Aniwa v0.1.1

MVP Foundation Release

Release Date: TBD

---

Overview

v0.1.1 represents the first public MVP foundation release of Aniwa.

This release establishes the core profiling infrastructure, reporting systems, and developer-first workflows that define the foundation of the Aniwa ecosystem.

Aniwa was created to help developers, analysts, researchers, and modern data teams:

understand datasets quickly, intelligently, and beautifully

before trusting or using them in production workflows.

---

Why Aniwa Exists

Data professionals constantly work with:

unknown datasets

Before trusting a dataset, teams need answers to questions like:

• What columns exist?
• What data types are present?
• Are there missing values?
• Are there duplicates?
• Are there suspicious patterns?
• Which columns might contain IDs or PII?
• Is the dataset healthy?

Aniwa exists to make answering those questions:

fast, simple, and developer-friendly

---

Release Goals

The goals of this release were to establish:

• foundational profiling infrastructure
• universal dataset support
• intelligent profiling workflows
• report generation systems
• scalable architecture
• contributor-ready project structure

---

Core Philosophy

This release was built around several core principles:

Principle
universal
developer-first
fast
modular
intelligent
beautiful
automation-friendly

---

Major Features

---

Universal Dataset Support

Aniwa v0.1.1 introduces support for multiple modern dataset formats.

---

Supported Formats

Current supported formats include:

Format
CSV
Excel (.xlsx)
JSON
Parquet

---

Why Universal Support Matters

Modern data workflows involve datasets from many sources and formats.

Aniwa aims to provide:

one unified profiling workflow across datasets

---

Core Profiling Engine

v0.1.1 introduces the first version of the Aniwa profiling engine.

---

Current Profiling Capabilities

The profiling engine currently supports:

Capability
schema profiling
data quality analysis
statistical profiling
intelligent insights

---

Dataset Summary

Aniwa currently provides:

Feature
row counts
column counts
dataset size analysis

---

Schema Profiling

Schema profiling currently includes:

Feature
type inference
schema overview
mixed type detection

---

Data Quality Analysis

Quality analysis currently supports:

Feature
null analysis
duplicate detection
uniqueness analysis
sparse column detection

---

Statistical Profiling

Statistical profiling currently includes:

Metric
minimum
maximum
mean
median
standard deviation

---

Intelligent Insights

The first insight system currently supports:

Insight
possible ID detection
high-cardinality warnings
sparse column warnings
suspicious quality patterns

---

Profiling Modes

v0.1.1 introduces multiple profiling modes.

---

Current Profiling Modes

Mode
fast
deep

---

Fast Mode

Fast mode prioritizes:

lightweight rapid profiling

for quick inspection workflows.

---

Deep Mode

Deep mode prioritizes:

full dataset understanding

through more detailed analysis.

---

Why Profiling Modes Matter

Different workflows require different tradeoffs between:

• speed
• depth
• resource usage

---

Reporting System

v0.1.1 introduces the first version of the modular reporting system.

---

Current Report Formats

Supported report formats include:

Format
console
JSON
HTML

---

Rich Terminal Reports

Aniwa uses:

Rich-powered terminal interfaces

to provide beautiful developer-friendly profiling output.

---

Why Console Reports Matter

Terminal reports improve:

• readability
• developer experience
• workflow speed

---

JSON Reports

JSON reports provide:

• machine-readable outputs
• automation compatibility
• integration-friendly workflows

---

HTML Reports

HTML reports provide:

• shareable outputs
• cleaner presentation
• team-friendly reporting

---

Why HTML Reports Matter

HTML reports improve:

• collaboration
• audits
• debugging workflows
• dataset sharing

---

Architecture Foundation

v0.1.1 establishes the foundational layered architecture of Aniwa.

---

Current Architecture

CLI
→ Readers
→ Profiling Engine
→ Models
→ Reports

---

Why the Architecture Matters

The architecture prioritizes:

• modularity
• maintainability
• scalability
• contributor friendliness

---

CLI Foundation

The CLI currently supports:

• dataset profiling
• profiling modes
• report generation
• output handling

---

Example Commands

Basic profiling:

aniwa customers.csv

Generate JSON report:

aniwa customers.csv --report json --output profile.json

Generate HTML report:

aniwa customers.csv --report html --output profile.html

Fast profiling:

aniwa customers.csv --mode fast

Deep profiling:

aniwa customers.csv --mode deep

---

Project Structure

v0.1.1 establishes the initial Aniwa project structure.

---

Current Structure

Aniwa/
│
├── aniwa/
│   ├── cli.py
│   ├── core/
│   ├── io/
│   ├── models/
│   ├── reports/
│   └── utils/
│
├── tests/
├── examples/
├── docs/
│
├── README.md
├── CONTRIBUTING.md
├── requirements.txt
└── pyproject.toml

---

Why the Structure Matters

This structure improves:

• contributor onboarding
• maintainability
• modularity
• ecosystem scalability

---

Developer Experience

v0.1.1 strongly prioritizes:

developer-first workflows

---

Current DX Features

Current developer experience features include:

Feature
Rich terminal UI
fast profiling mode
deep profiling mode
modular architecture
readable reports

---

Documentation Foundation

This release establishes the first version of the Aniwa documentation ecosystem.

---

Documentation Areas

Current documentation areas include:

Area
getting started
installation
usage
architecture
roadmap
philosophy

---

Why Documentation Matters

Documentation is treated as:

core project infrastructure

---

Internal Improvements

Additional internal improvements include:

• reusable profiling systems
• modular report rendering
• structured execution flow
• reusable models
• cleaner CLI orchestration

---

Testing Foundation

Initial testing systems currently include:

Area
reader tests
CLI tests
report tests

---

Why Testing Matters

Testing improves:

• reliability
• contributor confidence
• release stability

---

Current Limitations

v0.1.1 remains an early foundational release.

Several major systems are still planned for future releases.

---

Future Areas

Future releases are expected to expand:

Area
Markdown reports
Excel reports
PDF reports
charts
templates
database support
governance systems
plugin systems
AI-assisted profiling

---

Long-Term Vision

Aniwa is designed to evolve from:

a profiling CLI

into:

universal dataset intelligence infrastructure

---

Strategic Importance

v0.1.1 establishes:

the architectural and philosophical foundation of the Aniwa ecosystem

---

Acknowledgements

Thank you to all contributors, testers, reviewers, and early adopters helping shape the early Aniwa ecosystem.

---

Related Documentation

Continue with:

• philosophy.md
• roadmap.md
• developer-guide/architecture.md