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Data Warehousing in Big Data

Data Warehousing in Big Data

$298.00
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What does the Data Warehousing in Big Data course cover?

Data Warehousing in Big Data is covered here in 9 modules: Architecting Scalable Data Warehouse Foundations on Distributed Systems, Enterprise Data Modeling for Big Data Environments, Real-Time Ingestion and Stream Processing Integration and 6 more. The outline lists 72 specific topics, opening with selecting between Lambda and Kappa architectures based on real-time SLA requirements and data reprocessing frequency and closing with optimizing.

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

The work is sequenced in 9 stages. It starts with Architecting Scalable Data Warehouse Foundations on Distributed Systems, moves through Enterprise Data Modeling for Big Data Environments and Real-Time Ingestion and Stream Processing Integration, and ends at Advanced Analytics and Machine Learning Integration. Each stage carries its own topic list, so the sequence is followed rather than summarised.

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

Module 1 is Architecting Scalable Data Warehouse Foundations on Distributed Systems. It works through selecting between Lambda and Kappa architectures based on real-time SLA requirements and data reprocessing frequency, designing partitioning and bucketing strategies in Delta Lake or Iceberg to optimize query performance on petabyte-scale datasets, configuring cluster sizing and autoscaling policies in cloud-based Spark environments to balance cost and throughput and.

How is the Data Warehousing in Big Data course delivered?

The Data Warehousing 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 Warehousing in Big Data course cost?

The Data Warehousing 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 Warehousing Toolkit, Data Warehousing Consulting Toolkit, Big Data in Big Data, Data Warehousing in Data replication Dataset.

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

This curriculum spans the technical and operational complexity of a multi-workshop program for building and operating a cloud-native data warehouse, comparable to the design and implementation efforts seen in enterprise advisory engagements focused on scalable data platforms.

Module 1: Architecting Scalable Data Warehouse Foundations on Distributed Systems

  • Selecting between Lambda and Kappa architectures based on real-time SLA requirements and data reprocessing frequency
  • Designing partitioning and bucketing strategies in Delta Lake or Iceberg to optimize query performance on petabyte-scale datasets
  • Configuring cluster sizing and autoscaling policies in cloud-based Spark environments to balance cost and throughput
  • Implementing schema evolution mechanisms in Parquet-based storage to maintain backward compatibility during field additions or type changes
  • Choosing ingestion patterns (batch vs. micro-batch) based on source system capabilities and downstream data freshness needs
  • Integrating metastore solutions (e.g., AWS Glue, Unity Catalog) across multiple compute engines for consistent schema management
  • Validating data consistency across distributed nodes after ETL job failures using transaction log inspection
  • Defining retry logic and idempotency in ingestion pipelines to prevent data duplication under transient network failures

Module 2: Enterprise Data Modeling for Big Data Environments

  • Adapting dimensional modeling techniques to semi-structured data sources with nested JSON or Avro formats
  • Implementing slowly changing dimension (SCD) Type 2 logic in immutable data lake architectures using merge operations
  • Designing conformed dimensions across multiple business domains to enable cross-functional reporting
  • Choosing between 3NF and dimensional models based on query patterns and data marts' consumption needs
  • Handling schema drift in streaming data by implementing dynamic schema inference with validation guardrails
  • Modeling time-series data with time-windowed aggregations to reduce storage footprint while preserving analytical utility
  • Creating bridge tables to manage many-to-many relationships in star schemas without denormalizing into wide tables
  • Optimizing fact table grain decisions when integrating high-frequency IoT sensor data with transactional systems

Module 3: Real-Time Ingestion and Stream Processing Integration

  • Configuring Kafka topic retention and compaction policies to align with data warehouse replay requirements
  • Deploying Kafka Connect with transformation chains to cleanse and enrich data before landing in the warehouse
  • Implementing exactly-once semantics in Spark Structured Streaming using checkpointing and idempotent sinks
  • Designing watermark strategies to handle late-arriving data in time-based aggregations without excessive state accumulation
  • Selecting between stateful and stateless transformations based on business logic complexity and fault tolerance needs
  • Integrating Flink SQL jobs with Hive metastore for unified schema access across batch and streaming pipelines
  • Monitoring end-to-end latency from source to materialized view using distributed tracing and watermark tracking
  • Scaling stream processing parallelism by tuning partition counts and avoiding data skew in key distribution

Module 4: Data Quality, Validation, and Observability at Scale

  • Embedding Great Expectations or Soda Core checks into ingestion pipelines to enforce schema and value constraints
  • Designing data freshness monitors using timestamp validation with alerting thresholds based on business SLAs
  • Implementing row-count reconciliation between source systems and data warehouse layers for auditability
  • Creating lineage-aware validation rules that trigger reprocessing when upstream data changes unexpectedly
  • Using statistical profiling to detect data distribution shifts indicative of source system changes or pipeline corruption
  • Configuring anomaly detection on metric time series (e.g., null rates, cardinality) using seasonal decomposition models
  • Integrating data quality dashboards with incident management systems for automated ticket creation
  • Defining escalation paths for data incidents based on severity, impacted consumers, and regulatory exposure

Module 5: Security, Access Control, and Compliance in Distributed Warehouses

  • Implementing column-level masking policies in Unity Catalog or AWS Lake Formation based on user roles and PII sensitivity
  • Enforcing row-level security using dynamic filters in SQL query engines for multi-tenant data access
  • Managing encryption key lifecycle for data-at-rest using cloud KMS with rotation and audit logging enabled
  • Configuring audit trails to capture query activity, access attempts, and DDL changes across distributed compute clusters
  • Applying GDPR and CCPA compliance controls through automated PII detection and data retention enforcement
  • Integrating with enterprise identity providers using SAML or OIDC for centralized user authentication
  • Designing secure cross-account data sharing patterns using AWS RAM or Azure Lighthouse
  • Validating compliance with SOC 2 and ISO 27001 through automated configuration scanning and evidence collection

Module 6: Performance Optimization and Cost Management

  • Tuning Spark executor memory and core allocation to minimize garbage collection and maximize shuffle efficiency
  • Implementing Z-Order indexing on multi-dimensional filters to reduce file scanning in large tables
  • Designing materialized view refresh strategies to balance query performance and compute cost
  • Using query plan analysis to identify and eliminate Cartesian joins in ad-hoc reporting workloads
  • Applying data compaction routines to reduce small file proliferation and improve read performance
  • Setting up cost allocation tags in cloud billing to attribute warehouse usage to business units or projects
  • Implementing query queuing and workload management to prevent resource starvation during peak loads
  • Optimizing storage tiering by moving cold data to lower-cost storage classes with automated lifecycle policies

Module 7: Data Governance and Metadata Management

  • Deploying automated metadata extractors to capture technical lineage from ETL job configurations and SQL scripts
  • Implementing business glossary integration with data catalog tools to link technical assets to business terms
  • Enforcing data ownership and stewardship assignments through catalog-based approval workflows
  • Creating data product manifests with defined SLAs, quality metrics, and usage documentation
  • Using schema registry tools to version and govern Avro or Protobuf schemas across microservices
  • Implementing data retention policies with automated purging based on regulatory and business requirements
  • Integrating catalog search with natural language processing to improve discoverability for non-technical users
  • Generating data inventory reports for compliance audits with classification and sensitivity tagging

Module 8: Cloud-Native Deployment and Operational Resilience

  • Designing multi-region deployment strategies for disaster recovery with cross-region replication of metadata and data
  • Implementing infrastructure-as-code using Terraform or Pulumi to version and deploy warehouse environments
  • Configuring CI/CD pipelines for ETL code with automated testing and rollback capabilities
  • Setting up health checks and auto-healing for long-running streaming jobs using Kubernetes liveness probes
  • Managing dependency conflicts in Python or Scala libraries across shared cluster environments
  • Planning capacity for peak workloads during month-end reporting cycles with preemptive cluster scaling
  • Documenting runbooks for common failure scenarios including metastore outages and credential expiration
  • Conducting chaos engineering tests to validate resilience of data pipelines under network partition conditions

Module 9: Advanced Analytics and Machine Learning Integration

  • Preparing feature stores by aggregating raw data into time-windowed, versioned feature sets for model training
  • Synchronizing feature engineering logic between batch and real-time pipelines to prevent training-serving skew
  • Exporting model inference results back to the data warehouse with transactional consistency guarantees
  • Implementing model monitoring by logging predictions and actuals for drift and performance analysis
  • Using warehouse-native ML engines (e.g., BigQuery ML, Redshift ML) for rapid prototyping without data movement
  • Designing backfill processes for historical feature generation with efficient date-range partitioning
  • Securing access to model training datasets with the same governance policies as operational reporting data
  • Optimizing feature storage using columnar formats with dictionary encoding to reduce I/O during training
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