SAP Fieldglass connector

Set up the SAP Fieldglass connector in Kaivo: authentication, configuration, the 1 BigQuery tables it syncs, and answers to common questions.

Written By Lauri Raivio

Last updated 16 days ago

Kaivo is a fully managed data platform that syncs your SAP Fieldglass 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 HR data instead of moving it.

What is the SAP Fieldglass connector

Sync your SAP Fieldglass data into BigQuery with Kaivo to analyse your external workforce and services spend.

Category	HR
Authentication	API key
Setup	Self-service

Getting started with the SAP Fieldglass connector

Sign up for Kaivo and create a workspace.
Connect your SAP Fieldglass 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 SAP Fieldglass

Authenticate with your API Key.

Field	Description
API Key	Your SAP Fieldglass API key, injected as the `apikey` header on each request.

Tables and columns synced from SAP Fieldglass

Kaivo syncs 1 table from SAP Fieldglass into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.

data (37 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`bill_rate`	`STRING`	Worker?s Bill Rate that is associated to the RateCategory/UOM listed in the previous field.
`billable_per_diem`	`STRING`
`business_unit_code`	`STRING`	Business Unit Code.
`business_unit_name`	`STRING`	Business Unit Name.
`buyer_code`	`STRING`	Buyer Company Code.
`cost_center_code`	`STRING`	Cost Center Code in SAP Fieldglass.
`cost_center_name`	`STRING`	Cost Center Name in SAP Fieldglass.
`currency`	`STRING`	ISO currency designation (ex. USD).
`end_date`	`STRING`	Work order end date.
`first_name`	`STRING`	First name of the Worker as defined in SAP Fieldglass.
`job_posting_title`	`STRING`	Job Posting?s Title or SOW Name for SOW Workers.
`job_seeker_id`	`STRING`	14-character alphanumeric SAP Fieldglass Job Seeker ID.
`last_name`	`STRING`	Last name of the Worker as defined in SAP Fieldglass.
`pay_rate`	`STRING`	Worker?s Pay Rate that is associated to the Rate Category/UOMlisted in the previous field. If Supplier did not enter a Pay Rate, 0.00 will bedisplayed.
`person_id`	`STRING`	Unique identifier for the worker as a person.
`rate_category_uom`	`STRING`	Worker?s Rate Category/Unit Of Measure for which thecorresponding rate will be displayed. More than one Rate Category/UOM may exist for a singleWorker. If this is the case a new line will appear for eachassociated Rate Category/UOM in this file.
`remit_to_address_code`	`STRING`	Code as assigned to the Remit To Address by supplier.
`security_id`	`STRING`	This column will only appear in the file if the security IDfield functionality is activated in SAP Fieldglass for thebuyer. If the functionality is not activated, this columnwill not be in the file.
`segmented_object_detail`	`STRING`	Segmented Object Detail string. Segments will be separated bydelimiter on the Segmented Object.Valid delimiters are colon (:), semi-colon (;), pipe (\|), anddash (-).Required if ?Enable Segmented Object Detail? configuration isset.This field is mutually exclusive with GL Account fields.More than one string can appear for a cost center.
`sequence`	`STRING`	Work Order Revision Number.
`site_code`	`STRING`	Site Code.
`site_name`	`STRING`	Site Name.
`start_date`	`STRING`	Work order start date.
`status`	`STRING`	Status of the worker.
`vendor_name`	`STRING`	Supplier name.
`vendor_number`	`STRING`	SAP Fieldglass Supplier code for buyer.
`work_order_id`	`STRING`	Work Order ID.
`work_order_work_order_revision_owner`	`STRING`	Work Order Owner?s name.
`work_order_work_order_revision_owner_employee_id`	`STRING`	Work Order Owner?s Employee ID.
`worker_email`	`STRING`	Worker?s e-mail address.
`worker_id`	`STRING`	14-character alphanumeric SAP Fieldglass Worker ID.
`_c_buyer_or_supplier_custom_fields`	`STRING`	Custom fields found on the supplier. (For supplier sidedownload, only those fields required by buyer for supplierto be entered and viewed are downloaded.)If there are manycustom fields, there will be many columns.Column headerformat will be: ?[c]modulename_custom field name? (i.e. [c] followed bymodule custom text lowercase with no spaces, followed byunderscore, followed by the custom field name text asdefined by users in the SAP Fieldglass application.)
`_c_work_order_custom_fields`	`STRING`	Custom fields found on the work order/work order revision. If there are many custom fields, there will be many columns.Column header format will be:?[c]modulename_custom field name? (i.e. [c] followed bymodule custom text lowercase with no spaces, followed byunderscore, followed by the custom field name text asdefined by users in the SAP Fieldglass application.)
`_c_worker_custom_fields`	`STRING`	Custom fields found on the worker.If there are many custom fields, there will be manycolumns.Column header format will be: "[c]modulename_custom field name" (i.e. [c] followed bymodule custom text lowercase with no spaces, followed byunderscore, followed by the custom field name text asdefined by users in the SAP Fieldglass application.)
`_c_worker_user_person_custom_fields`	`STRING`	Custom fields found on the worker user personIf there are many custom fields, there will be many columns.Column header format will be: ?[c]modulename_custom field name? (i.e. [c] followed bymodule custom text lowercase with no spaces, followed byunderscore, followed by the custom field name text asdefined by users in the SAP Fieldglass application.)
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

How the SAP Fieldglass sync works

After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where SAP Fieldglass 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 SAP Fieldglass?

It depends on how much history is in your SAP Fieldglass 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 SAP Fieldglass'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 SAP Fieldglass, 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.

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 SAP Fieldglass 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 SAP Fieldglass connector in Kaivo and all of its synced data is deleted with it.

Common use cases for SAP Fieldglass data

Workforce reporting

Use the data feed to report on your external workforce over time.

Spend analysis

Analyse Fieldglass data to track services spend across vendors.

Combine with other data

Bring Fieldglass data into BigQuery to join it with your finance and HR data.

Use SAP Fieldglass data in your AI and BI tools

Once SAP Fieldglass 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 SAP Fieldglass 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 SAP Fieldglass connector pricing and plan details.

Greenhouse: Sync Greenhouse to BigQuery.
Kellokortti: Sync Kellokortti to BigQuery.
Nepton: Sync Nepton to BigQuery.
Teamtailor: Sync Teamtailor to BigQuery.
7shifts: Sync 7shifts to BigQuery.
Ashby: Sync Ashby to BigQuery.

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