STEP 1
Connect Elastic 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.
Elastic and Google BigQuery Integration
Keep Elastic and Google BigQuery in sync by integrating their events and actions. When database events happen in Elastic, update the Google BigQuery automatically - without any line of code in visual workflow builder.
3-day trialCancel anytimeUp to 10 minutes quick set up
- GDPR
- Data Encryption
- 2FA
- Local Data Region
Learn about Elastic and Google BigQuery
Detailed info you need to know about Elastic and Google BigQuery integrations in WEXTL. Explore which modules apps support and how you connect them.
Google BigQuery
Query and manage Google BigQuery datasets, tables, jobs, and routines — run SQL and stream rows. [6]
How to connect Elastic to Google BigQuery
Set up your Elastic and Google BigQuery integration in 3 simple steps [2] — no coding required. Up to 10 minutes setup.
STEP 2
Build a workflow
Paste selected Elastic 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 Elastic and Google BigQuery. Use filters or leverage a JS custom code step in between — everything is on the canvas.
How does the integration between Elastic and Google BigQuery work in WEXTL?
WEXTL exposes actions and triggers for Elastic 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 triggersElastic 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.
Elastic modules
- Analyze Index TextSplits text into search tokens the way an index or one of its fields would.
- Analyze TextSplits text into search tokens with a built-in analyzer, tokenizer, or filters.
- Bulk Index DocumentsIndexes, creates, updates, or deletes many documents in one request.
- Cancel TaskCancels a running task, such as a long reindex or a delete by query.
- Clean Snapshot RepositoryRemoves leftover files that no snapshot references anymore from a snapshot repository.
- Clone IndexCopies an existing index into a new index with the same shards and data.
- Close IndexCloses an index so it keeps its data but can no longer be searched or written to.
- Count DocumentsCounts the documents across all indices that match a query.
- Count Index DocumentsCounts the documents in an index that match a query.
- Create Component TemplateCreates a reusable building block of settings and mappings for index templates, or replaces one with the same name.
- Create Data StreamCreates a data stream for append-only time series data, using a matching index template.
- Create DocumentAdds a new document with the ID you choose and fails if that ID is already taken.
- Create ILM PolicyCreates an index lifecycle policy, or replaces the policy that has the same name.
- Create IndexCreates a new index with optional settings, field mappings, and aliases.
- Create Index AliasAdds an alias to an index, or updates the options of an existing alias.
- Create Index TemplateCreates an index template that applies settings and mappings to new matching indices, or replaces one with the same name.
- Create Ingest PipelineCreates an ingest pipeline that transforms documents before they are stored, or replaces one with the same ID.
- Create SnapshotTakes a backup snapshot of indices, data streams, or the whole cluster into a repository.
- Create Snapshot RepositoryRegisters a storage location for snapshots, such as an S3 bucket, or updates an existing repository.
- Delete Component TemplateDeletes a component template that no index template uses anymore.
- Delete Data StreamDeletes a data stream together with all of its backing indices and documents.
- Delete Data Stream LifecycleRemoves the lifecycle from a data stream so its data is no longer expired or downsampled.
- Delete DocumentDeletes a single document by its ID.
- Delete Documents by QueryDeletes every document in an index that matches a query.
- Delete ILM PolicyDeletes an index lifecycle policy that no index uses anymore.
- Delete IndexDeletes an index together with all of its documents.
- Delete Index AliasRemoves an alias from an index without touching the index data.
- Delete Index TemplateDeletes an index template without changing indices that already exist.
- Delete Ingest PipelineDeletes an ingest pipeline.
- Delete SnapshotDeletes a snapshot from a repository, or cancels it while it is still running.
- Delete Snapshot RepositoryUnregisters a snapshot repository while leaving its stored snapshots in place.
- Explain AllocationExplains why a shard is unassigned or why it stays on its current node.
- Explain DocumentExplains why a single document does or does not match a query and how its score was computed.
- Explain Index LifecycleReturns the lifecycle phase, action, and step an index is in, and any lifecycle error.
- Flush All IndicesWrites the in-memory changes of every index to disk and clears their transaction logs.
- Flush IndexWrites the in-memory changes of an index to disk and clears its transaction log.
- Force Merge IndexMerges the segments of an index to reclaim space and speed up searches on data that no longer changes.
- Get All MappingsRetrieves the field mappings of every index in the cluster.
- Get All SettingsRetrieves the settings of every index in the cluster.
- Get Catalog HealthReturns a one-line summary of cluster health with node and shard counts.
- Get Cluster HealthReturns the health status of the cluster together with node and shard counts.
- Get Cluster SettingsReturns the persistent and transient settings configured for the cluster.
- Get Cluster StateReturns the internal state of the cluster, including nodes, index metadata, and shard routing.
- Get Cluster StatsReturns cluster-wide statistics about indices, documents, storage, and nodes.
- Get Component TemplateReturns a component template with its settings, mappings, and aliases.
- Get Data StreamReturns a data stream with its backing indices, template, and health.
- Get Data Stream LifecycleReturns the retention and downsampling lifecycle of a data stream.
- Get Data Stream MappingsReturns the mappings set on a data stream and the effective mappings merged with its template.
- Get Data Stream SettingsReturns the settings set on a data stream and the effective settings merged with its template.
- Get Data Stream StatsReturns storage size, backing index count, and latest timestamp for every data stream.
Explore alternative integrations
There are other apps [9] supported by WEXTL that can be paired with Elastic 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 Elastic and Google BigQuery.
Connect your Elastic 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 "Analyze Index Text" in Elastic. Elastic has 123 actions (including Analyze Index Text, Analyze Text, and Bulk Index Documents); 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. Elastic-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 Elastic (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 Elastic + 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 Elastic and Google BigQuery integration.
- Elastic and Google BigQuery integration source code, module specs and credentials implementation.
- Our Elastic and Google BigQuery integration documentation, structured input and output parameters, scopes, and authentication methods.
- Official vendor API documentation for Elastic 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.
- Elastic integration information
- Google BigQuery integration information
- WEXTL® Scheduling
- WEXTL® Pricing and Trial
- WEXTL® Apps Library