Skip to main content
Data Lakes in Big Data

Data Lakes in Big Data

$300.00
Your guarantee:
30-day money-back guarantee — no questions asked
When you get access:
Course access is prepared after purchase and delivered via email
How you learn:
Self-paced • Lifetime updates
Who trusts this:
Trusted by professionals in 160+ countries
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.
Adding to cart… The item has been added

What does the Data Lakes in Big Data course cover?

Data Lakes in Big Data is covered here in 9 modules: Architecting Scalable Data Lake Foundations, Ingestion Pipeline Design and Orchestration, Metadata Management and Data Discovery and 6 more. The outline lists 72 specific topics, opening with select storage layer technologies (e.g., S3, ADLS, GCS) based on compliance, throughput, and cross-region replication requirements.

How do you approach Data Lakes in Big Data step by step?

The work is sequenced in 9 stages. It starts with Architecting Scalable Data Lake Foundations, moves through Ingestion Pipeline Design and Orchestration and Metadata Management and Data Discovery, and ends at Operational Resilience and Disaster Recovery. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Data Lakes in Big Data course?

Module 1 is Architecting Scalable Data Lake Foundations. It works through 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.

How is the Data Lakes in Big Data course delivered?

The Data Lakes in Big Data course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.

How much does the Data Lakes in Big Data course cost?

The Data Lakes in Big Data course is $300 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.

Closely related courses: Data lake analytics in Big Data, Enterprise Data Lake Implementation for Big Data Analytics, Data Lakes and Big Data Analytics for Financial Services, Implementing Data Lakes for Big Data Analytics.

More answers: what you get with every course, refund policy, all help answers.

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.
!