Skip to main content
Process Management in Big Data

Process Management in Big Data

$302.00
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.
How you learn:
Self-paced • Lifetime updates
Who trusts this:
Trusted by professionals in 160+ countries
Your guarantee:
30-day money-back guarantee — no questions asked
Adding to cart… The item has been added

What does the Process Management in Big Data course cover?

Process Management in Big Data is covered here in 9 modules: Defining Data Pipeline Architecture at Scale, Orchestration Framework Selection and Configuration, Data Quality and Validation Engineering and 6 more. The outline lists 72 specific topics, opening with selecting between batch, micro-batch, and streaming ingestion based on SLA requirements and source system capabilities and closing with integrating data change notifications into team.

How do you approach Process Management in Big Data step by step?

The work is sequenced in 9 stages. It starts with Defining Data Pipeline Architecture at Scale, moves through Orchestration Framework Selection and Configuration and Data Quality and Validation Engineering, and ends at Change Management and Cross-Team Collaboration. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Process Management in Big Data course?

Module 1 is Defining Data Pipeline Architecture at Scale. It works through selecting between batch, micro-batch, and streaming ingestion based on SLA requirements and source system capabilities, designing idempotent processing stages to handle duplicate message delivery in distributed queues, choosing serialization formats (Avro, Parquet, Protobuf) based on schema evolution needs and downstream consumption patterns and 5 more.

How is the Process Management in Big Data course delivered?

The Process Management 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 Process Management in Big Data course cost?

The Process Management in Big Data course is $298 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: Big data processing in Big Data, Data Processing in Big Data, Archival processes in Big Data, Process Modelling in Big Data.

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

This curriculum spans the technical and organisational complexity of a multi-workshop program focused on building and operating data platforms in large enterprises, covering the full lifecycle from pipeline architecture and governance to cost optimisation and cross-team coordination.

Module 1: Defining Data Pipeline Architecture at Scale

  • Selecting between batch, micro-batch, and streaming ingestion based on SLA requirements and source system capabilities
  • Designing idempotent processing stages to handle duplicate message delivery in distributed queues
  • Choosing serialization formats (Avro, Parquet, Protobuf) based on schema evolution needs and downstream consumption patterns
  • Implementing schema registry integration to enforce backward and forward compatibility in evolving data contracts
  • Partitioning strategies for large fact tables to balance query performance and storage efficiency
  • Configuring retry mechanisms with exponential backoff for transient failures in cross-system communication
  • Establishing data lineage tracking at the field level for auditability and impact analysis
  • Defining ownership and stewardship roles for pipeline components across data engineering and domain teams

Module 2: Orchestration Framework Selection and Configuration

  • Evaluating Airflow, Prefect, or Dagster based on team expertise, observability needs, and dynamic DAG generation requirements
  • Designing DAG structure to minimize cross-dependencies while maintaining business process integrity
  • Implementing dynamic task generation for parameterized workflows processing multiple tenants or regions
  • Setting up alerting thresholds for task duration, backfill volume, and sensor timeouts
  • Securing connections and variables using role-based access and backend-secrets integration
  • Managing deployment workflows for DAG updates using CI/CD with rollback capability
  • Handling backfill operations without overloading downstream systems or storage tiers
  • Integrating custom operators for legacy systems lacking native connector support

Module 3: Data Quality and Validation Engineering

  • Embedding Great Expectations or custom validators into pipeline stages for early failure detection
  • Defining acceptable null rates and distribution bounds for critical business metrics
  • Implementing quarantine zones for records failing validation without blocking pipeline progression
  • Automating schema drift detection and routing alerts to data stewards for review
  • Configuring sampling strategies for validation on multi-terabyte datasets
  • Establishing escalation paths for recurring data quality incidents tied to upstream systems
  • Versioning data expectations to support A/B testing and gradual rollout of new rules
  • Measuring validation overhead and optimizing execution to avoid pipeline bottlenecks

Module 4: Metadata Management and Discovery

  • Integrating automated metadata extraction from ETL jobs into centralized catalog (e.g., DataHub, Atlas)
  • Mapping technical column names to business glossary terms with ownership attribution
  • Scheduling freshness checks and surfacing stale datasets in discovery interfaces
  • Implementing access-controlled metadata views based on user roles and data classification
  • Tracking dataset dependencies to assess impact of deprecation or schema changes
  • Enabling user feedback mechanisms (e.g., ratings, annotations) on dataset usability
  • Harvesting usage statistics from query logs to prioritize curation efforts
  • Automating PII detection and tagging to support compliance workflows

Module 5: Scalable Storage and Data Lakehouse Design

  • Choosing between Delta Lake, Iceberg, and Hudi based on transactional requirements and compute engine compatibility
  • Implementing time-travel and version rollback capabilities for debugging and recovery
  • Configuring partitioning and clustering keys to optimize query performance on large tables
  • Setting up lifecycle policies to transition cold data to lower-cost storage tiers
  • Enforcing file size targets to prevent small-file problems in object storage
  • Designing folder structures that support efficient partition pruning and access control
  • Managing concurrent writes using optimistic concurrency control or locking mechanisms
  • Validating data integrity after compaction and optimization jobs

Module 6: Monitoring, Alerting, and Incident Response

  • Instrumenting pipelines with structured logging and distributed tracing for root cause analysis
  • Defining SLOs for pipeline completion time and data freshness with error budget policies
  • Creating alert suppression rules to avoid noise during scheduled maintenance windows
  • Integrating with incident management systems (e.g., PagerDuty, Opsgenie) for on-call rotation
  • Setting up dashboards for pipeline health, backlog volume, and resource utilization
  • Conducting blameless postmortems for major data incidents with action item tracking
  • Simulating failure scenarios (e.g., source outage, schema change) in staging environments
  • Documenting runbooks for common failure modes and recovery procedures

Module 7: Access Control and Data Governance

  • Implementing row-level and column-level security in query engines (e.g., Presto, Spark SQL)
  • Mapping business roles to data access policies using attribute-based access control (ABAC)
  • Integrating with enterprise identity providers (e.g., Okta, Azure AD) for SSO and provisioning
  • Automating access revocation upon employee offboarding or role change
  • Auditing data access patterns to detect anomalous or unauthorized queries
  • Enforcing data classification labels during ingestion and propagating them through transformations
  • Managing consent flags for personal data in multi-region deployments
  • Coordinating data retention schedules with legal and compliance teams

Module 8: Cost Management and Resource Optimization

  • Right-sizing cluster configurations based on historical workload patterns and peak demand
  • Implementing auto-scaling policies for streaming and batch processing frameworks
  • Tracking compute and storage costs by team, project, or business unit using tagging
  • Optimizing file formats and compression to reduce storage footprint and I/O costs
  • Negotiating reserved instance or savings plan commitments for stable workloads
  • Identifying and eliminating orphaned datasets and unused pipelines
  • Using spot instances for fault-tolerant, non-critical processing jobs
  • Conducting regular cost reviews with stakeholders to align spending with business value

Module 9: Change Management and Cross-Team Collaboration

  • Establishing change advisory boards for reviewing high-impact data model modifications
  • Documenting API contracts and deprecation timelines for shared datasets
  • Coordinating schema evolution with consumer teams using versioned endpoints
  • Facilitating data domain alignment sessions to resolve ownership disputes
  • Creating sandbox environments for testing breaking changes without affecting production
  • Standardizing naming conventions and metadata practices across business units
  • Managing technical debt in pipelines through scheduled refactoring windows
  • Integrating data change notifications into team communication platforms (e.g., Slack, Teams)
!