Description

This curriculum spans the technical, governance, and operational complexities of managing big data projects across a multi-team enterprise environment, comparable to the scope of a multi-phase internal capability program addressing data platform integration, compliance alignment, and lifecycle management.

Module 1: Defining Big Data Project Scope and Stakeholder Alignment

Selecting use cases based on measurable business KPIs rather than technical novelty, ensuring alignment with executive priorities.
Determining data ownership boundaries across departments when multiple business units contribute or consume data.
Negotiating scope inclusion/exclusion criteria with legal and compliance teams for data subject to regulatory constraints (e.g., GDPR, HIPAA).
Deciding whether to pursue incremental enhancements or a greenfield implementation based on existing data maturity.
Documenting assumptions about data availability and quality during scoping to prevent downstream delays.
Establishing escalation paths for scope changes initiated by stakeholders mid-project.
Aligning project milestones with fiscal reporting cycles to facilitate budget renewal approvals.
Defining success metrics for data latency, coverage, and accuracy prior to development kickoff.

Module 2: Selecting and Integrating Big Data Platforms and Tools

Choosing between cloud-native (e.g., AWS EMR, Azure Databricks) and on-premise Hadoop clusters based on data residency and cost models.
Integrating workflow schedulers (e.g., Apache Airflow, Luigi) with existing DevOps pipelines for version-controlled orchestration.
Assessing vendor lock-in risks when adopting managed services for storage, compute, or machine learning.
Standardizing data serialization formats (e.g., Parquet vs. Avro) across ingestion and processing layers for compatibility.
Configuring resource allocation policies in YARN or Kubernetes to balance cost and performance for concurrent workloads.
Validating API compatibility between ETL tools and source systems with frequent schema changes.
Implementing fallback mechanisms for third-party data connectors prone to outages or throttling.
Documenting interoperability constraints between open-source components (e.g., Spark version vs. Hive metastore).

Module 3: Data Governance and Compliance Frameworks

Implementing role-based access controls (RBAC) in data lakes to restrict access by department, role, or sensitivity level.
Mapping data lineage from raw ingestion to reporting layers to satisfy audit requirements and troubleshoot errors.
Classifying datasets according to sensitivity (PII, financial, operational) and applying encryption or masking policies.
Establishing data retention schedules and automating purge workflows for compliance with data minimization principles.
Integrating metadata management tools (e.g., Apache Atlas) with existing data catalogs to maintain consistency.
Conducting DPIAs (Data Protection Impact Assessments) for new data processing activities involving personal data.
Coordinating with internal legal teams to update data processing agreements when onboarding new data vendors.
Logging access and modification events in immutable audit trails for forensic investigations.

Module 4: Building Scalable Data Ingestion Pipelines

Choosing between batch and streaming ingestion based on downstream SLA requirements and source system capabilities.
Designing idempotent ingestion processes to handle duplicate or out-of-order messages in distributed systems.
Implementing backpressure mechanisms in Kafka consumers to prevent system overload during traffic spikes.
Partitioning large datasets by time or key to optimize query performance and reduce scan costs.
Validating schema conformance at ingestion using schema registries to prevent malformed data propagation.
Monitoring data drift in source systems and triggering alerts when new fields or value ranges appear.
Configuring retry logic and dead-letter queues for failed records without blocking pipeline execution.
Estimating storage growth and provisioning cloud buckets or HDFS space with buffer for peak loads.

Module 5: Managing Compute and Storage Costs

Right-sizing cluster instances based on historical workload patterns and auto-scaling during peak periods.
Implementing lifecycle policies to transition cold data from hot to archive storage tiers (e.g., S3 Glacier).
Tracking per-job compute costs using cloud billing tags or custom monitoring agents.
Optimizing query performance through partitioning, clustering, and materialized views to reduce compute spend.
Negotiating reserved instance commitments for predictable workloads to reduce cloud costs by 30–70%.
Enforcing query timeouts and resource quotas to prevent runaway jobs from consuming cluster resources.
Conducting cost-benefit analysis for caching layers (e.g., Redis, Alluxio) versus recomputation.
Reporting cost allocation by team or project to enable chargeback or showback models.

Module 6: Ensuring Data Quality and Reliability

Defining data quality rules (completeness, accuracy, consistency) per dataset and integrating checks into pipelines.
Implementing automated anomaly detection for metric deviations using statistical baselines or ML models.
Establishing data ownership roles to assign accountability for data quality issues.
Creating alerting workflows that route data quality failures to responsible engineers with context.
Running reconciliation jobs between source and target systems to detect data loss during ETL.
Versioning datasets and processing logic to enable rollback during data corruption events.
Documenting known data quirks and exceptions in a shared knowledge base accessible to analysts.
Conducting root cause analysis for recurring data quality incidents and updating preventive controls.

Module 7: Cross-Functional Team Coordination and Delivery

Establishing shared sprint goals between data engineering, analytics, and business teams using agile frameworks.
Defining interface contracts (e.g., schema, SLA, ownership) for data products consumed across teams.
Managing dependencies between parallel workstreams using Gantt charts or dependency graphs.
Conducting code reviews with mandatory participation from security and data governance roles.
Standardizing documentation templates for data models, pipelines, and APIs to reduce onboarding time.
Resolving environment drift by enforcing IaC (Infrastructure as Code) for staging and production parity.
Coordinating production deployments during maintenance windows to minimize business impact.
Facilitating blameless post-mortems after pipeline outages to improve system resilience.

Module 8: Monitoring, Alerting, and Incident Response

Configuring synthetic transactions to validate end-to-end data flow from ingestion to dashboarding.
Setting dynamic alert thresholds based on historical patterns to reduce false positives.
Integrating monitoring tools (e.g., Prometheus, Datadog) with incident management systems (e.g., PagerDuty).
Defining escalation policies for alerts based on severity, time of day, and on-call rotations.
Creating runbooks with step-by-step remediation procedures for common pipeline failures.
Validating backup and recovery procedures for critical data assets quarterly.
Measuring and reporting on pipeline uptime and SLA compliance to stakeholders.
Using distributed tracing to identify bottlenecks in complex, multi-stage data workflows.

Module 9: Scaling and Evolving the Data Architecture

Assessing technical debt in legacy pipelines and prioritizing refactoring based on failure frequency and business impact.
Planning data mesh adoption by identifying domain boundaries and decentralizing ownership.
Migrating monolithic ETL jobs into modular, reusable components with versioned APIs.
Evaluating the need for real-time capabilities based on evolving business requirements.
Standardizing data modeling patterns (e.g., Data Vault, Dimensional) across teams to improve consistency.
Implementing feature stores to manage machine learning feature lifecycle and reduce duplication.
Conducting architecture review boards to approve changes to shared data platforms.
Developing a roadmap for retiring deprecated tools and transitioning workloads to supported systems.