STEP 1
Connect FileMaker and Google BigQuery
Save credentials in WEXTL once. We store them encrypted and reuse the authorization across every workflow [3]. No coding or API skills required.
FileMaker and Google BigQuery Integration
Keep FileMaker and Google BigQuery in sync by integrating their events and actions. When database events happen in FileMaker, update the Google BigQuery automatically - without any line of code in visual workflow builder.
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Learn about FileMaker and Google BigQuery
Detailed info you need to know about FileMaker and Google BigQuery integrations in WEXTL. Explore which modules apps support and how you connect them.
FileMaker
Read and write records in FileMaker databases hosted on FileMaker Server or FileMaker Cloud, run scripts, and inspect layout metadata. [5]
Google BigQuery
Query and manage Google BigQuery datasets, tables, jobs, and routines — run SQL and stream rows. [6]
How to connect FileMaker to Google BigQuery
Set up your FileMaker and Google BigQuery integration in 3 simple steps [2] — no coding required. Up to 10 minutes setup.
STEP 2
Build a workflow
Paste selected FileMaker and Google BigQuery nodes onto the canvas [4], or describe what you want and WEXTL wires the flow together with AI Builder.
STEP 3
Map your data
Choose which fields need to sync between FileMaker and Google BigQuery. Use filters or leverage a JS custom code step in between — everything is on the canvas.
How does the integration between FileMaker and Google BigQuery work in WEXTL?
WEXTL exposes actions and triggers for FileMaker and Google BigQuery that help you connect apps and automate repetitive tasks [1]. A trigger is an event that happens, an action — a step that performs when the event has occurred.
Google BigQuery triggersFileMaker actions
Google BigQuery modules
- Cancel JobCancels a running job.
- Create DatasetCreates a new dataset.
- Create JobCreates a query, load, copy, or extract job.
- Create RoutineCreates a new routine.
- Create Row Access PolicyCreates a row access policy.
- Create TableCreates a new table.
- Delete DatasetDeletes a dataset.
- Delete JobDeletes the metadata of a completed job.
- Delete ModelDeletes a model.
- Delete RoutineDeletes a routine.
- Delete Row Access Policies in BatchDeletes several row access policies.
- Delete Row Access PolicyDeletes a row access policy.
- Delete TableDeletes a table.
- Edit DatasetUpdates selected fields on a dataset.
- Edit ModelUpdates selected fields on a model.
- Edit TableUpdates selected fields on a table.
- Execute QueryRuns a SQL query.
- Get DatasetReturns a single dataset.
- Get JobReturns a single job.
- Get ModelReturns a single model.
- Get Query ResultsReturns results for a query job.
- Get RoutineReturns a single routine.
- Get Routine IAM PolicyReturns the IAM policy for a routine.
- Get Row Access PolicyReturns a single row access policy.
- Get Row Access Policy IAM PolicyReturns the IAM policy for a row access policy.
- Get Service AccountReturns the project's BigQuery service account.
- Get TableReturns a single table.
- Get Table IAM PolicyReturns the IAM policy for a table.
- Insert RowsInserts rows into a table.
- List DatasetsRetrieves datasets in a project.
- List JobsRetrieves jobs in a project.
- List ModelsRetrieves models in a dataset.
- List ProjectsRetrieves projects the user can access.
- List RoutinesRetrieves routines in a dataset.
- List Row Access PoliciesRetrieves row access policies on a table.
- List Table DataRetrieves rows from a table.
- List TablesRetrieves tables in a dataset.
- Make an API CallPerforms an arbitrary authorized API call.
- Set Routine IAM PolicySets the IAM policy for a routine.
- Set Table IAM PolicySets the IAM policy for a table.
- Test Routine IAM PermissionsTests IAM permissions on a routine.
- Test Row Access Policy IAM PermissionsTests IAM permissions on a row access policy.
- Test Table IAM PermissionsTests IAM permissions on a table.
- Undelete DatasetRestores a recently deleted dataset.
- Update DatasetReplaces an existing dataset.
- Update RoutineReplaces an existing routine.
- Update Row Access PolicyReplaces a row access policy.
- Update TableReplaces an existing table.
- Watch JobsTriggers when a new BigQuery job is created.
FileMaker modules
- Create RecordCreates a new record on a layout.
- Delete RecordDeletes a record from a layout.
- Duplicate RecordCreates a copy of an existing record on a layout.
- Edit RecordUpdates the field values of an existing record.
- Find RecordsRetrieves the records on a layout that match the given find criteria.
- Get Layout MetadataReturns the field, portal, and value list definitions for a layout.
- Get Product InfoReturns version and locale format information about the FileMaker host.
- Get RecordReturns a single record from a layout by its record ID.
- List DatabasesRetrieves a list of hosted databases available through the Data API.
- List LayoutsRetrieves a list of layouts in the connected database.
- List RecordsRetrieves a list of records from a layout.
- List ScriptsRetrieves a list of scripts in the connected database.
- Make an API CallPerforms an arbitrary authorized API call.
- Run ScriptRuns a FileMaker script in the connected database.
- Set Global Field ValuesSets the values of global fields in the connected database.
- Upload Container DataUploads a file into a container field on a record.
Explore alternative integrations
There are other apps [9] supported by WEXTL that can be paired with FileMaker and Google BigQuery integrations. Pick any from the list below if the current combination isn't the one you need.
Frequently asked questions
Common questions about connecting FileMaker and Google BigQuery.
Connect your FileMaker and Google BigQuery accounts once, pick a trigger in one app and an action in the other, then map the fields so data flows automatically between them. [3]
Example: when Google BigQuery's "Watch Jobs" fires, WEXTL can "Create Record" in FileMaker. FileMaker has 16 actions (including Create Record, Delete Record, and Duplicate Record); Google BigQuery has 48 actions (including Cancel Job, Create Dataset, and Create Job). Add filters and transforms between steps when you need them. [1]
Yes — the integration works in both directions. Use each app's triggers to start a workflow and the other app's actions to update it. [2]
No. FileMaker-to-Google BigQuery automations are built visually on the canvas. You can optionally add custom JavaScript with Functions when you need advanced logic. [4]
Yes. Trigger-based automations start the moment FileMaker (or Google BigQuery) fires a new event — webhook-backed apps run within seconds, and polling-only apps re-check on a schedule you control. [7]
A first automation typically takes 5–10 minutes: connect each app's credential once, drop a trigger + action onto the canvas, map the fields, and turn the workflow on.
You can build your FileMaker + Google BigQuery automation on a 3-day trial with every feature unlocked. Cancel anytime. For your protection, a small deposit is charged to your card when your trial starts. It counts toward your first payment and is non-refundable. [8]
References
List of sources used to gather information about the FileMaker and Google BigQuery integration.
- FileMaker and Google BigQuery integration source code, module specs and credentials implementation.
- Our FileMaker and Google BigQuery integration documentation, structured input and output parameters, scopes, and authentication methods.
- Official vendor API documentation for FileMaker and Google BigQuery — used to verify field meanings, authentication flows, and anything our own specs don't fully capture.
- WEXTL® visual workflow builder, anonymized data from our technical support team.
- FileMaker integration information
- Google BigQuery integration information
- WEXTL® Scheduling
- WEXTL® Pricing and Trial
- WEXTL® Apps Library