Installation

Python SDK

The Python SDK is the primary interface for Kanoniv. It provides schema authoring, validation, planning, reconciliation, and diff - all running locally via the embedded Rust engine. No server, no API keys, no account required.

pip install kanoniv

from kanoniv import Spec, Source, validate, plan, diff, reconcile

spec = Spec.from_file("customer-spec.yaml")
validate(spec).raise_on_error()

sources = [
    Source.from_csv("crm", "crm.csv"),
    Source.from_csv("billing", "billing.csv"),
]
result = reconcile(sources, spec)
print(f"Golden records: {len(result.golden_records)}")

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System Requirements

Requirement	Version
Python	3.9+
OS	Linux, macOS, Windows

The core SDK has zero external dependencies beyond the bundled Rust engine (distributed as a prebuilt wheel).

Optional Dependencies

Some Source adapters use common data libraries. If you have them installed, they just work:

• Source.from_pandas() / result.to_pandas() - needs pandas
• Source.from_warehouse() / Source.from_dbt() - needs sqlalchemy + your database dialect (Databricks: databricks-sql-connector includes the dialect)

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Supported Adapters

The adapter field in your identity schema YAML declares the source type. The Python SDK loads the actual data using Source.from_csv(), Source.from_pandas(), Source.from_warehouse(), or Source.from_dbt().

Local Adapters

Adapter	Spec Value	SDK Method	Use Case
CSV	adapter: csv	Source.from_csv()	Local files, prototyping
JSON	adapter: json	Source.from_json()	JSON array files
pandas	adapter: pandas	Source.from_pandas()	In-memory DataFrames

Warehouse Adapters

All warehouse sources use Source.from_warehouse(), which connects via SQLAlchemy. Install the appropriate dialect for your database.

Adapter	Spec Value	Connection String	Dialect Package
PostgreSQL	adapter: postgres	postgresql://user:pass@host/db	psycopg2
Snowflake	adapter: snowflake	snowflake://user:pass@account/db	snowflake-sqlalchemy
BigQuery	adapter: bigquery	bigquery://project-id/dataset	sqlalchemy-bigquery
Redshift	adapter: redshift	redshift://user:pass@cluster/db	sqlalchemy-redshift
Databricks	adapter: databricks	databricks://token:dapi...@host?http_path=...	databricks-sql-connector

dbt Adapter

Adapter	Spec Value	SDK Method	Use Case
dbt	adapter: dbt	Source.from_dbt()	dbt model outputs

Example: Warehouse Source

sources:
  customers:
    adapter: snowflake
    location: ANALYTICS.CORE.CUSTOMERS
    primary_key: customer_id
    schema:
      email: { type: string, pii: true }
      phone: { type: string, pii: true }
      first_name: { type: string }
      last_name: { type: string }

from kanoniv import Source

source = Source.from_warehouse(
    "customers", "ANALYTICS.CORE.CUSTOMERS",
    connection_string="snowflake://user:pass@account/ANALYTICS",
    primary_key="customer_id",
)

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Next Steps

• Local Quickstart: Write your first schema and reconcile data
• Cloud Quickstart: Auto-discover and reconcile from the CLI
• Identity Schema Configuration: Full schema configuration options
• Source Adapters: Detailed adapter documentation

Need real-time resolution, governance, or a persistent identity graph?

Kanoniv Cloud adds sub-millisecond resolution API, audit logs, overrides, PII masking, SSO, and more. Same SDK, same schema.