Description

This curriculum spans the technical and operational complexity of a multi-workshop program for enterprise data platform teams, covering the design, governance, and lifecycle management challenges seen in large-scale data implementations across cloud environments.

Module 1: Data Architecture Design and Platform Selection

Selecting between data lakehouse and traditional data warehouse models based on query performance, schema flexibility, and governance requirements.
Evaluating cloud provider data platforms (AWS, Azure, GCP) for compatibility with existing identity management and compliance frameworks.
Designing partitioning and clustering strategies in distributed storage to balance query latency and cost.
Deciding on open table formats (Delta Lake, Iceberg, Hudi) based on ACID support, cross-engine compatibility, and tooling maturity.
Integrating real-time ingestion pipelines with batch processing systems without introducing data duplication or consistency issues.
Assessing vendor lock-in risks when adopting managed services for data orchestration and metadata management.
Implementing data lifecycle policies to automate tiering from hot to cold storage based on access patterns.
Establishing naming conventions and metadata standards across teams to ensure discoverability and reduce redundancy.

Module 2: Scalable Data Ingestion and Pipeline Engineering

Choosing between change data capture (CDC) and API-based extraction for source systems with limited logging capabilities.
Configuring Kafka topics with appropriate replication and retention settings to ensure durability without over-provisioning.
Handling schema evolution in streaming pipelines using schema registry with backward and forward compatibility checks.
Implementing backpressure mechanisms in Spark Streaming jobs to prevent executor overload during traffic spikes.
Designing idempotent ingestion workflows to allow safe retries without data duplication.
Monitoring end-to-end data latency across stages and setting up alerts for pipeline degradation.
Securing data in transit using mutual TLS and encrypting credentials in pipeline configuration stores.
Optimizing batch frequency trade-offs between near real-time needs and resource utilization in ETL scheduling.

Module 3: Data Quality and Observability Implementation

Defining data quality rules (completeness, accuracy, consistency) per domain and integrating them into pipeline validation layers.
Deploying automated anomaly detection on key metrics using statistical thresholds and historical baselines.
Instrumenting lineage tracking to trace data from source to consumption for audit and root cause analysis.
Selecting between open-source (Great Expectations) and commercial tools for data quality monitoring at scale.
Setting up data freshness alerts based on expected update cycles from source systems.
Managing false positives in data quality alerts by tuning thresholds and incorporating business context.
Integrating data observability into CI/CD pipelines for data models to catch issues before deployment.
Creating escalation protocols for data incidents with defined ownership and resolution SLAs.

Module 4: Identity, Access, and Data Governance

Implementing row-level and column-level security in query engines based on user roles and data sensitivity.
Mapping data classification labels (PII, PHI, financial) to access control policies across storage layers.
Integrating data governance tools with IAM systems to synchronize user permissions and group memberships.
Enforcing data access approvals through workflow systems for highly sensitive datasets.
Designing audit trails to log all data access and modification events for compliance reporting.
Negotiating data ownership responsibilities between business units and central data teams.
Implementing dynamic data masking for development and testing environments.
Managing consent tracking for customer data in alignment with GDPR and CCPA requirements.

Module 5: Master Data Management and Data Cataloging

Selecting a golden record strategy for customer or product entities across disparate source systems.
Implementing fuzzy matching algorithms to resolve entity duplicates with configurable thresholds.
Choosing between centralized MDM hubs and decentralized stewardship models based on organizational maturity.
Automating metadata extraction from ETL jobs, BI tools, and query logs into a central catalog.
Enabling self-service data discovery with search, tagging, and usage statistics in the catalog interface.
Defining stewardship workflows for metadata curation, including business definitions and KPI ownership.
Integrating data catalog with data quality tools to surface reliability scores alongside dataset entries.
Managing versioning of data models and schema changes within the catalog for historical traceability.

Module 6: Performance Optimization and Cost Management

Right-sizing cluster configurations for Spark workloads based on historical memory and CPU utilization.
Implementing materialized views and pre-aggregations to accelerate dashboard query performance.
Applying data compaction strategies to reduce small file problems in distributed file systems.
Using query optimization techniques such as predicate pushdown and column pruning in analytical engines.
Monitoring and controlling cloud data service spending with budget alerts and tagging policies.
Choosing between on-demand and reserved compute resources based on workload predictability.
Optimizing data serialization formats (Parquet vs. ORC vs. Avro) for read performance and compression.
Implementing caching layers for frequently accessed datasets in BI and machine learning workflows.

Module 7: Data for Machine Learning and Advanced Analytics

Designing feature stores with versioning and consistency guarantees for training and serving alignment.
Implementing point-in-time correct joins to prevent data leakage in historical feature generation.
Managing feature drift detection by monitoring statistical properties over time and triggering retraining.
Securing access to training datasets containing sensitive attributes used in model development.
Orchestrating reproducible training pipelines with dependency and data version tracking.
Deploying batch scoring pipelines with SLA monitoring for downstream consumption.
Integrating model metadata with data lineage to trace predictions back to source data and features.
Optimizing data shuffling and partitioning strategies in distributed model training jobs.

Module 8: Cross-Functional Data Operations and Collaboration

Establishing SLAs for data delivery between data engineering and consuming teams (analytics, ML, ops).
Implementing CI/CD for data pipelines with automated testing, peer review, and rollback procedures.
Coordinating schema change approvals across teams to prevent breaking changes in production.
Defining incident response playbooks for data outages and corruption events.
Conducting blameless post-mortems for major data incidents to improve system resilience.
Facilitating data literacy programs to align business stakeholders on data definitions and limitations.
Managing technical debt in data pipelines through scheduled refactoring and documentation updates.
Aligning data team priorities with business objectives using OKRs and quarterly planning cycles.

Module 9: Regulatory Compliance and Data Ethics

Conducting data protection impact assessments (DPIAs) for new data initiatives involving personal data.
Implementing data minimization practices by restricting collection to only necessary fields.
Designing data retention and deletion workflows to meet legal and regulatory timelines.
Enabling data subject access requests (DSARs) with tools to locate and export individual records.
Documenting algorithmic decision-making processes for regulatory scrutiny and internal review.
Assessing bias in training data for high-impact models using statistical fairness metrics.
Establishing data ethics review boards for sensitive use cases involving surveillance or profiling.
Ensuring cross-border data transfers comply with mechanisms like SCCs and adequacy decisions.