Skip to main content
Systems Review in Big Data

Systems Review in Big Data

$300.00
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.
Your guarantee:
30-day money-back guarantee — no questions asked
When you get access:
Course access is prepared after purchase and delivered via email
Who trusts this:
Trusted by professionals in 160+ countries
How you learn:
Self-paced • Lifetime updates
Adding to cart… The item has been added

What does the Systems Review in Big Data course cover?

Systems Review in Big Data is covered here in 9 modules: Data Ingestion Architecture at Scale, Distributed Storage and Data Lake Design, Compute Framework Selection and Optimization and 6 more. The outline lists 72 specific topics, opening with selecting between batch and streaming ingestion based on SLA requirements and source system capabilities and closing with coordinating with legal and privacy teams to.

How do you approach Systems Review in Big Data step by step?

The work is sequenced in 9 stages. It starts with Data Ingestion Architecture at Scale, moves through Distributed Storage and Data Lake Design and Compute Framework Selection and Optimization, and ends at Compliance and Regulatory Alignment. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Systems Review in Big Data course?

Module 1 is Data Ingestion Architecture at Scale. It works through selecting between batch and streaming ingestion based on SLA requirements and source system capabilities, designing idempotent ingestion pipelines to handle duplicate messages from unreliable sources, implementing backpressure mechanisms in Kafka consumers to prevent downstream system overloads and 5 more. It sets the vocabulary the remaining 8 modules build on.

How is the Systems Review in Big Data course delivered?

The Systems Review 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 Systems Review in Big Data course cost?

The Systems Review in Big Data course is $302 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 in Management Systems, Management Systems in Big Data, IT Systems in Big Data, Legacy Systems in Big Data.

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

This curriculum spans the technical and operational rigor of a multi-workshop systems architecture engagement, addressing the same breadth of concerns as an enterprise data platform review conducted across ingestion, storage, compute, governance, and compliance functions.

Module 1: Data Ingestion Architecture at Scale

  • Selecting between batch and streaming ingestion based on SLA requirements and source system capabilities
  • Designing idempotent ingestion pipelines to handle duplicate messages from unreliable sources
  • Implementing backpressure mechanisms in Kafka consumers to prevent downstream system overloads
  • Configuring retry policies with exponential backoff for transient failures in cloud-based APIs
  • Partitioning strategies for high-volume data sources to ensure parallel processing and scalability
  • Validating schema conformance during ingestion using schema registry tools like Confluent or AWS Glue
  • Monitoring data freshness and latency across ingestion stages using distributed tracing
  • Securing data in transit using mutual TLS and enforcing authentication for external data providers

Module 2: Distributed Storage and Data Lake Design

  • Choosing between object storage (S3, ADLS) and distributed file systems (HDFS) based on cost, durability, and access patterns
  • Implementing partitioning and bucketing strategies in Parquet/ORC formats to optimize query performance
  • Designing lifecycle policies to transition data between storage tiers (hot, cool, archive)
  • Enforcing object-level encryption using KMS-managed keys with granular access policies
  • Managing metadata consistency in data lakes using centralized catalog services like AWS Glue or Apache Atlas
  • Handling schema evolution in columnar formats using backward and forward compatibility rules
  • Implementing soft deletes and point-in-time recovery mechanisms for accidental overwrites
  • Assessing replication strategies across regions for disaster recovery and compliance

Module 3: Compute Framework Selection and Optimization

  • Choosing between Spark, Flink, and Beam based on stateful processing needs and fault tolerance requirements
  • Tuning Spark executor memory and core allocation to balance resource utilization and GC overhead
  • Configuring dynamic allocation and speculative execution in YARN or Kubernetes environments
  • Optimizing shuffle operations by adjusting partition counts and enabling shuffle service reuse
  • Implementing cost-aware autoscaling in cloud-based compute clusters using spot instances
  • Profiling job performance using Spark UI and identifying skew in data distribution
  • Integrating custom UDFs with type safety and serialization considerations
  • Managing dependency packaging and version conflicts in distributed runtime environments

Module 4: Data Quality and Observability

  • Defining and measuring data quality dimensions (accuracy, completeness, timeliness) per business domain
  • Embedding data validation rules in pipelines using Deequ or Great Expectations
  • Setting up alerting thresholds for anomaly detection in data volume and schema changes
  • Instrumenting pipelines with structured logging and correlation IDs for traceability
  • Creating synthetic test datasets to validate pipeline behavior under edge conditions
  • Establishing data freshness SLAs and monitoring end-to-end latency across stages
  • Implementing data lineage tracking to support root cause analysis during incidents
  • Conducting regular data profiling to detect silent data corruption or drift

Module 5: Security and Access Governance

  • Implementing row-level and column-level security in query engines like Presto or Snowflake
  • Integrating with enterprise identity providers using SAML or OIDC for centralized access control
  • Managing secrets for database credentials and API keys using HashiCorp Vault or cloud-native secret managers
  • Auditing data access patterns and generating compliance reports for regulatory requirements
  • Applying data masking and tokenization for PII in non-production environments
  • Enforcing least-privilege access through role-based policies in IAM systems
  • Conducting regular access reviews and deprovisioning stale permissions
  • Encrypting data at rest using customer-managed keys with rotation policies

Module 6: Metadata Management and Cataloging

  • Selecting a metadata store based on query performance and integration capabilities with existing tools
  • Automating metadata extraction from ETL jobs and query logs using custom connectors
  • Standardizing business glossary terms and linking them to technical data assets
  • Implementing versioned metadata to track schema and pipeline changes over time
  • Enabling search and discovery features with faceted navigation and relevance ranking
  • Integrating lineage capture from orchestration tools like Airflow or Dagster
  • Handling ownership metadata updates during team reorganizations or role changes
  • Securing metadata access based on user roles and data sensitivity classifications

Module 7: Orchestration and Pipeline Reliability

  • Designing idempotent tasks to ensure safe retry without side effects
  • Implementing dependency resolution with conditional branching and dynamic task generation
  • Setting up alerting for pipeline failures and missed SLAs using PagerDuty or Opsgenie
  • Scheduling workflows with timezone-aware triggers and handling daylight saving transitions
  • Managing configuration drift across development, staging, and production environments
  • Versioning pipeline definitions using Git and enforcing CI/CD practices
  • Handling backfills efficiently without overwhelming downstream systems
  • Monitoring resource consumption and queue depth in distributed schedulers

Module 8: Cost Management and Resource Optimization

  • Allocating compute costs to business units using tagging and resource labeling
  • Right-sizing cluster configurations based on historical utilization metrics
  • Implementing query cost estimation and blocking excessive scans in ad hoc environments
  • Negotiating reserved capacity or savings plans for predictable workloads
  • Optimizing data layout to reduce I/O and query execution costs
  • Setting up budget alerts and automated throttling for cost overruns
  • Comparing TCO across managed vs. self-hosted data platform options
  • Archiving cold data and decommissioning unused datasets to reduce storage spend

Module 9: Compliance and Regulatory Alignment

  • Mapping data processing activities to GDPR, CCPA, or HIPAA requirements
  • Implementing data retention and deletion workflows to honor user right-to-be-forgotten requests
  • Conducting data protection impact assessments (DPIAs) for new data initiatives
  • Documenting data flows and processing purposes for regulatory audits
  • Enabling data residency controls to restrict processing to specific geographic regions
  • Validating third-party data processors for compliance with organizational standards
  • Generating audit logs with immutable storage and tamper-evident controls
  • Coordinating with legal and privacy teams to interpret regulatory changes
!