Description

This curriculum spans the technical and operational breadth of a multi-workshop program focused on building and maintaining enterprise-grade data platforms, covering the design, governance, and optimization of data systems as they are implemented in large-scale, regulated organizations.

Module 1: Architecting Scalable Data Ingestion Pipelines

Select between batch vs. streaming ingestion based on SLA requirements, data source volatility, and downstream processing latency tolerance.
Implement schema validation at ingestion points to prevent data corruption and ensure downstream compatibility with analytics workloads.
Configure retry and backpressure mechanisms in Kafka consumers to handle broker outages without data loss or duplication.
Design idempotent ingestion logic to manage duplicate messages in distributed messaging systems.
Integrate change data capture (CDC) tools with transactional databases while minimizing performance impact on OLTP systems.
Establish data lineage tracking at the ingestion layer to support auditability and debugging of pipeline failures.
Optimize file size and format (e.g., Parquet vs. Avro) during batch ingestion to balance query performance and storage costs.
Enforce encryption in transit and at rest for sensitive data entering the pipeline from external partners.

Module 2: Distributed Data Storage and Partitioning Strategies

Choose between data lake and data warehouse architectures based on query patterns, governance needs, and user access profiles.
Define partitioning and bucketing schemes in Delta Lake to optimize query performance on time-series datasets.
Implement lifecycle policies for cold storage tiering to reduce costs while maintaining compliance with data retention rules.
Evaluate trade-offs between replication and erasure coding in HDFS for fault tolerance and storage efficiency.
Configure row-level and column-level access controls in Apache Ranger to meet regulatory and departmental data segregation requirements.
Design schema evolution strategies in Parquet and Avro to handle backward and forward compatibility in long-lived datasets.
Monitor and rebalance data skew across nodes in distributed file systems to prevent hotspotting and performance degradation.
Integrate object storage (e.g., S3, ADLS) with compute engines using optimized connectors to reduce I/O latency.

Module 3: Data Quality Monitoring and Anomaly Detection

Deploy automated data profiling jobs to detect schema drift, null rate spikes, and value distribution shifts in production datasets.
Set up threshold-based alerts for data freshness, volume, and completeness metrics using monitoring tools like Great Expectations.
Implement statistical anomaly detection on streaming data to flag outliers without relying on static rules.
Integrate data quality rules into CI/CD pipelines to prevent deployment of pipelines with failing validation checks.
Design reconciliation processes between source systems and data warehouse tables for critical financial data.
Assign ownership and escalation paths for data quality incidents to ensure timely resolution.
Balance false positive rates in anomaly detection with operational overhead of alert fatigue.
Log and version data quality rules to enable audit trails and rollback during incident investigations.

Module 4: Real-Time Stream Processing with Flink and Spark

Select windowing strategies (tumbling, sliding, session) based on business requirements for metrics like user session duration or fraud detection.
Configure state backends in Apache Flink to manage large state sizes while ensuring fault tolerance and recovery speed.
Handle late-arriving data in event-time processing by defining allowed lateness and side output for late events.
Optimize checkpoint intervals in Spark Structured Streaming to balance recovery time and performance overhead.
Implement exactly-once processing semantics using two-phase commit protocols when writing to external systems.
Scale stream processing jobs dynamically based on input rate using Kubernetes-based autoscaling.
Isolate stateful processing logic to avoid non-deterministic results during job restarts.
Instrument streaming jobs with custom metrics for throughput, latency, and backpressure to support capacity planning.

Module 5: Building and Governing a Modern Data Warehouse

Define dimensional modeling standards for fact and dimension tables to ensure consistency across business domains.
Implement slowly changing dimension (SCD) Type 2 logic to track historical changes in master data.
Use materialized views in Snowflake or BigQuery to precompute aggregations for high-frequency reporting queries.
Enforce data warehouse access policies using role-based access control (RBAC) aligned with organizational units.
Version control DDL and DML scripts using Git to enable reproducible environments and audit changes.
Design data vault or data mesh structures for large enterprises requiring decentralized ownership and scalability.
Monitor query performance and cost using built-in tools to identify and refactor expensive operations.
Establish naming conventions and metadata documentation standards to improve discoverability and reduce onboarding time.

Module 6: Data Discovery and Metadata Management

Deploy automated metadata extractors to catalog datasets across databases, data lakes, and BI tools.
Integrate data lineage tracking from ingestion to reporting layers to support impact analysis and compliance audits.
Configure business glossary terms in Apache Atlas and link them to technical assets for cross-functional alignment.
Implement metadata retention policies to avoid performance degradation in large-scale metadata stores.
Use semantic search over metadata to enable non-technical users to locate relevant datasets.
Expose metadata APIs to enable integration with data quality, lineage, and access control systems.
Classify datasets by sensitivity level using automated scanners and enforce access policies accordingly.
Track data ownership and stewardship assignments to ensure accountability for data health and usage.

Module 7: Advanced Analytics with Machine Learning Pipelines

Design feature stores to enable consistent feature engineering and reuse across ML models.
Orchestrate retraining workflows using Airflow or Kubeflow based on data drift detection or model performance decay.
Validate model inputs against production data distributions to prevent silent failures in inference.
Implement shadow mode deployment to compare new model predictions against existing systems before cutover.
Monitor model performance metrics (precision, recall, AUC) in production with statistical significance testing.
Version datasets, models, and code using MLflow to ensure reproducibility and auditability.
Balance model complexity with inference latency requirements in real-time scoring systems.
Apply bias detection techniques on model outputs to identify disparate impact across demographic groups.

Module 8: Data Governance and Compliance at Scale

Map data processing activities to GDPR, CCPA, or HIPAA requirements using a data inventory and processing register.
Implement data minimization practices by masking or truncating non-essential fields during ingestion.
Configure dynamic data masking policies in query engines to restrict sensitive data exposure based on user roles.
Conduct DPIAs (Data Protection Impact Assessments) for high-risk data processing initiatives.
Establish data retention and deletion workflows to comply with legal hold and right-to-be-forgotten requests.
Integrate PII detection tools to scan unstructured data in data lakes and apply classification tags.
Enforce encryption key management using centralized KMS solutions with strict access policies.
Perform regular access certification reviews to deprovision stale user permissions.

Module 9: Performance Optimization and Cost Management

Right-size cluster configurations for Spark jobs based on shuffle spill and memory utilization metrics.
Implement query pushdown and predicate filtering to reduce data scanned in object storage.
Use workload management queues in Snowflake or Databricks to prioritize critical reporting jobs.
Analyze cost per query in cloud data warehouses and identify top spenders for optimization.
Convert wide, denormalized tables into normalized star schemas to reduce storage and improve compression.
Schedule resource-intensive jobs during off-peak hours to avoid contention and reduce costs.
Enable auto-suspend and auto-resume for cloud data warehouse warehouses to minimize idle compute spend.
Implement data compaction routines to reduce small file problems in distributed storage systems.