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Databricks to Snowflake integration

Connect Databricks to Snowflake and automatically sync your cloud data warehouse data. Weld extracts data from Databricks, loads it into Snowflake, and lets you transform the data using SQL or dbt, no custom scripts required.

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4.8 on G2
DatabricksSource
WeldWeld
SnowflakeWarehouse
Weld · New sync
DatabricksSourceDatabricksConnected
Tables to sync

Your own tables, picked when you set up the sync.

  • Extract
  • Load
  • Keep in sync
SnowflakeDestinationSnowflakeReady
databricks schema
  • databricks.Synced
  • databricks.Syncing
  • databricks.Synced
  • databricks.Synced

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How it works

How to set up Databricks to Snowflake in Weld.
In the app, with the API or an agent.

API guideMCP docs
Weld · Data warehouse
SnowflakeData warehouseSnowflakeConnected
Data locationChosen when you connect
CredentialsService account or key, stored encrypted
Use cases

Why sync Databricks to Snowflake?
Ready for analysis.

Centralizing your Databricks data in Snowflake lets you join it with other data sources for cross-platform analytics and deeper insights.

Build reliable dashboards and reports by querying Databricks data directly in Snowflake using SQL or your preferred BI tool.

  • Centralized data warehouse

    Bring Databricks data into Snowflake alongside other sources to create a single source of truth for your organization.

  • Automated reporting

    Replace manual exports and spreadsheet-based reporting with automated pipelines from Databricks to Snowflake. Connect BI tools like Looker, Metabase, or Power BI.

Data

What data can you sync?
From Databricks to Snowflake.

Weld syncs the objects the Databricks connector exposes into Snowflake, and you choose which tables and fields to include. The connector's docs list what is available.

    Weld MCP server

    Ask your agent.
    It queries Snowflake for you.

    Connect Claude, Cursor, ChatGPT or your own agent to Weld over MCP. It reads the Databricks context, finds the tables it needs and runs the query in Snowflake.

    Set up the MCP serverClaudeCursorChatGPTGemini
    Claude CodeOpus 5
    >

    Which Databricks tables do we have in Snowflake, and which is the largest?

    Looking up what Weld has synced from Databricks.

    ●weld·search_available_references
    ⎿raw.databricks.*
    tables, columns and freshness
    ●weld·get_reference_schema
    ⎿columns, types and nullability
    ●weld·run_query
    ⎿select count(*) from {{raw.databricks.…}}
    submitted
    ●weld·get_query_status
    ⎿DONE
    ●weld·get_query_result
    ⎿rows returned
    ✓Answered from Databricks data in Snowflake
    >Ask about your Databricks data
    Under the hood

    Technical considerations.
    What to expect.

    Incremental syncing
    Weld uses incremental syncing where supported by the Databricks API, loading only new and updated records to minimize data transfer and warehouse costs.
    Schema handling
    Weld automatically manages schema changes. If Databricks adds new fields, they're detected and added to the corresponding Snowflake tables.
    Data freshness
    Sync frequency depends on your Weld plan. Most connectors support schedules from every 5 minutes up to daily intervals.
    Security
    All data is encrypted in transit (TLS) and at rest. Weld is SOC 2 Type II compliant and supports IP whitelisting for database connections.
    Resources

    Keep reading.
    Guides, docs and schema.

    Documentation

    Snowflake setup guide

    How to connect Snowflake to Weld, step by step.

    Connector

    Databricks connector

    Everything Weld does with Databricks.

    Connector

    Snowflake connector

    Everything Weld does with Snowflake.

    FAQ

    Common questions.
    About Databricks to Snowflake.

    Start syncing Databricks to Snowflake.

    Set up your Databricks data pipeline in minutes. No credit card required. Free for 14 days.

    DatabricksSource
    WeldWeld
    SnowflakeWarehouse
    Start for freeTalk to us
    Related

    Related integrations.
    More with Databricks and Snowflake.

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