Description

This curriculum spans the design and operationalization of enterprise-scale data platforms, comparable in scope to a multi-phase internal capability program that integrates data strategy, governance, and engineering practices across complex organizational units.

Module 1: Defining Data Strategy and Business Alignment

Selecting use cases with measurable ROI, such as customer churn prediction or supply chain optimization, based on stakeholder pain points and data availability
Mapping data initiatives to enterprise KPIs and securing executive sponsorship for cross-functional data projects
Conducting feasibility assessments that evaluate data maturity, infrastructure readiness, and skill gaps before project initiation
Establishing data ownership models across business units to prevent siloed analytics efforts
Aligning data architecture decisions with long-term business growth scenarios, including mergers and geographic expansion
Creating feedback loops between analytics teams and business units to refine use case scope and success metrics
Negotiating data access rights with legal and compliance teams for regulated domains such as healthcare or finance
Documenting data lineage from source systems to dashboards to support auditability and trust

Module 2: Designing Scalable Data Ingestion Architectures

Choosing between batch and streaming ingestion based on SLAs, data volume, and downstream processing requirements
Implementing change data capture (CDC) for real-time replication from OLTP databases without impacting transaction performance
Configuring message brokers like Apache Kafka for fault-tolerant, high-throughput data pipelines
Handling schema evolution in streaming data by implementing schema registry and backward compatibility policies
Designing retry and dead-letter queue mechanisms for failed ingestion jobs in distributed systems
Evaluating cloud-native ingestion services (e.g., AWS Kinesis, Azure Event Hubs) versus on-premise solutions for hybrid environments
Securing data in transit using TLS and managing credentials via secret management tools like HashiCorp Vault
Monitoring ingestion latency and throughput using observability tools like Prometheus and Grafana

Module 3: Building and Governing Data Storage Layers

Selecting file formats (Parquet, ORC, Avro) based on query patterns, compression needs, and schema evolution support
Partitioning and bucketing large datasets in data lakes to optimize query performance and reduce compute costs
Implementing data lifecycle policies to transition cold data from hot storage (SSD) to archival tiers (e.g., S3 Glacier)
Enforcing data access controls at the object and column level using Apache Ranger or AWS Lake Formation
Designing multi-zone, multi-region storage architectures for disaster recovery and data sovereignty compliance
Validating data integrity after ETL processes using checksums and row count reconciliation
Managing metadata consistency across data catalog tools like Apache Atlas or AWS Glue Data Catalog
Handling unstructured data (logs, images, text) by integrating object storage with content indexing systems

Module 4: Implementing Data Processing Frameworks

Choosing between Apache Spark, Flink, and cloud-managed services (e.g., Dataproc, EMR) based on latency and scalability needs
Optimizing Spark jobs by tuning executor memory, parallelism, and shuffle partitions for large datasets
Writing idempotent transformations to support replayability in case of pipeline failures
Integrating UDFs (user-defined functions) in Python or Scala while managing serialization and performance overhead
Validating data quality during processing using embedded rules for completeness, consistency, and validity
Implementing watermarking and windowing strategies in streaming jobs to handle late-arriving data
Managing dependencies and versioning for processing jobs using CI/CD pipelines and containerization
Monitoring resource utilization and job failures using cluster-level metrics and alerting systems

Module 5: Ensuring Data Quality and Observability

Defining data quality rules (e.g., null rate thresholds, value distributions) per dataset and integrating them into pipelines
Implementing automated data validation checks using tools like Great Expectations or Deequ
Setting up anomaly detection on data drift using statistical baselines and alerting on deviations
Creating data quality dashboards that show health scores across pipelines and datasets
Establishing ownership for data issue resolution and defining SLAs for incident response
Logging data lineage and transformation history to support root cause analysis during data incidents
Conducting root-cause analysis when data discrepancies impact business decisions or reporting
Integrating data observability into DevOps workflows to catch quality issues before production deployment

Module 6: Enabling Secure and Compliant Data Access

Implementing role-based access control (RBAC) and attribute-based access control (ABAC) in data platforms
Masking sensitive data (PII, PCI) dynamically at query time using row- and column-level security
Conducting data protection impact assessments (DPIAs) for new analytics projects under GDPR or CCPA
Managing data residency requirements by deploying region-specific data zones and access gateways
Encrypting data at rest using customer-managed keys and rotating keys on a defined schedule
Auditing data access patterns and generating compliance reports for internal and external auditors
Integrating with enterprise identity providers (e.g., Active Directory, Okta) for centralized authentication
Handling data subject access requests (DSARs) by building traceability from individual records to source systems

Module 7: Deploying Analytics and Machine Learning at Scale

Orchestrating complex workflows using Apache Airflow or cloud schedulers with dependency management and retry logic
Versioning datasets and models using tools like DVC or MLflow to ensure reproducibility
Deploying machine learning models as REST APIs with auto-scaling and load balancing in containerized environments
Monitoring model performance decay and triggering retraining based on data drift or accuracy thresholds
Integrating feature stores (e.g., Feast, Tecton) to ensure consistency between training and serving data
Validating model inputs for schema and distribution compliance before inference
Managing A/B testing frameworks to evaluate model impact on business metrics in production
Logging prediction requests and outcomes for audit, debugging, and regulatory compliance

Module 8: Optimizing Performance and Cost Efficiency

Right-sizing compute clusters based on workload patterns and leveraging auto-scaling policies
Implementing data compaction and indexing strategies to reduce query execution time and cost
Using spot instances or preemptible VMs for fault-tolerant batch workloads to reduce cloud spend
Setting budget alerts and cost allocation tags to track spending by team, project, or dataset
Optimizing query performance through predicate pushdown, column pruning, and caching
Consolidating underutilized clusters and retiring unused datasets to reduce operational overhead
Conducting regular cost reviews with finance and engineering to align spending with business value
Evaluating total cost of ownership (TCO) when choosing between open-source and managed cloud services

Module 9: Establishing Data Governance and Operational Sustainability

Forming a data governance council with representatives from legal, IT, and business units to set policies
Defining and enforcing data classification standards (public, internal, confidential, restricted)
Creating data dictionaries and business glossaries to standardize terminology across teams
Implementing data stewardship roles to manage quality, metadata, and compliance for critical datasets
Conducting regular data governance audits to assess policy adherence and identify gaps
Integrating data governance tools with CI/CD pipelines to enforce standards during deployment
Developing runbooks and escalation procedures for data incidents and outages
Planning for technical debt by scheduling refactoring of legacy pipelines and deprecated schemas