rates, duplicate counts, and referential integrity violations.
Correlating data pipeline failures with upstream source system incidents using observability tooling.
Creating data reliability scorecards to communicate trustworthiness across stakeholder teams.
Integrating data quality checks into CI/CD pipelines for data transformation code.
Handling false positives in data quality alerts by implementing adaptive baselines and suppression rules.
Documenting data incident root causes and resolution steps in a runbook for recurring issues.

Module 7: Security, Privacy, and Compliance

Implementing attribute-based access control (ABAC) for fine-grained data access in multi-tenant environments.
Masking or tokenizing PII fields in non-production environments using deterministic encryption.
Conducting data classification scans to identify sensitive data across structured and unstructured stores.
Enabling audit logging for data access and modification events to support forensic investigations.
Applying differential privacy techniques in aggregated reporting to prevent re-identification.
Managing data residency requirements by routing workloads to region-specific clusters or zones.
Integrating with enterprise identity providers (IdP) using SAML or SCIM for user provisioning.
Responding to data subject access requests (DSARs) with automated discovery and export workflows.

Module 8: Scalability, Performance, and Cost Management

Right-sizing compute clusters based on historical workload patterns and peak demand forecasts.
Implementing auto-scaling policies for streaming and batch processing with cost caps.
Optimizing shuffle operations in distributed processing to reduce network I/O and execution time.
Using materialized views or pre-aggregated tables to accelerate high-frequency queries.
Monitoring storage growth trends and identifying orphaned or redundant datasets for cleanup.
Negotiating reserved instances or savings plans for predictable cloud data service usage.
Implementing cost attribution by tagging resources with project, team, and cost center metadata.
Conducting query performance reviews to identify inefficient patterns and recommend indexing or rewriting.

Module 9: Governance, Stewardship, and Operating Model

Establishing data governance councils with cross-functional representation to prioritize initiatives.
Defining escalation paths for data ownership disputes and SLA violations across teams.
Implementing data product registration and onboarding workflows for new datasets.
Creating stewardship playbooks for routine tasks such as schema changes and deprecation notices.
Enforcing data contract agreements between producers and consumers using versioned specifications.
Measuring governance effectiveness through KPIs like time-to-discover, incident resolution time, and policy adherence.
Integrating data governance tools with service catalogs and DevOps pipelines for automation.
Conducting periodic data inventory audits to validate compliance with retention and classification policies.