databricks-uniform-sync 1.1.4

A SDK for syncing Databricks using Unity Catalog and Uniform

Databricks to Snowflake Table Mirroring

This repository provides a utility to synchronize (mirror) Iceberg table metadata from Databricks Unity Catalog to Snowflake Horizon.

It automates the creation of:

• Snowflake Catalog Integrations
• External Iceberg Tables

Note: This library uses credential vending to access cloud storage. Snowflake External Volumes are not required.

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Table of Contents

1. Overview
2. Snowflake Setup
3. Databricks Setup
4. How to Use
5. Configuration
6. Parameter Reference
7. Example Usage
8. Limitations

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Overview

This utility automates the following tasks:

• Retrieves Iceberg metadata from Unity Catalog
• Generates Delta-based metadata tables in Databricks
• Creates Catalog Integrations in Snowflake
• Creates External Iceberg Tables in Snowflake

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Snowflake Setup

This utility supports two usage patterns:

• Manual: Generate DDLs for execution in Snowflake
• Automated: Create Snowflake assets directly from Databricks

Required Snowflake permissions:

• Create Catalog Integrations
• Create External Iceberg Tables

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Databricks Setup

Install the library:

pip install databricks_uniform_sync

Initialize the class:

from databricks_uniform_sync import DatabricksToSnowflakeMirror

d2s = DatabricksToSnowflakeMirror(
    spark_session=spark,
    dbx_workspace_url="https://dbcxyz.databricks.cloud.net",
    dbx_workspace_pat="dapi...",
    metadata_catalog="dbx_sf_mirror_catalog",
    metadata_schema="dbx_sf_mirror_schema"
)

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How to Use

1. Create or Refresh Metadata Tables

d2s.create_metadata_tables()
d2s.refresh_metadata_tables(catalog="your_catalog")

These methods are idempotent and safe to rerun.
If metadata tables do not exist, refresh_metadata_tables() will create them.

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2. Add Unity Catalog Discovery Tags

d2s.refresh_uc_metadata_tags()

These tags are used to determine sync eligibility. Do not remove them.

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3. Create Snowflake Catalog Integrations

Dry run (SQL only):

d2s.generate_create_sf_catalog_integrations_sql(
    oauth_client_id="client-id",
    oauth_client_secret="client-secret"
)

Execute directly:

d2s.create_sf_catalog_integrations(
    sf_account_id="xyz-123",
    sf_user="svc_name",
    sf_private_key_file="rsa/rsa_key.p8",
    sf_private_key_file_pwd="your-password",
    oauth_client_id="client-id",
    oauth_client_secret="client-secret"
)

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4. Create Iceberg Tables in Snowflake

Dry run:

d2s.generate_create_sf_iceberg_tables_sql()

Execute directly:

d2s.create_sf_iceberg_tables_sql(
    sf_account_id="xyz-123",
    sf_user="svc_name",
    sf_private_key_file="rsa/rsa_key.p8",
    sf_private_key_file_pwd="your-password"
)

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Configuration

Custom Metadata Table Name

d2s = DatabricksToSnowflakeMirror(
    spark_session,
    dbx_workspace_url,
    dbx_workspace_pat,
    metadata_catalog,
    metadata_schema,
    metadata_table_name="custom_table_name"
)

A corresponding view will also be created with a _vw suffix.

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Custom Refresh Interval

d2s.create_sf_catalog_integrations(
    ...,
    refresh_interval_seconds=120
)

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Disable Auto-Refresh on Iceberg Tables

d2s.create_sf_iceberg_tables_sql(
    ...,
    auto_refresh=False
)

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Parameter Reference

Databricks Parameters

Parameter	Description
spark_session	Active SparkSession in Databricks
dbx_workspace_url	URL of your Databricks workspace
dbx_workspace_pat	Personal Access Token for authentication
metadata_catalog	Unity Catalog catalog to store metadata
metadata_schema	Unity Catalog schema to store metadata
metadata_table_name (optional)	Custom name for metadata table

Snowflake Parameters

Parameter	Description
sf_account_id	Snowflake account identifier
sf_user	Snowflake user/service account
sf_private_key_file	Path to RSA private key
sf_private_key_file_pwd	Password to decrypt RSA key
oauth_client_id	Databricks OAuth client ID
oauth_client_secret	Databricks OAuth client secret
refresh_interval_seconds (optional)	Catalog Integration refresh interval
auto_refresh (optional)	Enable/disable automatic refresh on tables

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Example Usage

Coming soon.
A demo notebook or script will be added to show end-to-end execution.

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Limitations

• Only supports Iceberg tables on S3
• Deleting tables in Unity Catalog does not remove them in Snowflake
• Only supports RSA key pair authentication (Snowflake MFA compliance)

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