Harvest to Databricks integration
Connect Harvest to Databricks and automatically sync your finance & accounting data. Weld extracts data from Harvest, loads it into Databricks, and lets you transform the data using SQL or dbt, no custom scripts required.
Source
WarehouseSourceHarvestConnected- project27 cols
- client9 cols
- user22 cols
- time_entry48 cols
- expense35 cols
- task9 cols
- Extract
- Load
- Keep in sync
DestinationDatabricksReady- harvest.projectSynced
- harvest.clientSyncing
- harvest.userSynced
- harvest.time_entrySynced
- harvest.expenseSynced
- harvest.taskSynced
- Harvest tables
- 35
- fields, typed
- 520
- relationships kept
- 36
- agent context included
- MCP
Trusted by data and AI teams
Why sync Harvest to Databricks?
Ready for analysis.
Centralizing your Harvest 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 Harvest data directly in Databricks using SQL or your preferred BI tool.
Financial reporting
Centralize financial data from Harvest in Databricks for automated financial dashboards and month-end close analysis.
Centralized data warehouse
Bring Harvest 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 Harvest to Databricks. Connect BI tools like Looker, Metabase, or Power BI.
What data can you sync?
Every Harvest table, keys intact.
Weld syncs the objects the Harvest connector exposes into Databricks, and you choose which tables and fields to include. The connector's docs list what is available.
- project
- client
- user
- time_entry
- expense
- task
- user_project_assignment
- user_project_assignment_task_assignments
- project_user_assignment
- project_task_assignment
- uninvoiced_report
- project_budget_report
- time_projects_report
- expense_projects_report
- expense_category
- invoice
- estimate
- invoice_line_items
- contact
- time_team_report
- estimate_line_items
- expense_team_report
- time_clients_report
- time_tasks_report
- expense_categories_report
- expense_clients_report
- company
- invoice_message
- estimate_message
- invoice_payment
- and 5 more
Ask your agent.
It queries Databricks for you.
Connect Claude, Cursor, ChatGPT or your own agent to Weld over MCP. It reads the Harvest context, finds the tables it needs and runs the query in Databricks.
How many project rows do we have per client? Use our Harvest data in Databricks.
Reading the Harvest context first, so the query uses the right tables and keys.
Technical considerations.
What to expect.
- Incremental syncing
- Weld uses incremental syncing where supported by the Harvest API, loading only new and updated records to minimize data transfer and warehouse costs.
- Schema handling
- Weld automatically manages schema changes. If Harvest 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.
Keep reading.
Guides, docs and schema.
Common questions.
About Harvest to Databricks.
Start syncing Harvest to Databricks.
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SourceWarehouse
