Description

This curriculum spans the technical and operational complexity of a multi-workshop program for building and operating a cloud-native data warehouse, comparable to the design and implementation efforts seen in enterprise advisory engagements focused on scalable data platforms.

Module 1: Architecting Scalable Data Warehouse Foundations on Distributed Systems

Selecting between Lambda and Kappa architectures based on real-time SLA requirements and data reprocessing frequency
Designing partitioning and bucketing strategies in Delta Lake or Iceberg to optimize query performance on petabyte-scale datasets
Configuring cluster sizing and autoscaling policies in cloud-based Spark environments to balance cost and throughput
Implementing schema evolution mechanisms in Parquet-based storage to maintain backward compatibility during field additions or type changes
Choosing ingestion patterns (batch vs. micro-batch) based on source system capabilities and downstream data freshness needs
Integrating metastore solutions (e.g., AWS Glue, Unity Catalog) across multiple compute engines for consistent schema management
Validating data consistency across distributed nodes after ETL job failures using transaction log inspection
Defining retry logic and idempotency in ingestion pipelines to prevent data duplication under transient network failures

Module 2: Enterprise Data Modeling for Big Data Environments

Adapting dimensional modeling techniques to semi-structured data sources with nested JSON or Avro formats
Implementing slowly changing dimension (SCD) Type 2 logic in immutable data lake architectures using merge operations
Designing conformed dimensions across multiple business domains to enable cross-functional reporting
Choosing between 3NF and dimensional models based on query patterns and data marts' consumption needs
Handling schema drift in streaming data by implementing dynamic schema inference with validation guardrails
Modeling time-series data with time-windowed aggregations to reduce storage footprint while preserving analytical utility
Creating bridge tables to manage many-to-many relationships in star schemas without denormalizing into wide tables
Optimizing fact table grain decisions when integrating high-frequency IoT sensor data with transactional systems

Module 3: Real-Time Ingestion and Stream Processing Integration

Configuring Kafka topic retention and compaction policies to align with data warehouse replay requirements
Deploying Kafka Connect with transformation chains to cleanse and enrich data before landing in the warehouse
Implementing exactly-once semantics in Spark Structured Streaming using checkpointing and idempotent sinks
Designing watermark strategies to handle late-arriving data in time-based aggregations without excessive state accumulation
Selecting between stateful and stateless transformations based on business logic complexity and fault tolerance needs
Integrating Flink SQL jobs with Hive metastore for unified schema access across batch and streaming pipelines
Monitoring end-to-end latency from source to materialized view using distributed tracing and watermark tracking
Scaling stream processing parallelism by tuning partition counts and avoiding data skew in key distribution

Module 4: Data Quality, Validation, and Observability at Scale

Embedding Great Expectations or Soda Core checks into ingestion pipelines to enforce schema and value constraints
Designing data freshness monitors using timestamp validation with alerting thresholds based on business SLAs
Implementing row-count reconciliation between source systems and data warehouse layers for auditability
Creating lineage-aware validation rules that trigger reprocessing when upstream data changes unexpectedly
Using statistical profiling to detect data distribution shifts indicative of source system changes or pipeline corruption
Configuring anomaly detection on metric time series (e.g., null rates, cardinality) using seasonal decomposition models
Integrating data quality dashboards with incident management systems for automated ticket creation
Defining escalation paths for data incidents based on severity, impacted consumers, and regulatory exposure

Module 5: Security, Access Control, and Compliance in Distributed Warehouses

Implementing column-level masking policies in Unity Catalog or AWS Lake Formation based on user roles and PII sensitivity
Enforcing row-level security using dynamic filters in SQL query engines for multi-tenant data access
Managing encryption key lifecycle for data-at-rest using cloud KMS with rotation and audit logging enabled
Configuring audit trails to capture query activity, access attempts, and DDL changes across distributed compute clusters
Applying GDPR and CCPA compliance controls through automated PII detection and data retention enforcement
Integrating with enterprise identity providers using SAML or OIDC for centralized user authentication
Designing secure cross-account data sharing patterns using AWS RAM or Azure Lighthouse
Validating compliance with SOC 2 and ISO 27001 through automated configuration scanning and evidence collection

Module 6: Performance Optimization and Cost Management

Tuning Spark executor memory and core allocation to minimize garbage collection and maximize shuffle efficiency
Implementing Z-Order indexing on multi-dimensional filters to reduce file scanning in large tables
Designing materialized view refresh strategies to balance query performance and compute cost
Using query plan analysis to identify and eliminate Cartesian joins in ad-hoc reporting workloads
Applying data compaction routines to reduce small file proliferation and improve read performance
Setting up cost allocation tags in cloud billing to attribute warehouse usage to business units or projects
Implementing query queuing and workload management to prevent resource starvation during peak loads
Optimizing storage tiering by moving cold data to lower-cost storage classes with automated lifecycle policies

Module 7: Data Governance and Metadata Management

Deploying automated metadata extractors to capture technical lineage from ETL job configurations and SQL scripts
Implementing business glossary integration with data catalog tools to link technical assets to business terms
Enforcing data ownership and stewardship assignments through catalog-based approval workflows
Creating data product manifests with defined SLAs, quality metrics, and usage documentation
Using schema registry tools to version and govern Avro or Protobuf schemas across microservices
Implementing data retention policies with automated purging based on regulatory and business requirements
Integrating catalog search with natural language processing to improve discoverability for non-technical users
Generating data inventory reports for compliance audits with classification and sensitivity tagging

Module 8: Cloud-Native Deployment and Operational Resilience

Designing multi-region deployment strategies for disaster recovery with cross-region replication of metadata and data
Implementing infrastructure-as-code using Terraform or Pulumi to version and deploy warehouse environments
Configuring CI/CD pipelines for ETL code with automated testing and rollback capabilities
Setting up health checks and auto-healing for long-running streaming jobs using Kubernetes liveness probes
Managing dependency conflicts in Python or Scala libraries across shared cluster environments
Planning capacity for peak workloads during month-end reporting cycles with preemptive cluster scaling
Documenting runbooks for common failure scenarios including metastore outages and credential expiration
Conducting chaos engineering tests to validate resilience of data pipelines under network partition conditions

Module 9: Advanced Analytics and Machine Learning Integration

Preparing feature stores by aggregating raw data into time-windowed, versioned feature sets for model training
Synchronizing feature engineering logic between batch and real-time pipelines to prevent training-serving skew
Exporting model inference results back to the data warehouse with transactional consistency guarantees
Implementing model monitoring by logging predictions and actuals for drift and performance analysis
Using warehouse-native ML engines (e.g., BigQuery ML, Redshift ML) for rapid prototyping without data movement
Designing backfill processes for historical feature generation with efficient date-range partitioning
Securing access to model training datasets with the same governance policies as operational reporting data
Optimizing feature storage using columnar formats with dictionary encoding to reduce I/O during training