Skip to main content
Data Warehouses in Big Data

Data Warehouses in Big Data

$299.00
How you learn:
Self-paced • Lifetime updates
Who trusts this:
Trusted by professionals in 160+ countries
When you get access:
Course access is prepared after purchase and delivered via email
Your guarantee:
30-day money-back guarantee — no questions asked
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

This curriculum spans the technical and organizational complexities of integrating data warehouses within big data environments, comparable in scope to a multi-workshop technical advisory engagement focused on enterprise data platform modernization.

Module 1: Strategic Alignment of Data Warehouses with Big Data Ecosystems

  • Decide whether to extend an existing enterprise data warehouse (EDW) or build a separate big data warehouse based on data velocity, variety, and organizational data governance maturity.
  • Assess integration points between legacy EDWs and Hadoop/Spark clusters to determine data replication frequency and latency tolerance for operational reporting.
  • Negotiate data ownership and stewardship responsibilities between data engineering teams and business units during cross-functional data warehouse planning.
  • Select between cloud-native data warehouses (e.g., Snowflake, Redshift, BigQuery) and on-premises solutions based on regulatory constraints and long-term TCO projections.
  • Define SLAs for data availability and refresh cycles that align with business reporting requirements and upstream data pipeline capabilities.
  • Establish criteria for data inclusion in the warehouse versus leaving it in data lake staging zones based on usage frequency and compliance needs.
  • Conduct workload profiling to determine whether mixed OLAP and operational workloads should be isolated or co-located.
  • Develop a phased migration roadmap from monolithic ETL to ELT patterns when transitioning to cloud data platforms.

Module 2: Data Modeling for Scalable Analytics

  • Choose between normalized, dimensional, and data vault modeling based on query patterns, auditability needs, and source system volatility.
  • Implement slowly changing dimension (SCD) Type 2 tracking in distributed environments while managing storage growth and query performance.
  • Design partitioning and clustering strategies in cloud data warehouses to reduce scan costs and improve query response times.
  • Balance denormalization for performance against data consistency risks in real-time analytical workloads.
  • Define surrogate key generation strategies across distributed sources with inconsistent natural keys.
  • Model semi-structured data (JSON, Avro) in columnar formats while preserving schema evolution capabilities.
  • Integrate time-series data patterns into fact tables with appropriate grain and aggregation rules.
  • Address late-arriving dimensions by implementing placeholder records or reprocessing logic in streaming pipelines.

Module 3: Ingestion Architecture and Pipeline Orchestration

  • Select batch frequency (daily, hourly) versus micro-batch or streaming ingestion based on business impact of data staleness.
  • Implement change data capture (CDC) using log-based tools (e.g., Debezium) and assess impact on source database performance.
  • Design idempotent ingestion pipelines to handle retries and duplicate message scenarios in distributed systems.
  • Choose between push and pull ingestion patterns when integrating third-party SaaS applications with rate limits and API constraints.
  • Orchestrate interdependent data pipelines using tools like Airflow or Dagster while managing resource contention and backfill operations.
  • Implement schema validation and drift detection at ingestion points to prevent downstream processing failures.
  • Configure retry policies and dead-letter queues for failed records in streaming ingestion workflows.
  • Monitor pipeline latency and throughput to identify bottlenecks in source-to-target data flow.

Module 4: Storage Optimization and Data Lifecycle Management

  • Define tiered storage policies (hot, warm, cold) based on access frequency and cost-performance trade-offs in cloud storage.
  • Implement data compaction strategies for Parquet or ORC files to reduce file count and improve query efficiency.
  • Apply data masking or tokenization at rest for sensitive fields based on compliance requirements (e.g., GDPR, HIPAA).
  • Set retention policies for historical data and automate archival to lower-cost storage or deletion processes.
  • Optimize compression algorithms (Snappy, Zstandard) based on CPU overhead and storage savings in query-intensive environments.
  • Manage metadata lifecycle in external catalogs (e.g., AWS Glue, Unity Catalog) when datasets are archived or restructured.
  • Balance columnar format benefits against write amplification in high-frequency update scenarios.
  • Implement data versioning strategies for analytical datasets to support reproducible reporting and rollback capabilities.

Module 5: Query Performance and Workload Management

  • Size and configure compute resources (e.g., Snowflake warehouses, Redshift clusters) based on concurrent user load and query complexity.
  • Implement materialized views or pre-aggregated summary tables to accelerate common analytical queries.
  • Use query profiling tools to identify and rewrite inefficient SQL patterns (e.g., cross-joins, unnecessary scans).
  • Configure workload management (WLM) rules to prioritize critical reporting queries over ad hoc exploration.
  • Implement result caching strategies while managing cache invalidation during data refresh cycles.
  • Monitor and limit runaway queries using time-out thresholds and resource quotas per user or role.
  • Optimize join ordering and distribution strategies in distributed query engines to minimize data shuffling.
  • Evaluate the use of approximate query processing for large datasets when exact results are not required.

Module 6: Security, Compliance, and Data Governance

  • Implement row-level and column-level security policies based on user roles and data sensitivity classifications.
  • Integrate data warehouse access controls with enterprise identity providers (e.g., SAML, Okta) and manage role propagation.
  • Conduct data classification scans to identify PII/PHI and enforce encryption or masking policies.
  • Design audit logging to capture data access, query execution, and schema changes for compliance reporting.
  • Establish data lineage tracking from source systems to warehouse tables to support impact analysis and regulatory audits.
  • Negotiate data sharing agreements when enabling cross-departmental or external access to warehouse datasets.
  • Implement dynamic data masking rules that vary based on user clearance levels and query context.
  • Enforce data retention and deletion workflows in alignment with legal hold requirements.

Module 7: Data Quality and Observability

  • Define data quality rules (completeness, uniqueness, consistency) per critical data asset and automate validation checks.
  • Implement automated alerting for data pipeline failures, schema drift, or unexpected data volume drops.
  • Track data freshness by monitoring the delta between source system timestamps and warehouse load times.
  • Use statistical profiling to detect anomalies in numerical distributions or categorical value shifts.
  • Integrate data observability tools (e.g., Great Expectations, Monte Carlo) into CI/CD pipelines for data changes.
  • Establish ownership escalation paths for resolving data quality issues identified in production reports.
  • Log and analyze rejected records during transformation to identify systemic source system issues.
  • Measure and report on data reliability metrics (e.g., % of pipelines meeting SLA) to stakeholders.

Module 8: Cloud Cost Management and Resource Optimization

  • Implement auto-suspend and auto-resize policies for cloud data warehouse compute clusters to reduce idle costs.
  • Allocate cost centers and tag resources by department, project, or workload to enable chargeback modeling.
  • Monitor query cost per user and set thresholds to prevent excessive spending on exploratory analytics.
  • Optimize storage-compute separation by scaling compute independently based on workload demand.
  • Evaluate reserved capacity or savings plans against variable usage patterns to determine cost savings.
  • Conduct regular cost reviews to identify underutilized tables, unused schemas, or redundant data copies.
  • Implement data pruning and filtering at ingestion to reduce downstream storage and compute consumption.
  • Compare cost-performance trade-offs of different instance types or regions for data warehouse deployments.

Module 9: Advanced Analytics Integration and Future-Proofing

  • Expose warehouse data to machine learning platforms via secure, versioned feature stores with refresh SLAs.
  • Enable direct querying of external data lake files from the warehouse to reduce data duplication.
  • Integrate real-time analytics capabilities using streaming ingestion and in-database processing extensions.
  • Support self-service analytics by publishing curated datasets and business definitions in a discoverable data catalog.
  • Implement semantic layer tools (e.g., LookML, dbt metrics) to standardize business logic across reporting tools.
  • Plan for multi-cloud or hybrid deployments when vendor lock-in poses strategic risk.
  • Adopt infrastructure-as-code (IaC) practices to manage warehouse configurations, permissions, and pipelines.
  • Design extensible metadata frameworks to support AI-driven data discovery and automated lineage inference.
!