Data Lakes in Big Data

This curriculum spans the design and operationalization of enterprise-scale data lakes, comparable in scope to a multi-phase advisory engagement covering architecture, governance, security, and resilience across complex, distributed data environments.

Module 1: Architecting Scalable Data Lake Foundations

• Select storage layer technologies (e.g., S3, ADLS, GCS) based on compliance, throughput, and cross-region replication requirements.
• Design hierarchical data lake zone structures (raw, curated, trusted, archive) with retention policies and access controls per zone.
• Implement metadata partitioning strategies to optimize query performance and reduce compute costs in distributed environments.
• Evaluate file format trade-offs (Parquet vs. ORC vs. Avro) for compression, schema evolution, and query engine compatibility.
• Configure object storage lifecycle policies to automate tiering from hot to cold storage based on data access patterns.
• Integrate identity federation (e.g., SAML, OIDC) to align data lake access with enterprise IAM systems.
• Define naming conventions and tagging standards for assets to support auditability and cost allocation.
• Assess multi-cloud vs. single-cloud data lake strategies based on vendor lock-in, data egress costs, and SLA dependencies.

Module 2: Ingestion Pipeline Design and Orchestration

• Choose between batch and streaming ingestion patterns based on source system capabilities and downstream SLAs.
• Implement idempotent ingestion workflows to handle duplicate data from source retries or network issues.
• Configure change data capture (CDC) for transactional databases using Debezium or native log shipping.
• Design schema validation and rejection handling at ingestion to prevent downstream pipeline failures.
• Orchestrate complex dependencies across ingestion jobs using Airflow or equivalent with retry and alerting logic.
• Apply data masking during ingestion for PII fields when source systems lack native anonymization.
• Monitor ingestion latency and backpressure in streaming pipelines using Kafka consumer lag metrics.
• Optimize file sizing in object storage by coalescing small files from streaming sources using compaction jobs.

Module 3: Metadata Management and Data Discovery

• Deploy a centralized metadata repository (e.g., Apache Atlas, AWS Glue Data Catalog) with automated lineage capture.
• Integrate custom metadata extractors for non-standard data sources or legacy systems.
• Implement automated schema drift detection and alerting for upstream source changes.
• Configure metadata retention policies to avoid catalog bloat from transient or test datasets.
• Enforce metadata quality rules (e.g., mandatory business owner, data classification) during dataset registration.
• Expose metadata via API for integration with internal data portals and self-service analytics tools.
• Map technical metadata to business glossaries using automated and manual curation workflows.
• Track dataset usage patterns to identify stale or underutilized data for archival.

Module 4: Security, Access Control, and Compliance

• Implement attribute-based access control (ABAC) policies using tags and user attributes for fine-grained data access.
• Enforce encryption at rest and in transit with customer-managed keys (CMKs) and audit key usage.
• Integrate data lake access logs with SIEM systems for real-time anomaly detection.
• Apply dynamic data masking rules in query engines (e.g., Presto, Spark SQL) for sensitive fields.
• Conduct quarterly access reviews using automated reports of least-privilege violations.
• Implement data classification at rest using automated scanners for PII, PHI, and PCI data.
• Configure audit trails for all data access and modification events with immutable storage.
• Align data residency requirements with storage location policies across global regions.

Module 5: Data Quality and Observability

• Define and enforce data quality SLAs (completeness, accuracy, timeliness) per critical dataset.
• Implement automated data profiling during ingestion to detect null rates, value distributions, and outliers.
• Integrate data quality checks into CI/CD pipelines for data transformations.
• Configure alerting on data freshness using watermark tracking in streaming pipelines.
• Deploy synthetic test data injection to validate pipeline behavior during outages.
• Use statistical baselines to detect data distribution shifts indicating upstream issues.
• Instrument pipeline metrics (row counts, latency, failure rates) in centralized monitoring dashboards.
• Establish root cause analysis workflows for data quality incidents with escalation paths.

Module 6: Governance and Data Stewardship Frameworks

• Define data ownership and stewardship roles with RACI matrices for high-value datasets.
• Implement data catalog tagging for regulatory domains (e.g., GDPR, HIPAA, CCPA).
• Enforce data retention and deletion workflows based on legal hold and lifecycle policies.
• Conduct impact analysis for schema changes using lineage to assess downstream consumers.
• Integrate data governance workflows with Jira or ServiceNow for issue tracking.
• Automate policy compliance checks using Open Policy Agent or custom rule engines.
• Document data sourcing, transformation logic, and business definitions in the catalog.
• Perform regular data inventory audits to reconcile physical assets with governance records.

Module 7: Performance Optimization and Cost Management

• Apply predicate pushdown and column pruning in query design to minimize data scanned.
• Implement partitioning and bucketing strategies based on query access patterns.
• Use materialized views or summary tables for high-frequency analytical queries.
• Monitor and optimize compute资源配置 (e.g., Spark executors, shuffle partitions) per workload.
• Enforce query cost controls using query queuing and resource groups in shared clusters.
• Analyze storage access patterns to identify candidates for archive or deletion.
• Implement data skipping techniques (min/max statistics, Bloom filters) in Parquet files.
• Negotiate reserved capacity or sustained use discounts for predictable workloads.

Module 8: Advanced Analytics and Machine Learning Integration

• Expose curated data lake zones as feature stores for ML model training pipelines.
• Implement versioned datasets for reproducible model training and experimentation.
• Integrate ML metadata (model parameters, metrics) with data lineage for auditability.
• Secure access to training data using temporary credentials and short-lived tokens.
• Optimize data layout for distributed training frameworks (e.g., TensorFlow, PyTorch).
• Implement data drift monitoring in production models using statistical tests on input features.
• Enable feature engineering workflows directly on data lake data using Spark or Dask.
• Manage large-scale model output storage with lifecycle policies and access controls.

Module 9: Operational Resilience and Disaster Recovery

• Design cross-region replication for critical data with RPO and RTO targets.
• Test failover procedures for metadata stores and catalog services annually.
• Implement immutable backups using versioned storage and WORM policies.
• Document runbooks for data recovery scenarios including accidental deletion.
• Validate data consistency across regions using checksum validation jobs.
• Configure automated alerts for replication lag or sync failures in distributed storage.
• Plan for vendor-specific service outages by maintaining portable data formats and metadata.
• Conduct disaster recovery drills with stakeholders to validate communication and recovery timelines.