Taxonomy Distribution Report

Overview

• Total Concepts: 100
• Number of Taxonomies: 9
• Average Concepts per Taxonomy: 11.1

Distribution Summary

Category	TaxonomyID	Count	Percentage	Status
Settings & Configuration	SETT	18	18.0%	✅
Ibis Backend	IBIS	16	16.0%	✅
Iceberg Backend	ICE	14	14.0%	✅
Foundation Concepts	FOUND	10	10.0%	✅
Dialect System	DIAL	10	10.0%	✅
Adapters & Auth	ADAPT	10	10.0%	✅
Backend Protocol	PROTO	8	8.0%	✅
Inspection Model	INSP	8	8.0%	✅
Advanced Features	ADV	6	6.0%	✅

Visual Distribution

Settings & Configuration █████████  18 ( 18.0%)
Ibis Backend             ████████  16 ( 16.0%)
Iceberg Backend          ███████  14 ( 14.0%)
Foundation Concepts      █████  10 ( 10.0%)
Dialect System           █████  10 ( 10.0%)
Adapters & Auth          █████  10 ( 10.0%)
Backend Protocol         ████   8 (  8.0%)
Inspection Model         ████   8 (  8.0%)
Advanced Features        ███   6 (  6.0%)

Balance Analysis

✅ No Over-Represented Categories

All categories are under the 30% threshold. Good balance!

Category Details

Settings & Configuration (SETT)

Count: 18 concepts (18.0%)

Concepts:

• 1. ConnectionProfile Base
• 1. To Driver Kwargs Method
• 1. BackendSpec Class
• 1. ParameterSpec Class
• 1. Parameter Tiers
• 1. DATABASES REGISTRY
• 1. Register Decorator
• 1. Auto Registration
• 1. SQLiteAuthSettings
• 1. DuckDBAuthSettings
• 1. PostgreSQLAuthSettings
• 1. BigQueryAuthSettings
• 1. SnowflakeAuthSettings
• 1. RedshiftAuthSettings
• 1. ClickhouseAuthSettings
• ...and 3 more

Ibis Backend (IBIS)

Count: 16 concepts (16.0%)

Concepts:

• 1. IbisBackend Class
• 1. Ibis Connection
• 1. Fluent Query API
• 1. Raw SQL Queries
• 1. DDL Operations
• 1. Create Table
• 1. DML Operations
• 1. Insert Data
• 1. Upsert Data
• 1. Truncate Table
• 1. Create View
• 1. List Tables Ibis
• 1. Table Inspection Ibis
• 1. Namespace Inspection Ibis
• 1. Catalog Inspection Ibis
• ...and 1 more

Iceberg Backend (ICE)

Count: 14 concepts (14.0%)

Concepts:

• 1. IcebergBackend Class
• 1. Apache Iceberg Overview
• 1. Iceberg Connection Base
• 1. REST Catalog Type
• 1. Hive Catalog Type
• 1. Glue Catalog Type
• 1. SQL Catalog Type
• 1. Catalog Type Registry
• 1. Iceberg Operations
• 1. List Tables Iceberg
• 1. Table Inspection Iceberg
• 1. Namespace Inspection Iceberg
• 1. Catalog Inspection Iceberg
• 1. PyIceberg Library

Foundation Concepts (FOUND)

Count: 10 concepts (10.0%)

Concepts:

• 1. SQL Databases
• 1. Database Schemas
• 1. Database Catalogs
• 1. Connection Management
• 1. Python Protocols
• 1. Runtime Checkable Protocol
• 1. Pydantic Models
• 1. Decorators
• 1. Registry Pattern
• 1. Ibis Library

Dialect System (DIAL)

Count: 10 concepts (10.0%)

Concepts:

• 1. DialectSpec Overview
• 1. Dialect Registry
• 1. Dialect Name Key
• 1. Connection Builder
• 1. Operation Hooks
• 1. Per Dialect Configuration
• 1. SQLite Dialect
• 1. DuckDB Dialect
• 1. PostgreSQL Dialect
• 1. Snowflake Dialect

Adapters & Auth (ADAPT)

Count: 10 concepts (10.0%)

Concepts:

• 1. Adapter Pipeline
• 1. Credential Transformation
• 1. OAuth Adapter
• 1. JWT Adapter
• 1. Cloud Native Auth
• 1. SSL Bundle Adapter
• 1. NoAuth Settings
• 1. PasswordAuth Settings
• 1. TokenAuth Settings
• 1. IAMAuth Settings

Backend Protocol (PROTO)

Count: 8 concepts (8.0%)

Concepts:

• 1. Backend Protocol Definition
• 1. Connect Method
• 1. Close Method
• 1. List Tables Method
• 1. Inspect Table Method
• 1. List Namespaces Method
• 1. Inspect Namespace Method
• 1. Inspect Catalog Method

Inspection Model (INSP)

Count: 8 concepts (8.0%)

Concepts:

• 1. CatalogInfo Dataclass
• 1. NamespaceInfo Dataclass
• 1. TableInfo Dataclass
• 1. ColumnInfo Dataclass
• 1. Frozen Metadata Model
• 1. Backend Agnostic Metadata
• 1. Driver Metadata Conversion
• 1. Unified Inspection API

Advanced Features (ADV)

Count: 6 concepts (6.0%)

Concepts:

• 1. Cross Backend Queries
• 1. Backend Capability Matrix
• 1. DDL Index Support
• 1. Memory Intensive Upsert
• 1. REST Catalog Cursors
• 1. Unimplemented Operations

Recommendations

• ✅ Excellent balance: Categories are evenly distributed (spread: 12.0%)
• ✅ MISC category minimal: Good categorization specificity

Educational Use Recommendations

• Use taxonomy categories for color-coding in graph visualizations
• Design curriculum modules based on taxonomy groupings
• Create filtered views for focused learning paths
• Use categories for assessment organization
• Enable navigation by topic area in interactive tools

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Report generated by learning-graph-reports/taxonomy_distribution.py