Description

This curriculum spans the technical and organisational complexity of an enterprise data platform rollout, comparable to a multi-quarter advisory engagement focused on building and operating a governed, scalable big data environment across distributed teams and systems.

Module 1: Defining Data Strategy and Business Alignment

Selecting key performance indicators (KPIs) that align big data initiatives with enterprise revenue, cost, or risk objectives
Mapping data sources to business units and determining ownership for data stewardship accountability
Conducting feasibility assessments to determine whether batch or real-time processing better supports strategic use cases
Negotiating access to siloed operational data across departments with competing priorities
Establishing criteria for retiring legacy systems while ensuring continuity of historical data access
Documenting data lineage requirements for auditability in regulated industries
Deciding whether to build internal data products or integrate third-party analytics platforms
Designing escalation paths for resolving data ownership disputes between business stakeholders

Module 2: Data Ingestion Architecture and Pipeline Design

Selecting between push and pull ingestion models based on source system capabilities and latency requirements
Implementing idempotent ingestion logic to prevent duplication during pipeline retries
Configuring throttling mechanisms to avoid overloading source databases during bulk extraction
Designing schema-on-read patterns for semi-structured data from IoT or log sources
Choosing between change data capture (CDC) and log-based replication for database synchronization
Validating payload integrity when ingesting data from third-party APIs with inconsistent formats
Setting up dead-letter queues to isolate malformed records without halting pipeline execution
Monitoring end-to-end ingestion latency across hybrid cloud and on-premises environments

Module 3: Distributed Storage and Data Lake Governance

Partitioning data by time and business domain to optimize query performance and access control
Implementing lifecycle policies to transition cold data from hot to archival storage tiers
Enforcing file format standards (e.g., Parquet, ORC) to ensure compression and schema evolution support
Applying role-based access control (RBAC) at the directory and file level in multi-tenant environments
Registering datasets in a centralized metadata catalog with standardized tagging and descriptions
Conducting regular audits to detect and remove orphaned or unused datasets
Designing encryption strategies for data at rest using customer-managed or provider-managed keys
Managing schema drift by versioning data layouts and implementing backward compatibility checks

Module 4: Scalable Processing with Batch and Stream Frameworks

Choosing between Apache Spark and Flink based on stateful processing and windowing requirements
Tuning executor memory and parallelism settings to prevent out-of-memory errors on large joins
Implementing watermarking and allowed lateness in streaming jobs to handle late-arriving data
Designing checkpointing intervals to balance recovery time and performance overhead
Optimizing shuffle operations by pre-aggregating data before wide transformations
Validating exactly-once semantics in streaming pipelines using transactional sinks
Isolating development, testing, and production workloads to prevent resource contention
Monitoring backpressure in Kafka consumers to detect processing bottlenecks

Module 5: Data Quality and Anomaly Detection

Defining data quality rules (completeness, consistency, accuracy) per dataset and business context
Automating data profiling during ingestion to detect unexpected null rates or value distributions
Setting up alerting thresholds for metric deviations using statistical process control
Integrating data quality checks into CI/CD pipelines for data transformation code
Handling missing dimensions in fact tables by implementing referential integrity fallbacks
Investigating root causes of data drift in machine learning feature pipelines
Logging and reporting data quality violations to operational teams without blocking pipelines
Versioning data quality rules to track changes over time and support reproducibility

Module 6: Performance Optimization and Cost Management

Right-sizing cluster configurations based on historical utilization and peak demand patterns
Implementing data compaction routines to reduce small file overhead in distributed file systems
Using predicate pushdown and column pruning to minimize data scanned in query execution
Establishing query cost estimation tools to prevent runaway jobs in shared environments
Setting up auto-scaling policies with cooldown periods to avoid thrashing
Allocating compute resources using YARN or Kubernetes namespaces with guaranteed quotas
Monitoring storage growth trends to forecast budget needs and justify infrastructure investments
Enforcing query timeouts and user concurrency limits in self-service analytics platforms

Module 7: Security, Compliance, and Auditability

Implementing field-level masking for sensitive data in non-production environments
Integrating with enterprise identity providers using SAML or OIDC for single sign-on
Generating audit logs for data access and modification events across storage and compute layers
Conducting data protection impact assessments (DPIAs) for cross-border data transfers
Applying data retention policies aligned with legal hold requirements
Encrypting data in transit using TLS 1.3 and validating certificate chains in microservices
Responding to data subject access requests (DSARs) by tracing personal data across pipelines
Performing regular penetration testing on exposed data APIs and dashboard endpoints

Module 8: Advanced Analytics and Machine Learning Integration

Building feature stores with versioned, reusable feature sets for multiple ML models
Scheduling regular retraining cycles based on data drift detection thresholds
Deploying model scoring pipelines in batch versus real-time based on SLA requirements
Validating model input schemas to prevent training-serving skew
Monitoring prediction latency and error rates in production inference services
Managing model registry workflows including staging, approval, and rollback procedures
Integrating A/B testing frameworks to evaluate model performance against business metrics
Ensuring explainability outputs meet regulatory requirements for high-stakes decisions

Module 9: Operational Monitoring and Incident Response

Defining service level objectives (SLOs) for data freshness, availability, and correctness
Setting up distributed tracing across microservices to diagnose pipeline failures
Creating runbooks for common failure scenarios such as broker outages or schema mismatches
Automating recovery procedures for failed jobs using retry budgets and circuit breakers
Correlating infrastructure metrics (CPU, I/O) with data pipeline performance indicators
Conducting blameless postmortems after data incidents to update prevention controls
Managing configuration drift by enforcing infrastructure-as-code practices
Coordinating incident response between data engineering, DevOps, and business operations teams