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CSV via FTP to Databricks integration

Connect CSV via FTP to Databricks and automatically sync your file storage data. Weld extracts data from CSV via FTP, loads it into Databricks, and lets you transform the data using SQL or dbt, no custom scripts required.

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4.8 on G2
CSV via FTPSource
WeldWeld
DatabricksWarehouse
Weld · New sync
CSV via FTPSourceCSV via FTPConnected
Tables to sync

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

  • Extract
  • Load
  • Keep in sync
DatabricksDestinationDatabricksReady
ftp__csv schema
  • ftp__csv.Synced
  • ftp__csv.Syncing
  • ftp__csv.Synced
  • ftp__csv.Synced

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How it works

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

API guideMCP docs
Weld · New CSV via FTP sync
CSV via FTPNew connectionCSV via FTPAuthorised
Connection nameftp__csv
AuthorizeOpens CSV via FTP to grant read access
Use cases

Why sync CSV via FTP to Databricks?
Ready for analysis.

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

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

  • Centralized data warehouse

    Bring CSV via FTP data into Databricks 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 CSV via FTP to Databricks. Connect BI tools like Looker, Metabase, or Power BI.

Data

What data can you sync?
From CSV via FTP to Databricks.

Weld syncs the objects the CSV via FTP connector exposes into Databricks, 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 Databricks for you.

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

    Set up the MCP serverClaudeCursorChatGPTGemini
    Claude CodeOpus 5
    >

    Which CSV via FTP tables do we have in Databricks, and which is the largest?

    Looking up what Weld has synced from CSV via FTP.

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

    Technical considerations.
    What to expect.

    Incremental syncing
    Weld uses incremental syncing where supported by the CSV via FTP API, loading only new and updated records to minimize data transfer and warehouse costs.
    Schema handling
    Weld automatically manages schema changes. If CSV via FTP adds new fields, they're detected and added to the corresponding Databricks 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

    Databricks setup guide

    How to connect Databricks to Weld, step by step.

    Connector

    CSV via FTP connector

    Everything Weld does with CSV via FTP.

    Connector

    Databricks connector

    Everything Weld does with Databricks.

    FAQ

    Common questions.
    About CSV via FTP to Databricks.

    Start syncing CSV via FTP to Databricks.

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

    CSV via FTPSource
    WeldWeld
    DatabricksWarehouse
    Start for freeTalk to us
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