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Core Competencies in Big Data

Core Competencies in Big Data

$302.00
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Self-paced • Lifetime updates
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What does the Core Competencies in Big Data course cover?

Core Competencies in Big Data is covered here in 9 modules: Data Architecture Design at Scale, Distributed Data Processing Frameworks, Data Ingestion and Pipeline Orchestration 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 replay complexity.

How do you approach Core Competencies in Big Data step by step?

The work is sequenced in 9 stages. It starts with Data Architecture Design at Scale, moves through Distributed Data Processing Frameworks and Data Ingestion and Pipeline Orchestration, and ends at Cloud-Native Data Platform Operations. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Core Competencies in Big Data course?

Module 1 is Data Architecture Design at Scale. It works through selecting between lambda and kappa architectures based on real-time SLA requirements and data replay complexity., designing partitioning strategies in distributed file systems to balance query performance and storage overhead., implementing schema evolution in Parquet and Avro formats while maintaining backward compatibility across pipelines. and 5 more.

How is the Core Competencies in Big Data course delivered?

The Core Competencies 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 Core Competencies in Big Data course cost?

The Core Competencies 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: Core Competencies Toolkit, Core Competencies in DevOps, Core Competencies in Sales Kit, Core Competencies in Business Strategy Alignment.

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 program, addressing the same data architecture, pipeline orchestration, and governance challenges encountered in large-scale cloud data platform rollouts and internal engineering enablement initiatives.

Module 1: Data Architecture Design at Scale

  • Selecting between lambda and kappa architectures based on real-time SLA requirements and data replay complexity.
  • Designing partitioning strategies in distributed file systems to balance query performance and storage overhead.
  • Implementing schema evolution in Parquet and Avro formats while maintaining backward compatibility across pipelines.
  • Choosing appropriate data serialization formats based on compression efficiency, query engine compatibility, and ingestion throughput.
  • Configuring data lake zone separation (raw, curated, trusted) with automated validation and quarantine workflows.
  • Integrating metadata catalogs with data lineage tracking to support auditability and impact analysis.
  • Designing immutable data storage patterns to support point-in-time recovery and reproducible analytics.
  • Managing cross-region replication strategies for compliance, disaster recovery, and low-latency access.

Module 2: Distributed Data Processing Frameworks

  • Tuning Spark executor memory and core allocation to prevent out-of-memory errors and underutilization.
  • Optimizing shuffle operations by adjusting partition counts and selecting broadcast joins over repartitioning.
  • Implementing checkpointing in streaming jobs to manage state size and ensure fault tolerance.
  • Configuring backpressure mechanisms in Kafka-Spark integrations to handle variable input rates.
  • Choosing between Structured Streaming and Spark Streaming based on API maturity and operational support needs.
  • Managing version skew between processing frameworks and cluster runtime environments.
  • Deploying Flink applications with savepoints for controlled upgrades and rollback capabilities.
  • Monitoring and diagnosing speculative execution issues in heterogeneous cluster environments.

Module 3: Data Ingestion and Pipeline Orchestration

  • Designing idempotent ingestion pipelines to handle duplicate messages from message queues.
  • Selecting batch frequency based on data freshness requirements and downstream processing windows.
  • Implementing dead-letter queues and alerting for failed records in streaming ingestion pipelines.
  • Orchestrating interdependent workflows with Airflow, including sensor patterns and retry backoffs.
  • Securing credential handling in pipeline configurations using secret management systems.
  • Validating data schema at ingestion points to prevent pipeline corruption from malformed inputs.
  • Scaling ingestion workers dynamically based on backlog metrics from Kafka or Kinesis.
  • Versioning pipeline definitions to enable reproducibility and rollback during failures.

Module 4: Data Storage and Query Optimization

  • Choosing columnar vs. row-based storage based on query patterns (aggregations vs. point lookups).
  • Implementing data compaction strategies to reduce small file problems in cloud storage.
  • Designing indexing strategies in NoSQL databases to balance read performance and write amplification.
  • Tuning partition and clustering keys in distributed databases to minimize scan ranges.
  • Managing time-to-live (TTL) policies for compliance and cost control in large-scale datasets.
  • Optimizing file sizes in data lakes to align with compute engine block processing limits.
  • Implementing zone-redundant storage configurations for high availability in cloud environments.
  • Configuring caching layers between query engines and storage to reduce repeated I/O.

Module 5: Data Quality and Observability

  • Defining data quality rules (completeness, consistency, accuracy) per dataset and business context.
  • Implementing automated anomaly detection on data distributions using statistical baselines.
  • Instrumenting pipeline metrics (latency, throughput, error rates) for operational dashboards.
  • Creating alert thresholds that minimize false positives while ensuring timely incident response.
  • Tracking data freshness SLAs and triggering notifications for delayed pipeline runs.
  • Integrating data profiling into CI/CD pipelines for schema and content validation.
  • Using synthetic data injection to test pipeline resilience under data quality degradation.
  • Correlating data quality issues with upstream system changes using deployment logs.

Module 6: Security, Privacy, and Access Governance

  • Implementing attribute-based access control (ABAC) for fine-grained data access in data lakes.
  • Enforcing encryption at rest and in transit across storage and compute services.
  • Masking sensitive fields dynamically based on user roles during query execution.
  • Managing personally identifiable information (PII) through automated discovery and classification.
  • Integrating with enterprise identity providers (e.g., Active Directory, Okta) for centralized authentication.
  • Conducting periodic access certification reviews to enforce least-privilege principles.
  • Configuring audit logging for data access and modification across distributed systems.
  • Applying data retention and deletion workflows in compliance with regulatory requirements.

Module 7: Performance Monitoring and Cost Management

  • Allocating compute resources based on workload criticality and business priority.
  • Right-sizing cluster configurations using historical utilization metrics and peak demand analysis.
  • Implementing auto-scaling policies that respond to queue depth and processing lag.
  • Tracking cost attribution by team, project, or workload using cloud tagging strategies.
  • Optimizing storage tiering (hot, cool, archive) based on access frequency and retrieval cost.
  • Identifying and eliminating orphaned data assets to reduce storage sprawl.
  • Monitoring data transfer costs between availability zones and regions in cloud environments.
  • Using query cost estimators to evaluate the financial impact of analytical workloads.

Module 8: Data Governance and Metadata Management

  • Establishing data ownership and stewardship roles for critical datasets.
  • Implementing business glossary integration with technical metadata for semantic consistency.
  • Automating metadata extraction from ETL jobs and query logs for lineage accuracy.
  • Enforcing data classification policies during dataset registration in the catalog.
  • Managing dataset lifecycle states (draft, published, deprecated, retired) with approval workflows.
  • Integrating data quality metrics into the metadata catalog for discoverability.
  • Resolving naming conflicts and synonymy across departments using canonical naming standards.
  • Supporting regulatory reporting by exporting metadata and access logs in auditable formats.

Module 9: Cloud-Native Data Platform Operations

  • Designing multi-account cloud architectures to isolate workloads and manage billing boundaries.
  • Automating infrastructure provisioning using IaC tools (Terraform, CloudFormation) with version control.
  • Managing cross-cloud data transfer compliance and egress costs in hybrid deployments.
  • Implementing blue-green deployment patterns for zero-downtime pipeline updates.
  • Configuring centralized logging and monitoring across distributed data services.
  • Enforcing security baselines through automated policy-as-code checks (e.g., AWS Config, GCP Forseti).
  • Planning capacity and budget forecasts based on historical growth trends and business roadmaps.
  • Managing vendor lock-in risks by abstracting core logic from proprietary cloud services.
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