CloudSketch AI Logo
FeaturesTemplatesPricingEnterpriseAboutContactLog in
πŸŒ™β˜€οΈ
Log inStart free
Home/Templates/Data Engineering & Analytics

πŸ“Š Data Engineering & Analytics Β· Mindmap

Data Governance Areas

The main parts of a data governance programme, from ownership and quality to privacy and access, for teams setting up one.

More Data Engineering & Analytics templates

Drawing diagram…

What this diagram shows

  • Covers people, process and tools
  • Privacy and access sit next to quality, not after it
  • A useful checklist for a first governance charter

Prompt used

Mind map of data governance: ownership (data owners, stewards, RACI), quality (rules, monitoring, issue process), catalog and lineage, privacy (PII classification, consent, retention), access (role-based access, approvals, audits) and standards (naming, definitions, metric glossary).

Mermaid code
mindmap
  root((Data Governance))
    Ownership
      Data owners
      Data stewards
      RACI per domain
    Quality
      Rules and thresholds
      Monitoring
      Issue process
    Catalog
      Table descriptions
      Lineage
      Search
    Privacy
      PII classification
      Consent
      Retention periods
    Access
      Role-based access
      Approval workflow
      Access audits
    Standards
      Naming conventions
      Metric glossary

Related templates

C4 ArchitectureProData Engineering & Analytics

Modern Data Platform Architecture

A typical modern data stack: data is loaded from apps and SaaS tools into a cloud warehouse, modelled with dbt, orchestrated with Airflow and served to BI dashboards.

Data FlowData Engineering & Analytics

Change Data Capture Pipeline

How every insert, update and delete in an operational database is streamed to the data lake and search index in near real time using change data capture.

Database ERDData Engineering & Analytics

Sales Star Schema

A classic star schema for sales analytics: one fact table of order lines surrounded by date, customer, product, store and promotion dimensions.

FlowchartData Engineering & Analytics

Nightly Batch ETL Workflow

The steps of a nightly batch pipeline with data quality gates: extract, validate, transform, load and publish, stopping safely when checks fail.

DeploymentProData Engineering & Analytics

Real-time Streaming Analytics Deployment

Where a real-time analytics stack runs: Kafka for events, Flink for stream processing, a real-time OLAP database and live dashboards.

State MachineData Engineering & Analytics

Data Pipeline Run States

The states of a single pipeline run in an orchestrator like Airflow, including retries, upstream failures and manual reruns.