Freightview connector
Set up the Freightview connector in Kaivo: authentication, configuration, the 6 BigQuery tables it syncs, and answers to common questions.
Written By Lauri Raivio
Last updated 15 days ago
Kaivo is a fully managed data platform that syncs your Freightview data into a Google BigQuery warehouse and keeps it up to date automatically. There is no pipeline to build and no infrastructure to run, so you can spend your time analysing your e-commerce data instead of moving it.
What is the Freightview connector
Sync your Freightview shipping data into BigQuery with Kaivo to analyse shipments, quotes, and freight cost.
Getting started with the Freightview connector
- Sign up for Kaivo and create a workspace.
- Connect your Freightview account.
- Choose which tables to sync.
- Wait for the initial sync to finish.
- Query your data in BigQuery or your favourite AI or BI tool.
Authenticating Freightview
Authenticate with your Client Secret.
Prerequisites
- Your Freightview account must be active and have at least one carrier added — the API returns no data otherwise.
- In Freightview, select the gear icon (top right) → API Integration, open the V2 API window, and copy the Client ID and Client Secret. The same page also offers V1 Account and User API keys; those are a different, older credential and will not work here.
- Enter those values below.
Need help? Reach our team via the chat in the bottom-right corner.
Configuring the Freightview connector
When you set up the connector, you provide:
Tables and columns synced from Freightview
Kaivo syncs 6 tables from Freightview into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.
account_carriers (5 columns)
account_carriers (5 columns)
account_locations (11 columns)
account_locations (11 columns)
account_users (6 columns)
account_users (6 columns)
quotes (20 columns)
quotes (20 columns)
shipments (50 columns)
shipments (50 columns)
Subtable: shipments__items
Subtable: shipments__items__contains
Subtable: shipments__ref_nums
Subtable: shipments__documents
Subtable: shipments__equipment__accessorials
Subtable: shipments__equipment__alternate_types
Subtable: shipments__locations
Subtable: shipments__locations__ref_nums
Subtable: shipments__locations__accessorials
tracking (9 columns)
tracking (9 columns)
How the Freightview sync works
After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where Freightview supports it, each sync pulls only new and changed records so it stays fast; otherwise it refreshes the whole table. Every record keeps its original ID, so you won't get duplicate rows.
Frequently asked questions
How long does the initial sync take for Freightview?
It depends on how much history is in your Freightview account. Most initial syncs finish within minutes, while large accounts can take a few hours. After that, syncs only fetch new and changed records, so they're much faster.
Can I sync only some tables or columns?
Yes. You pick which tables to sync when you set up the connection and can change the selection later. Tables you don't select are never copied to your warehouse.
What happens when Freightview's schema changes?
New fields are never added automatically. You choose which fields to sync, so data you haven't selected (sensitive personal data, for example) never lands in your warehouse. When a new field appears, it becomes available for you to add. What happens to removed or renamed fields depends on a table's sync mode: full-refresh tables always match what's currently in Freightview, so dropped fields disappear, while incremental tables keep their existing columns and history, so an old field stays and newly added fields fill in over time.
How do I handle GDPR or data deletion requests?
Your data lives in your own Kaivo-managed BigQuery warehouse, so the most direct option is to delete or anonymise specific records right in BigQuery. If you delete data in Freightview instead, full-refresh tables drop it on the next sync, while incremental tables keep it, so you would remove the row in BigQuery or ask us to run a full refresh. To remove everything, delete the Freightview connector in Kaivo and all of its synced data is deleted with it.
Common use cases for Freightview data
Shipping cost
Use shipments and quotes to track freight spend and compare carrier rates over time.
Delivery tracking
Join shipments with tracking to monitor delivery performance.
Quote analysis
Use quotes to see how often you book the lowest rate.
Use Freightview data in your AI and BI tools
Once Freightview data lands in your Kaivo-managed BigQuery warehouse, you can explore it with AI tools or any BI tool that connects to BigQuery. Here's how the most common destinations work with Freightview data.
Claude
Use Kaivo's MCP server to give Claude secure, workspace-scoped access to your data. Setup guide →
Power BI
Microsoft's BI tool with a native BigQuery connector. Supports direct query and scheduled refresh. Setup guide →
Data Studio
Free Google BI tool with native BigQuery support. One-click connection to your Kaivo warehouse; great for SMB teams on Google Workspace. Setup guide →
Tableau
The premium analytics standard, with native BigQuery integration. Setup guide →
Google Sheets
Use Connected Sheets to query BigQuery directly from a spreadsheet, with no SQL. Setup guide →
Excel
Connect via Power Query's BigQuery connector. Setup guide →
Metabase
Open-source BI tool with strong BigQuery support. Setup guide →
See our pricing page for Freightview connector pricing and plan details.
Related connectors
- Google Analytics: Sync Google Analytics to BigQuery.
- Recharge: Sync Recharge to BigQuery.
- Extensiv 3PL Warehouse Manager: Sync Extensiv 3PL Warehouse Manager to BigQuery.
- WooCommerce: Sync WooCommerce to BigQuery.
- Amazon Selling Partner: Sync Amazon Selling Partner to BigQuery.
- Adobe Commerce: Sync Adobe Commerce to BigQuery.
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