Description

This curriculum spans the technical and operational breadth of a multi-workshop data lake implementation program, addressing architecture, pipeline engineering, governance, and production operations at the level of detail found in enterprise advisory engagements.

Module 1: Defining Data Lake Architecture and Scope

Selecting between object storage (e.g., S3, ADLS) and distributed file systems based on compliance, throughput, and cost per terabyte.
Deciding on a centralized vs. federated data lake model to balance control with departmental autonomy.
Establishing naming conventions and metadata tagging standards for datasets at ingestion to ensure discoverability.
Choosing between schema-on-read and schema-on-write based on source system volatility and downstream SLAs.
Integrating existing data warehouse subject areas into the data lake without duplicating ETL pipelines.
Defining data retention policies for raw, processed, and curated zones based on legal holds and business needs.
Mapping data lineage requirements early to support auditability in regulated domains.
Evaluating multi-region replication needs for disaster recovery and data sovereignty compliance.

Module 2: Ingestion Pipeline Design and Implementation

Configuring batch vs. streaming ingestion based on source system capabilities and latency requirements.
Implementing change data capture (CDC) for transactional databases using Debezium or native log shipping.
Designing idempotent ingestion jobs to handle duplicate or out-of-order data in distributed environments.
Selecting serialization formats (e.g., Parquet, Avro, JSON) based on compression, schema evolution, and query performance.
Handling schema drift from source systems by implementing schema registry integration and validation rules.
Securing data in transit using TLS and managing credentials via secret management systems (e.g., HashiCorp Vault).
Monitoring ingestion pipeline health with alerting on lag, failure rates, and data volume anomalies.
Partitioning raw data by time and source to optimize downstream processing and access patterns.

Module 3: Data Quality and Validation Frameworks

Implementing field-level validation rules (e.g., null checks, regex patterns) during or immediately after ingestion.
Designing automated data profiling jobs to detect anomalies in distribution, cardinality, or range.
Integrating data quality metrics into pipeline orchestration (e.g., Airflow) to gate downstream processing.
Establishing thresholds for acceptable data completeness and accuracy per data domain.
Logging data quality violations to a centralized monitoring dashboard with root cause tagging.
Creating quarantine zones for invalid records with automated reprocessing workflows.
Collaborating with domain owners to define business-specific data quality rules and ownership.
Versioning data quality rules to track changes and support rollback during incident response.

Module 4: Metadata Management and Data Discovery

Selecting and configuring a metadata catalog (e.g., Apache Atlas, AWS Glue Data Catalog) with custom classification.
Automating technical metadata extraction (e.g., schema, size, update frequency) from ingestion pipelines.
Implementing business metadata tagging through integration with data stewardship workflows.
Enabling full-text search across dataset names, descriptions, and column-level annotations.
Integrating lineage tracking from source to curated layers using parser-based or agent-driven tools.
Controlling metadata access via role-based permissions aligned with data access policies.
Scheduling metadata health checks to detect stale or orphaned entries.
Exposing metadata via API for integration with BI and self-service analytics platforms.

Module 5: Security, Access Control, and Compliance

Implementing column- and row-level security using policy engines (e.g., Apache Ranger, Unity Catalog).
Managing identity federation across cloud providers and on-prem directories using SAML or OIDC.
Encrypting data at rest with customer-managed keys and auditing key access logs.
Enforcing data masking rules for PII/PHI in non-production environments.
Generating audit logs for all data access events and integrating with SIEM systems.
Conducting periodic access reviews to deprovision stale user and service account permissions.
Mapping data classifications (e.g., public, internal, confidential) to storage and access policies.
Validating GDPR, CCPA, and HIPAA compliance through automated policy checks and documentation.

Module 6: Data Transformation and Curation

Designing idempotent transformation jobs to support reproducible data pipelines.
Selecting between batch processing (Spark) and incremental processing (Delta Lake, Snowflake streams).
Implementing slowly changing dimension logic for master data in dimensional models.
Optimizing partitioning and bucketing strategies for large fact tables to reduce query scan costs.
Versioning curated datasets to enable point-in-time analysis and rollback.
Documenting transformation logic in code and linking to business definitions in the metadata catalog.
Validating output data distributions and row counts before publishing to downstream consumers.
Orchestrating dependent jobs with error handling, retries, and alerting on SLA breaches.

Module 7: Query Optimization and Performance Engineering

Choosing file formats and compression codecs based on query patterns (scan-heavy vs. point lookup).
Implementing file compaction and vacuuming routines to manage small file problems.
Using statistics and histogram collection to improve query planner efficiency.
Designing indexing strategies for high-cardinality filter columns in data lakehouse environments.
Configuring compute clusters with appropriate memory, CPU, and concurrency settings.
Monitoring query performance trends and identifying long-running or resource-intensive jobs.
Implementing query caching or materialized views for frequently accessed aggregations.
Right-sizing cluster autoscaling policies to balance cost and performance during peak loads.

Module 8: Monitoring, Observability, and Incident Response

Instrumenting pipelines with structured logging and distributed tracing for root cause analysis.
Setting up alerts for data freshness, pipeline failures, and data quality threshold breaches.
Creating runbooks for common failure scenarios (e.g., source API downtime, schema mismatch).
Tracking end-to-end data latency from source to curated layer with timestamp propagation.
Establishing incident escalation paths and on-call rotations for critical data products.
Conducting blameless post-mortems to document systemic issues and action items.
Measuring and reporting SLA/SLO compliance for key data assets to stakeholders.
Integrating observability tools (e.g., Datadog, Grafana) with pipeline orchestration and storage layers.

Module 9: Governance, Stewardship, and Lifecycle Management

Defining data ownership and stewardship roles for each domain and critical dataset.
Implementing automated classification of sensitive data using pattern matching and NLP.
Creating data retention and archival workflows based on legal and operational requirements.
Establishing a data change advisory board for approving schema or pipeline modifications.
Documenting data policies and making them accessible via internal knowledge portals.
Conducting quarterly data governance audits to verify policy enforcement and compliance.
Managing dataset deprecation and sunsetting with consumer notification workflows.
Integrating data governance metrics into executive dashboards for oversight and reporting.