Skip to main content
Decision Support in Big Data

Decision Support in Big Data

$296.00
Your guarantee:
30-day money-back guarantee — no questions asked
How you learn:
Self-paced • Lifetime updates
When you get access:
Course access is prepared after purchase and delivered via email
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.
Who trusts this:
Trusted by professionals in 160+ countries
Adding to cart… The item has been added

This curriculum spans the technical and operational complexity of a multi-workshop program focused on building and maintaining enterprise-scale decision systems, comparable to the iterative development cycles seen in ongoing internal capability programs for data platform modernization.

Module 1: Foundations of Big Data Infrastructure for Decision Systems

  • Selecting distributed file systems (e.g., HDFS vs. cloud object storage) based on query latency and compliance requirements
  • Configuring cluster resource managers (YARN, Kubernetes) to balance batch and real-time decision workloads
  • Designing data partitioning strategies to optimize query performance on petabyte-scale datasets
  • Implementing data lifecycle policies for tiered storage across hot, warm, and cold layers
  • Integrating streaming ingestion (Kafka, Kinesis) with batch processing pipelines for hybrid decision architectures
  • Evaluating on-premises, hybrid, and cloud-native deployments based on data sovereignty and egress cost constraints
  • Establishing baseline monitoring for cluster health, including node failure recovery and data replication integrity
  • Defining schema evolution standards for Parquet and Avro to maintain backward compatibility in decision models

Module 2: Data Governance and Compliance in Decision Workflows

  • Mapping data lineage across ETL, ML pipelines, and reporting layers to satisfy audit requirements
  • Implementing role-based access control (RBAC) and attribute-based access control (ABAC) in data lakes
  • Enforcing data masking and tokenization for PII in development and testing environments
  • Designing retention and deletion workflows to comply with GDPR, CCPA, and industry-specific regulations
  • Integrating data catalog tools (e.g., Apache Atlas, DataHub) with metadata extraction from Spark and Airflow
  • Conducting data classification assessments to identify high-risk datasets in decision systems
  • Establishing data stewardship roles and escalation paths for data quality incidents
  • Implementing audit logging for data access and model inference in regulated environments

Module 3: Real-Time Data Ingestion and Stream Processing

  • Choosing between Kafka Streams, Flink, and Spark Structured Streaming based on exactly-once semantics needs
  • Designing event time handling and watermarking strategies for late-arriving data in decision pipelines
  • Implementing stateful processing with fault-tolerant checkpoints in stream applications
  • Scaling consumer groups to handle peak throughput during business-critical decision windows
  • Integrating schema registry with Avro to enforce contract consistency across microservices
  • Building stream-table joins to enrich real-time events with reference data from data warehouses
  • Handling backpressure in streaming pipelines to prevent system overload during data spikes
  • Validating data quality in motion using streaming assertions and anomaly detection

Module 4: Decision Model Development and Integration

  • Selecting between rule-based engines (Drools) and ML models based on interpretability and maintenance needs
  • Versioning decision logic using Git and CI/CD pipelines for rollback and auditability
  • Embedding decision models into microservices with gRPC or REST APIs for low-latency access
  • Implementing feature stores to ensure consistency between training and serving environments
  • Managing feature drift by monitoring input distributions in production decision systems
  • Designing fallback mechanisms for model degradation or service unavailability
  • Integrating business rules with probabilistic models to balance automation and human oversight
  • Profiling decision latency under load to meet SLAs in customer-facing applications

Module 5: Scalable Analytics and Query Optimization

  • Tuning query engines (Presto, Trino, Spark SQL) for complex analytical queries used in decision support
  • Designing materialized views and aggregations to reduce compute cost for recurring reports
  • Implementing predicate pushdown and column pruning to minimize data scanned in queries
  • Choosing between OLAP databases (ClickHouse, Druid) and data lakehouses based on query patterns
  • Partitioning and bucketing strategies in Delta Lake and Iceberg for high-concurrency access
  • Configuring cost-based optimizers with up-to-date table statistics for efficient query plans
  • Managing query queuing and resource isolation in multi-tenant analytics environments
  • Integrating query caching layers to accelerate dashboard and BI tool performance

Module 6: Monitoring, Observability, and Incident Response

  • Instrumenting decision pipelines with structured logging and distributed tracing (OpenTelemetry)
  • Defining SLOs and error budgets for data freshness, model accuracy, and API latency
  • Setting up anomaly detection on data drift and prediction skew using statistical process control
  • Creating alerting hierarchies to distinguish between operational noise and critical decision failures
  • Conducting root cause analysis for data pipeline breaks affecting downstream decisions
  • Implementing synthetic transactions to validate end-to-end decision logic availability
  • Archiving and indexing operational logs for forensic analysis during regulatory investigations
  • Coordinating incident response playbooks across data engineering, ML, and business teams

Module 7: Human-in-the-Loop and Decision Explainability

  • Designing escalation workflows for high-stakes decisions requiring human review
  • Integrating SHAP or LIME outputs into user interfaces for model transparency
  • Logging decision rationale and input context to support post-hoc audits
  • Implementing A/B testing frameworks to compare automated and manual decision outcomes
  • Configuring confidence thresholds to route low-certainty predictions to human agents
  • Developing feedback loops to capture user corrections and retrain models
  • Standardizing explanation formats across different model types (tree-based, neural networks)
  • Conducting usability testing with domain experts to refine decision support interfaces

Module 8: Performance, Cost, and Capacity Management

  • Right-sizing compute clusters based on historical utilization and forecasted decision load
  • Implementing autoscaling policies for cloud-based data and ML workloads
  • Optimizing storage formats and compression to reduce I/O and query costs
  • Conducting cost attribution by tagging resources to business units and decision use cases
  • Negotiating reserved instances and savings plans based on predictable usage patterns
  • Implementing data compaction routines to reduce small file overhead in distributed storage
  • Monitoring and controlling data duplication across staging, feature, and serving layers
  • Performing capacity planning for peak decision cycles (e.g., month-end, holiday seasons)

Module 9: Change Management and System Evolution

  • Planning schema migrations in distributed systems with zero-downtime constraints
  • Coordinating cross-team rollouts of updated decision logic with backward compatibility
  • Managing technical debt in data pipelines through scheduled refactoring sprints
  • Deprecating legacy decision systems with parallel run validation and traffic shadowing
  • Documenting system architecture decisions using ADRs (Architecture Decision Records)
  • Establishing version compatibility matrices for APIs between data and decision services
  • Conducting post-implementation reviews to assess decision system effectiveness
  • Updating disaster recovery and backup strategies as data volumes and dependencies grow
!