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Process Modelling in Big Data

Process Modelling in Big Data

$296.00
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.
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What does the Process Modelling in Big Data course cover?

Process Modelling in Big Data is covered here in 9 modules: Defining Big Data Process Requirements and Scope, Architecting Scalable Data Ingestion Pipelines, Data Transformation and Workflow Orchestration and 6 more. The outline lists 72 specific topics, opening with selecting data ingestion sources based on SLA commitments, data freshness needs, and downstream system dependencies and closing with coordinating data breach response procedures.

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

The work is sequenced in 9 stages. It starts with Defining Big Data Process Requirements and Scope, moves through Architecting Scalable Data Ingestion Pipelines and Data Transformation and Workflow Orchestration, and ends at Governance, Security, and Compliance in Data Processes. Each stage carries its own topic list, so the sequence is followed rather than summarised.

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

Module 1 is Defining Big Data Process Requirements and Scope. It works through selecting data ingestion sources based on SLA commitments, data freshness needs, and downstream system dependencies, negotiating data ownership and access rights with legal and compliance teams for cross-departmental datasets, determining batch vs. real-time processing requirements based on business use case latency thresholds and 5 more.

How is the Process Modelling in Big Data course delivered?

The Process Modelling 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 Modelling in Big Data course cost?

The Process Modelling in Big Data course is $296 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, Archival processes in Big Data, Digital Processes in Big Data, Process Management 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 for building enterprise-grade data platforms, addressing the same design decisions and trade-offs encountered in large-scale data engineering engagements across ingestion, transformation, storage, and governance.

Module 1: Defining Big Data Process Requirements and Scope

  • Selecting data ingestion sources based on SLA commitments, data freshness needs, and downstream system dependencies
  • Negotiating data ownership and access rights with legal and compliance teams for cross-departmental datasets
  • Determining batch vs. real-time processing requirements based on business use case latency thresholds
  • Mapping data lineage requirements at project initiation to meet future audit and regulatory obligations
  • Establishing data volume thresholds that trigger architectural changes (e.g., from SQL to NoSQL or streaming)
  • Documenting exception handling expectations for missing, malformed, or delayed data inputs
  • Aligning process scope with existing enterprise data governance frameworks and metadata standards
  • Identifying key stakeholders for sign-off on process boundaries and data ownership models

Module 2: Architecting Scalable Data Ingestion Pipelines

  • Choosing between pull-based and push-based ingestion models based on source system capabilities and network constraints
  • Implementing retry logic and backpressure mechanisms in Kafka consumers to handle downstream outages
  • Designing schema evolution strategies for Avro or Protobuf in message queues to support backward compatibility
  • Configuring secure authentication (e.g., OAuth, mTLS) between ingestion tools and source systems
  • Partitioning strategies for Kafka topics based on throughput, ordering requirements, and consumer concurrency
  • Implementing dead-letter queues for failed records with monitoring and alerting on queue depth
  • Estimating and provisioning network bandwidth for high-volume ingestion from IoT or log sources
  • Validating data integrity at ingestion using checksums or hash comparisons

Module 3: Data Transformation and Workflow Orchestration

  • Selecting orchestration tools (e.g., Airflow, Prefect, Dagster) based on scheduling complexity and observability needs
  • Defining idempotent transformation logic to support safe pipeline retries without data duplication
  • Implementing incremental data processing using watermarking and change data capture (CDC) techniques
  • Managing dependency chains across heterogeneous environments (Spark, Python, SQL) in a single DAG
  • Configuring retry policies and timeout thresholds for long-running transformation jobs
  • Version-controlling ETL code and configuration using Git with branching strategies for testing and production
  • Embedding data quality checks (e.g., null rate, value distribution) within transformation workflows
  • Optimizing shuffle operations in Spark by tuning partition counts and broadcast joins

Module 4: Storage Architecture for Structured and Unstructured Data

  • Selecting file formats (Parquet, ORC, JSON) based on query patterns, compression needs, and schema evolution
  • Designing partitioning and bucketing strategies in data lakes to reduce scan costs and improve query performance
  • Implementing lifecycle policies for object storage (e.g., S3 Glacier transitions) to manage cost and compliance
  • Choosing between data lake and data warehouse based on query concurrency, ACID requirements, and user access patterns
  • Configuring access controls at the object and column level using IAM roles and Apache Ranger policies
  • Planning for metadata management using centralized catalogs (e.g., AWS Glue, Unity Catalog)
  • Designing schema registry integration for enforcing consistency across streaming and batch pipelines
  • Replicating data across regions for disaster recovery while managing egress costs and latency

Module 5: Real-Time Stream Processing Design

  • Selecting stream processing engines (Flink, Spark Streaming, Kafka Streams) based on exactly-once semantics needs
  • Designing windowing strategies (tumbling, sliding, session) to match business event aggregation logic
  • Managing state storage size and checkpointing frequency to balance recovery time and performance
  • Handling out-of-order events using watermarks and late-arrival buffers in time-based aggregations
  • Integrating stream joins with dimension data stored in external databases or state stores
  • Scaling consumer groups dynamically based on lag metrics and throughput requirements
  • Implementing end-to-end latency monitoring with distributed tracing across microservices
  • Securing stream data in transit and at rest using encryption and access policies

Module 6: Data Quality and Validation Engineering

  • Embedding data validation rules (e.g., uniqueness, referential integrity) at ingestion and transformation stages
  • Configuring automated alerting on data quality rule violations using tools like Great Expectations or Deequ
  • Establishing data reconciliation processes between source and target systems for critical pipelines
  • Defining acceptable data drift thresholds for statistical profiles and triggering retraining workflows
  • Implementing data profiling jobs to detect schema changes or unexpected value distributions
  • Managing false positive rates in data quality alerts to maintain operational trust
  • Documenting data quality SLAs and escalation paths for unresolved issues
  • Versioning data validation rules to support auditability and rollback capabilities

Module 7: Metadata Management and Data Lineage

  • Integrating automated lineage capture tools (e.g., Marquez, DataHub) with orchestration and ETL platforms
  • Mapping technical lineage (table-to-table) and business lineage (KPI-to-source) for regulatory reporting
  • Standardizing metadata tagging for data domains, sensitivity levels, and stewardship ownership
  • Implementing search and discovery features over metadata to reduce time-to-insight for analysts
  • Handling lineage gaps in legacy systems that lack instrumentation or logging capabilities
  • Synchronizing metadata across environments (dev, staging, prod) using CI/CD pipelines
  • Defining retention policies for operational metadata (e.g., job execution logs, query history)
  • Exposing lineage data to data governance teams via API or reporting dashboards

Module 8: Performance Tuning and Cost Optimization

  • Right-sizing cluster resources (CPU, memory, disk) based on historical job profiling and peak loads
  • Implementing autoscaling policies for cloud data platforms (e.g., Databricks, BigQuery) with cost caps
  • Optimizing query performance through predicate pushdown, column pruning, and indexing strategies
  • Reducing data transfer costs by co-locating compute and storage in the same region
  • Monitoring and controlling scan-to-result ratios in analytical queries to prevent wasteful processing
  • Using materialized views or pre-aggregated tables to accelerate frequent reporting queries
  • Identifying and eliminating orphaned data pipelines that consume resources but serve no active use case
  • Conducting regular cost attribution reviews to allocate spending by team, project, or business unit

Module 9: Governance, Security, and Compliance in Data Processes

  • Implementing role-based access control (RBAC) and attribute-based access control (ABAC) for data assets
  • Masking or tokenizing PII fields in non-production environments using dynamic data masking rules
  • Configuring audit logging for data access and modification events across storage and compute layers
  • Enforcing data retention and deletion policies in alignment with GDPR, CCPA, or industry mandates
  • Conducting data protection impact assessments (DPIAs) for high-risk processing activities
  • Integrating data classification tools to automatically tag sensitive data at rest and in motion
  • Managing encryption key rotation and access for data-at-rest using cloud KMS or on-prem HSMs
  • Coordinating data breach response procedures with incident management teams and legal counsel
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