STEP 1
Connect AWS DynamoDB 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.
AWS DynamoDB and Google BigQuery Integration
Build database workflows between AWS DynamoDB and Google BigQuery on a visual canvas — set up data fields sync between apps once and let data flow when events come automatically. Integration will work without code and your team will see the workflow inside WEXTL® builder.
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Learn about AWS DynamoDB and Google BigQuery
Detailed info you need to know about AWS DynamoDB and Google BigQuery integrations in WEXTL. Explore which modules apps support and how you connect them.
AWS DynamoDB
Read, write, and query items and tables in Amazon DynamoDB. [5]
Google BigQuery
Query and manage Google BigQuery datasets, tables, jobs, and routines — run SQL and stream rows. [6]
How to connect AWS DynamoDB to Google BigQuery
Set up your AWS DynamoDB and Google BigQuery integration in 3 simple steps [2] — no coding required. Up to 10 minutes setup.
STEP 2
Build a workflow
Paste selected AWS DynamoDB 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 AWS DynamoDB and Google BigQuery. Use filters or leverage a JS custom code step in between — everything is on the canvas.
How does the integration between AWS DynamoDB and Google BigQuery work in WEXTL?
WEXTL exposes actions and triggers for AWS DynamoDB 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 triggersAWS DynamoDB 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.
AWS DynamoDB modules
- Batch Get ItemsBatch Get Items via Amazon AWS.
- Batch Write ItemsBatch Write Items via Amazon AWS.
- Create BackupCreate Backup via Amazon AWS.
- Create TableCreate Table via Amazon AWS.
- Delete BackupDelete Backup via Amazon AWS.
- Delete ItemDelete Item via Amazon AWS.
- Delete Items In BatchDelete Items In Batch via Amazon AWS.
- Delete TableDelete Table via Amazon AWS.
- Describe LimitsDescribe Limits via Amazon AWS.
- Describe TableDescribe Table via Amazon AWS.
- Get ItemGet Item via Amazon DynamoDB.
- List TablesList Tables via Amazon AWS.
- List Tags Of ResourceList Tags Of Resource via Amazon AWS.
- Make an API CallPerforms an arbitrary authorized API call to the AWS DynamoDB endpoint.
- Put ItemPut Item via Amazon AWS.
- QueryQuery via Amazon AWS.
- ScanScan via Amazon AWS.
- Update Continuous BackupsUpdate Continuous Backups via Amazon AWS.
- Update ItemUpdate Item via Amazon AWS.
- Update TableUpdate Table via Amazon AWS.
- Update Time To LiveUpdate Time To Live via Amazon AWS.
Explore alternative integrations
There are other apps [9] supported by WEXTL that can be paired with AWS DynamoDB 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 AWS DynamoDB and Google BigQuery.
Connect your AWS DynamoDB 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 "Batch Get Items" in AWS DynamoDB. AWS DynamoDB has 21 actions (including Batch Get Items, Batch Write Items, and Create Backup); 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. AWS DynamoDB-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 AWS DynamoDB (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 AWS DynamoDB + 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 AWS DynamoDB and Google BigQuery integration.
- AWS DynamoDB and Google BigQuery integration source code, module specs and credentials implementation.
- Our AWS DynamoDB and Google BigQuery integration documentation, structured input and output parameters, scopes, and authentication methods.
- Official vendor API documentation for AWS DynamoDB 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.
- AWS DynamoDB integration information
- Google BigQuery integration information
- WEXTL® Scheduling
- WEXTL® Pricing and Trial
- WEXTL® Apps Library