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Examples

This guide provides practical Aniwa usage examples for common profiling workflows.

These examples demonstrate:

  • dataset inspection
  • report generation
  • configuration usage
  • automation workflows
  • profiling strategies

Basic Dataset Profiling

Profile a CSV dataset:

aniwa examples/customers.csv

Profile an Excel dataset:

aniwa examples/customers.xlsx

Profile a JSON dataset:

aniwa examples/customers.json

Profile a Parquet dataset:

aniwa examples/customers.parquet

Fast Profiling

Use lightweight profiling for speed:

aniwa examples/customers.csv --mode fast

Best for:

  • quick inspections
  • CI validation
  • very large datasets

Deep Profiling

Run comprehensive profiling:

aniwa examples/customers.csv --mode deep

Best for:

  • audits
  • production validation
  • deep analysis

JSON Reports

Generate machine-readable reports:

aniwa examples/customers.csv \
  --report json \
  --output reports/profile.json

Use cases:

  • automation
  • APIs
  • pipelines
  • integrations

HTML Reports

Generate shareable HTML reports:

aniwa examples/customers.csv \
  --report html \
  --output reports/profile.html

Best for:

  • stakeholders
  • sharing
  • auditing
  • visualization

PDF Reports

Generate printable reports:

aniwa examples/customers.csv \
  --report pdf \
  --output reports/profile.pdf

Best for:

  • compliance
  • documentation
  • management reviews

Markdown Reports

Generate GitHub-friendly reports:

aniwa examples/customers.csv \
  --report markdown \
  --output reports/profile.md

Best for:

  • documentation
  • GitHub issues
  • pull requests

Excel Reports

Generate spreadsheet reports:

aniwa examples/customers.csv \
  --report excel \
  --output reports/profile.xlsx

Best for:

  • analysts
  • spreadsheet workflows
  • exports

Using Templates

Dark Template

aniwa examples/customers.csv \
  --report html \
  --template dark

Enterprise Template

aniwa examples/customers.csv \
  --report pdf \
  --template enterprise

Compact Template

aniwa examples/customers.csv \
  --report html \
  --template compact

Output Directories

Generate reports into folders:

aniwa examples/customers.csv \
  --report html \
  --output-dir reports/

Generated output:

reports/aniwa_report.html

Include Specific Sections

Generate focused reports:

aniwa examples/customers.csv \
  --include summary,insights

Exclude Sections

Remove unnecessary sections:

aniwa examples/customers.csv \
  --exclude statistics

Configuration File Examples

YAML Workflow

aniwa.yaml

mode: deep

report:
  format: html
  template: enterprise
  output_dir: reports/

sections:
  include:
    - summary
    - statistics
    - insights

Run:

aniwa examples/customers.csv

Custom Config File

aniwa examples/customers.csv \
  --config examples/config_sample.yaml

Automation Examples

Nightly Profiling

aniwa production/customers.csv \
  --report html \
  --output-dir nightly_reports/

CI Validation

aniwa production/customers.csv \
  --mode fast \
  --report json

Audit Workflow

aniwa production/customers.csv \
  --mode deep \
  --report pdf \
  --template enterprise

Example Profiling Insights

Aniwa can generate insights like:

1 duplicate rows detected.

Column 'email' may contain sensitive information.

Column 'customer_id' may represent an identifier.

Column 'revenue' has high cardinality.

Example Enterprise Workflow

Step 1 — Initial Inspection

aniwa customers.csv

Step 2 — Generate HTML Report

aniwa customers.csv \
  --report html

Step 3 — Generate PDF Audit

aniwa customers.csv \
  --report pdf \
  --template enterprise

Step 4 — Standardize with Config

aniwa.yaml

mode: deep

report:
  format: pdf
  template: enterprise
  output_dir: audits/

Step 5 — Automate

aniwa customers.csv

Example Team Structure

project/
├── datasets/
├── reports/
├── configs/
├── audits/
├── aniwa.yaml

Example Data Engineering Workflow

Aniwa fits naturally into:

ingestion
→ validation
→ profiling
→ quality checks
→ transformation
→ analytics

Example Governance Workflow

Aniwa can support:

  • dataset audits
  • trust reviews
  • onboarding datasets
  • quality validation
  • metadata inspection

Future Workflow Examples

Future versions of Aniwa may support:

  • database profiling
  • distributed profiling
  • observability integration
  • AI-assisted insights
  • dataset lineage
  • semantic understanding
  • trust scoring

Recommended Practice

A recommended workflow:

  1. inspect datasets immediately
  2. generate reusable reports
  3. standardize configs
  4. automate validation
  5. integrate into CI/CD

Next Steps

Continue with:

  • cli-reference.md
  • profiling-modes.md
  • report-formats.md
  • architecture/overview.md