Data lake analytics in Big Data

This curriculum spans the technical and operational breadth of a multi-workshop program typically delivered during a data lake modernization advisory engagement, covering architecture, pipeline engineering, governance, and operations at the scale of a multi-team internal capability build.

Module 1: Data Lake Architecture and Platform Selection

• Evaluate trade-offs between cloud-native data lakes (e.g., AWS S3, Azure Data Lake Storage) and on-prem Hadoop-based deployments based on compliance, latency, and egress cost constraints.
• Design a multi-zone data lake structure (raw, trusted, curated) with explicit access controls and lifecycle policies for each zone.
• Select file formats (Parquet, ORC, Avro) based on query patterns, compression needs, and schema evolution requirements.
• Implement metadata management using Apache Atlas or cloud-native equivalents to ensure lineage and classification consistency.
• Decide on a metadata catalog strategy—integrated (e.g., AWS Glue) vs. open-source (e.g., Apache Hive Metastore)—based on ecosystem compatibility.
• Plan for cross-region replication and disaster recovery in cloud data lakes, including versioning and immutable storage configurations.
• Assess performance implications of object storage versus distributed file systems for high-concurrency analytical workloads.

Module 2: Data Ingestion and Pipeline Orchestration

• Choose between batch and streaming ingestion based on SLA requirements, source system capabilities, and downstream latency tolerance.
• Implement change data capture (CDC) from transactional databases using Debezium or cloud-managed services while managing log retention and schema drift.
• Configure Apache Kafka or cloud equivalents (e.g., Amazon Kinesis) for scalable event ingestion with proper partitioning and retention policies.
• Design idempotent ingestion pipelines to handle retries and duplicate records without corrupting data integrity.
• Orchestrate complex ETL workflows using Apache Airflow or cloud orchestrators, including failure handling, alerting, and dependency management.
• Implement backpressure mechanisms in streaming pipelines to prevent consumer lag and data loss under load spikes.
• Integrate unstructured data (logs, images, JSON) into the data lake with schema-on-read validation and metadata tagging.

Module 3: Data Quality and Validation at Scale

• Define data quality rules (completeness, consistency, accuracy) per domain and implement automated checks using Great Expectations or Deequ.
• Integrate data profiling into ingestion pipelines to detect schema anomalies and value distribution shifts.
• Handle nulls, duplicates, and outliers in raw data without premature cleansing that could bias downstream analysis.
• Implement quarantine zones for failed validation records with automated notifications and reprocessing workflows.
• Track data quality metrics over time and correlate them with upstream system changes or pipeline updates.
• Balance strict validation enforcement against operational continuity when dealing with legacy or third-party data sources.
• Design versioned data contracts between producers and consumers to manage schema evolution and deprecation.

Module 4: Metadata Management and Data Discovery

• Automate technical metadata extraction (schema, size, frequency) during ingestion and make it queryable via a central catalog.
• Implement business metadata tagging (ownership, sensitivity, purpose) with governance workflows for approval and updates.
• Integrate data lineage tracking from source to consumption to support impact analysis and regulatory audits.
• Configure search and discovery interfaces with faceted filtering based on domain, owner, and usage patterns.
• Enforce metadata completeness as a gate in CI/CD pipelines for data assets.
• Manage stale or unused datasets through automated tagging and archival policies based on access frequency.
• Sync metadata across hybrid environments where parts of the data lake reside on-prem and in cloud.

Module 5: Security, Access Control, and Compliance

• Implement role-based and attribute-based access control (RBAC/ABAC) at object and column levels in the data lake.
• Encrypt data at rest and in transit using managed keys (KMS) with strict key rotation and access auditing.
• Mask or redact sensitive fields (PII, PCI) dynamically based on user roles and query context.
• Integrate with enterprise identity providers (e.g., Active Directory, Okta) for centralized authentication.
• Log and monitor all data access patterns using audit trails for forensic analysis and compliance reporting.
• Implement data retention and deletion workflows to comply with GDPR, CCPA, and other regulatory requirements.
• Conduct regular access reviews and certification campaigns to eliminate privilege creep.

Module 6: Query Optimization and Performance Engineering

• Partition and bucket large datasets based on query patterns to reduce scan volume and improve performance.
• Implement data skipping techniques using min/max statistics, bloom filters, or zone maps in columnar formats.
• Tune query engines (Spark, Presto, Trino) with appropriate memory allocation, parallelism, and shuffle settings.
• Use materialized views or aggregate tables for frequently accessed summaries while managing update overhead.
• Monitor and optimize file sizes to avoid small file problems and maximize I/O efficiency.
• Implement cost controls for cloud query services by setting query timeouts and concurrency limits.
• Profile slow queries using execution plans and identify bottlenecks in joins, filters, or data skew.

Module 7: Scalable Analytics with Distributed Compute

• Select compute frameworks (Spark, Flink, Dask) based on workload type—batch, streaming, or interactive.
• Configure auto-scaling clusters with spot/preemptible instances while managing job checkpointing and fault tolerance.
• Optimize data locality by co-locating compute with storage in the same region or availability zone.
• Manage shared cluster resources using workload isolation (YARN queues, Kubernetes namespaces) and quotas.
• Implement checkpointing and state management in streaming applications to ensure exactly-once processing.
• Integrate machine learning workloads with distributed training frameworks using shared data lake access.
• Benchmark performance across different instance types and storage classes to optimize cost-performance ratio.

Module 8: Monitoring, Observability, and Cost Management

• Instrument pipelines with structured logging and metrics collection for latency, throughput, and error rates.
• Set up alerting on SLA breaches, pipeline failures, or data freshness degradation.
• Track storage growth by zone, project, and team to identify cost outliers and enforce quotas.
• Monitor query costs by user and workload to allocate charges and detect inefficient patterns.
• Use distributed tracing to diagnose latency across ingestion, transformation, and query layers.
• Implement automated cleanup of temporary files, failed job artifacts, and outdated snapshots.
• Generate monthly cost reports broken down by storage, compute, and egress for financial governance.

Module 9: Governance, Stewardship, and Lifecycle Management

• Define data ownership and stewardship roles with documented responsibilities and escalation paths.
• Implement data classification policies (public, internal, confidential) with automated labeling and enforcement.
• Create a data governance council to review high-risk changes and resolve cross-domain conflicts.
• Establish a lifecycle policy for datasets including archival, deletion, and retention based on legal and business needs.
• Manage schema evolution using backward- and forward-compatible changes with deprecation timelines.
• Conduct regular data quality and compliance audits using automated tooling and documented procedures.
• Integrate data governance into DevOps workflows with pre-deployment validation and policy-as-code checks.