Description

This curriculum spans the technical and operational complexity of a multi-workshop program focused on enterprise data platform engineering, covering the design, ingestion, governance, and optimization of structured data systems across hybrid and cloud environments.

Module 1: Defining Structured Data in Modern Data Ecosystems

Selecting appropriate schema definitions for transactional systems versus analytical workloads in hybrid architectures
Evaluating when to enforce rigid schema-on-write versus flexible schema-on-read in data lakehouse implementations
Integrating legacy relational data models with cloud-native structured data formats like Parquet and ORC
Mapping business entity definitions across departments to ensure consistent column semantics in shared datasets
Deciding between row-based and columnar storage based on query patterns in OLTP and OLAP systems
Implementing data type standardization across systems to prevent implicit casting errors in ETL pipelines
Assessing the impact of null handling policies on downstream reporting accuracy
Documenting metadata lineage for structured fields to support audit and compliance requirements

Module 2: Data Modeling for Scalable Structured Systems

Choosing between normalized, star, and wide-table models based on query performance and update frequency
Designing slowly changing dimensions for historical tracking in enterprise data warehouses
Partitioning large fact tables by time or region to optimize query performance and data lifecycle management
Implementing surrogate key strategies to decouple source system identifiers from analytical models
Resolving conformed dimension conflicts when integrating data from multiple operational systems
Denormalizing tables for analytical use cases while maintaining referential integrity constraints
Modeling time-series data with appropriate primary and clustering keys in distributed databases
Validating model assumptions against actual data distribution and cardinality post-ingestion

Module 3: Ingestion and Pipeline Architecture

Configuring batch frequency versus latency trade-offs for daily, hourly, or near-real-time structured data loads
Implementing change data capture (CDC) using log parsing versus polling mechanisms in RDBMS sources
Handling schema drift during ingestion by defining validation rules and error quarantine processes
Designing idempotent ingestion pipelines to prevent data duplication during retries
Selecting between micro-batch and streaming ingestion for structured event data from message queues
Compressing and encrypting structured data payloads in transit between on-prem and cloud environments
Monitoring ingestion pipeline backpressure and tuning parallelism in Spark or Flink workloads
Implementing data freshness checks and alerting for SLA-bound structured datasets

Module 4: Storage Formats and Optimization

Choosing Parquet, Avro, or ORC based on compression ratio, query speed, and schema evolution needs
Tuning Parquet row group size and page size for optimal I/O performance on cloud object storage
Implementing Z-Order or bin-packing clustering to co-locate related records in cloud data lakes
Managing file size fragmentation in append-heavy workloads using compaction strategies
Enabling predicate pushdown and column pruning in query engines by aligning schema design with access patterns
Encrypting structured data at rest using customer-managed or provider-managed keys in cloud storage
Versioning structured data files to support reproducibility and rollback in analytical environments
Applying data masking rules at ingestion time for PII fields stored in structured formats

Module 5: Query Engines and Performance Tuning

Selecting between Presto, Trino, Spark SQL, and cloud-native engines based on concurrency and cost
Configuring executor memory and shuffle partitions to avoid out-of-memory errors on large joins
Creating and maintaining statistics for cost-based optimizers in distributed SQL engines
Indexing strategies in columnar databases, including min/max, bloom filters, and secondary indexes
Optimizing join order and broadcast hints for skewed datasets in analytical queries
Partition pruning implementation to reduce scan volume in time-series queries
Monitoring and diagnosing slow queries using execution plan analysis and runtime profiling
Implementing materialized views or pre-aggregated tables for high-frequency reporting queries

Module 6: Data Quality and Validation

Defining and enforcing field-level constraints such as uniqueness, referential integrity, and allowed value sets
Implementing automated anomaly detection for numeric field distributions using statistical thresholds
Validating foreign key relationships across datasets in distributed environments where constraints aren't enforced
Configuring data quality scorecards with weighted rules for executive reporting
Handling missing or invalid records by routing to quarantine tables with remediation workflows
Tracking data completeness across expected ingestion windows using watermark validation
Integrating data profiling results into CI/CD pipelines for data model changes
Establishing ownership and escalation paths for recurring data quality failures

Module 7: Governance and Compliance

Classifying structured data fields as PII, PHI, or financial using pattern-based and dictionary-driven scanners
Implementing dynamic data masking policies in query engines based on user roles and sensitivity labels
Auditing access to sensitive structured tables using query logging and monitoring tools
Enforcing data retention policies with automated purging or archival based on regulatory requirements
Mapping data lineage from source systems to reports to support GDPR and CCPA data subject requests
Managing consent flags in structured customer records and propagating them through downstream systems
Documenting data ownership and stewardship responsibilities in a centralized catalog
Conducting impact analysis for schema changes using lineage to identify dependent reports and models

Module 8: Integration and Interoperability

Exposing structured data via REST or GraphQL APIs with pagination, filtering, and rate limiting
Synchronizing data between cloud data warehouses and on-prem BI tools using secure connectors
Transforming structured data for machine learning pipelines, including feature encoding and normalization
Implementing data sharing agreements using secure views or data exchange platforms
Handling time zone and calendar discrepancies when integrating structured data across global regions
Standardizing currency and unit conversions in financial structured datasets
Resolving identifier mismatches when merging customer data from disparate CRM and ERP systems
Generating OpenAPI specifications for structured data endpoints consumed by downstream applications

Module 9: Monitoring, Cost, and Lifecycle Management

Tracking storage growth of structured datasets to forecast cloud cost and capacity needs
Implementing auto-tiering policies to move cold structured data to lower-cost storage classes
Setting up alerts for abnormal query costs driven by full table scans on large structured tables
Right-sizing compute clusters based on historical utilization patterns for structured workloads
Archiving and decommissioning obsolete structured datasets with stakeholder approval workflows
Measuring and reporting on data pipeline success rates and error trends over time
Optimizing query caching strategies for frequently accessed structured reports
Conducting quarterly cost attribution reviews to allocate structured data expenses by business unit