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Data lake analytics in Self Development

Data lake analytics in Self Development

$299.00
How you learn:
Self-paced • Lifetime updates
Toolkit Included:
Includes a practical, ready-to-use toolkit containing implementation templates, worksheets, checklists, and decision-support materials used to accelerate real-world application and reduce setup time.
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Course access is prepared after purchase and delivered via email
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This curriculum spans the technical and governance dimensions of data lake development at the scale of a multi-workshop engineering initiative, covering the design, deployment, and operational rigor seen in enterprise data platform programs.

Module 1: Defining Data Lake Scope and Business Alignment

  • Select data domains to include in the lake based on stakeholder SLAs and downstream consumption requirements.
  • Negotiate data ingestion frequency with business units—real-time vs. batch—based on cost and latency constraints.
  • Document data ownership per domain to assign accountability for quality, access, and lineage.
  • Define retention policies for raw and processed zones in alignment with legal and compliance mandates.
  • Establish criteria for promoting datasets from experimental to production status.
  • Map critical data assets to business KPIs to justify investment and prioritize engineering effort.
  • Integrate data lake roadmap with enterprise data strategy to avoid siloed development.

Module 2: Storage Architecture and Data Layout Design

  • Choose between object storage vendors (e.g., S3, ADLS, GCS) based on egress costs and integration dependencies.
  • Partition large datasets by time and business key to optimize query performance and reduce scan costs.
  • Implement tiered storage strategies using lifecycle policies to move cold data to cheaper storage classes.
  • Decide on file format (Parquet, ORC, Avro) based on schema evolution needs and query engine compatibility.
  • Design zone-based structure (raw, curated, trusted, sandbox) with access controls enforced at directory level.
  • Balance compression ratio against query decompression overhead for frequently accessed tables.
  • Prevent small file proliferation by tuning batch job output settings or scheduling compaction routines.

Module 3: Ingestion Pipeline Engineering

  • Select ingestion method (batch, CDC, streaming) based on source system capabilities and data freshness needs.
  • Implement idempotent ingestion logic to support safe retries without duplication.
  • Configure error handling and dead-letter queues for failed records in streaming pipelines.
  • Apply schema validation at ingestion to prevent malformed data from entering curated zones.
  • Encrypt sensitive data in transit and at rest using platform-managed or customer-controlled keys.
  • Monitor ingestion pipeline latency and backpressure to detect source or network bottlenecks.
  • Version raw data snapshots to enable point-in-time recovery and reproducible processing.

Module 4: Data Transformation and Processing Frameworks

  • Choose between batch processing engines (Spark, Databricks, Dataflow) based on team expertise and vendor lock-in tolerance.
  • Orchestrate transformation workflows using Airflow or equivalent with dependency-aware scheduling.
  • Implement incremental processing logic to reduce compute costs and improve refresh speed.
  • Use temporary tables and atomic swaps to prevent partial or inconsistent data exposure during updates.
  • Optimize Spark executor memory and parallelism settings based on cluster node specifications.
  • Log transformation job metrics (rows processed, duration, errors) for performance benchmarking.
  • Isolate experimental transformations in sandbox environments before merging to production pipelines.

Module 5: Data Quality and Observability

  • Define and automate data quality checks (completeness, uniqueness, consistency) per critical dataset.
  • Set up alerting thresholds for data drift and anomaly detection in key metrics.
  • Integrate data profiling into CI/CD pipelines to catch schema changes pre-deployment.
  • Track data freshness SLAs and notify downstream consumers of delays.
  • Log data quality results in a central repository for audit and trend analysis.
  • Implement lineage tracking to map transformations from source to consumption layers.
  • Respond to data incident reports with root cause analysis and corrective runbooks.

Module 6: Access Control and Security Governance

  • Enforce role-based access control (RBAC) at the table and column level using platform-native tools.
  • Mask sensitive fields (PII, financials) dynamically based on user role and clearance.
  • Rotate access keys and credentials regularly and audit usage patterns for anomalies.
  • Integrate data lake authentication with corporate identity provider (e.g., Azure AD, Okta).
  • Conduct quarterly access reviews to deprovision stale user permissions.
  • Log all data access attempts for forensic analysis and compliance reporting.
  • Classify data assets by sensitivity level to apply appropriate encryption and retention rules.

Module 7: Query Optimization and Performance Tuning

  • Select query engine (Presto, Athena, BigQuery, Snowflake) based on concurrency and cost model.
  • Create and maintain statistics and metadata to improve query planner efficiency.
  • Use materialized views or aggregate tables to accelerate common reporting queries.
  • Limit wildcard SELECT statements in production jobs to reduce I/O costs.
  • Monitor query execution plans to identify inefficient joins or full table scans.
  • Implement result caching for frequently executed dashboards and API calls.
  • Set query timeouts and resource quotas to prevent runaway jobs from consuming cluster resources.

Module 8: Metadata Management and Discovery

  • Populate a central metadata catalog with technical, operational, and business context for each dataset.
  • Automate metadata extraction from ingestion and transformation pipelines.
  • Tag datasets with business terms from a governed data dictionary to improve searchability.
  • Expose metadata via API for integration with BI tools and self-service platforms.
  • Track dataset usage patterns to identify underutilized or orphaned assets.
  • Implement metadata versioning to audit changes in schema, ownership, or classification.
  • Enforce metadata completeness as a gate in deployment pipelines for new datasets.

Module 9: Cost Management and Operational Sustainability

  • Allocate storage and compute costs by team, project, or business unit using tagging and labeling.
  • Right-size compute clusters based on historical utilization and peak load patterns.
  • Negotiate reserved instances or committed use discounts for predictable workloads.
  • Implement automated shutdown policies for non-production environments.
  • Conduct monthly cost reviews to identify and eliminate wasteful queries or redundant datasets.
  • Optimize file sizes and partitioning to reduce metadata overhead and query scan volume.
  • Establish SLA-driven monitoring for cost, performance, and availability with escalation paths.
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