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Data warehouse ⇄ Analytics

BigQuery to Splunk integration — real-time, two-way sync

Keep BigQuery and Splunk in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

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Why teams connect BigQuery and Splunk

Put the same events, users, and metrics on both sides: Splunk and BigQuery stay current in real time, in both directions.

Splunk is where teams explore, visualize, and report; BigQuery is the store of record that holds the raw tables and full history behind those views. The two overlap wherever the same events, users, and metrics matter to both, and when the bridge between them is a nightly export or a hand-built extract, dashboards lag the warehouse and analysts spend the morning arguing over whose number is right.

Stacksync syncs Dashboards, Search Results, Saved Searches, Fired Alerts in Splunk with Partitioned tables, Clustered tables, Datasets, Projects in BigQuery field by field, in real time, and in both directions. You decide which system owns which fields, and Stacksync resolves conflicts by rules you set. Whether the flow is warehouse tables feeding live reports or captured events and segments landing back in BigQuery, every copy stays consistent.

Common use cases

  • 01 Read and write KV Store collections to keep Splunk lookups — asset inventories, allow/deny lists, enrichment tables — in sync with an external source of truth in both directions.
  • 02 Provision and update Saved Searches, alerts, and Dashboards from a Git-backed config repository so detections and reports stay versioned and consistent across search heads.
  • 03 Activate modeled BigQuery tables by syncing computed attributes back into sales and marketing tools
  • 04 Maintain a customer master table in BigQuery joined across CRM, billing, and support sources

Common sync patterns

Shared user and account keys

Users and accounts tracked in Splunk line up with the customer or user rows in BigQuery on a stable key, so both sides count the same population.

Corrections propagate instead of reloading

When a record is fixed or backfilled on one side, the change reaches the other without a full reload, keeping history consistent across both.

One number both sides agree on

Metrics and aggregates stay aligned between the two systems, so a figure shown in Splunk matches the BigQuery table it was built from instead of drifting between refreshes.

What you can sync between BigQuery and Splunk

Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.

BigQuery objects Splunk objects How this pairing syncs
Partitioned tables Synced like regular tables; partition columns map to target fields. Search Results SPL searches dispatched via POST /services/search/jobs return a search ID (SID); results are pulled from /services/search/jobs/{sid}/results once the job completes, or synchronously via oneshot/export mode. The primary read path for streaming indexed events out to a warehouse. Partitioned tables is specific to BigQuery and Search Results to Splunk — each maps to any object or custom field on the other side.
Clustered tables Supported; clustering is transparent to the sync. Saved Searches Scheduled searches, reports, and the definitions behind alerts at /services/saved/searches with full create, update, and delete. Read out for governance and coverage review, or provisioned and updated from a config source. Clustered tables is specific to BigQuery and Saved Searches to Splunk — each maps to any object or custom field on the other side.
Datasets Organizational container — you pick which dataset’s tables to sync. Fired Alerts Triggered alert instances listed at /services/alerts/fired_alerts; alert configuration (conditions, schedule, actions) lives on the corresponding saved search. Landed in a database for alert-trend and detection-coverage reporting. Datasets is specific to BigQuery and Fired Alerts to Splunk — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. KV Store Collections App-scoped, MongoDB-backed key-value collections at /servicesNS/{owner}/{app}/storage/collections/data/{collection} with full CRUD and batch endpoints. Genuinely bidirectional lookup/state store — read records out or write records in. Projects is specific to BigQuery and KV Store Collections to Splunk — each maps to any object or custom field on the other side.
Tables The syncable unit: only tables can be synced per the Stacksync docs. Indexes Index inventory and settings (retention, max size, event counts) via /services/data/indexes, with create and edit; loaded into a database for capacity, retention, and data-onboarding tracking. Tables is specific to BigQuery and Indexes to Splunk — each maps to any object or custom field on the other side.

How changes propagate between BigQuery and Splunk

Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.

BigQuery Splunk Sub-second propagation

DetectionChanges in BigQuery are captured at the source via change data capture — no polling loop against its API. Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen").

DeliveryEach detected change is written to Splunk through its API, with automatic retries and rate-limit backoff.

Splunk BigQuery Sub-second propagation

DetectionSplunk notifies Stacksync of record changes through webhook events. Time-range searches over indexed events (earliest/latest on _time or _indextime).

DeliveryEach detected change is applied to BigQuery as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • BigQuery: Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes.
  • Splunk: The management REST API throttles through search-concurrency quotas — max concurrent searches per user and role, plus per-CPU historical search limits in limits.conf — rather than a fixed per-request rate; exceeding quota queues or blocks jobs. HEC has per-token and per-instance throughput limits, and Splunk Cloud adds ingestion and API limits on top.
What ships with BigQuery ⇄ Splunk

Connect BigQuery and Splunk for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every BigQuery–Splunk connection.

Real-time

Two-way sync

Changes in BigQuery or Splunk instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or Splunk data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single BigQuery or Splunk record.

Observability

Monitoring

Track your BigQuery ⇄ Splunk sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between BigQuery and Splunk.

How the BigQuery and Splunk connectors work

BigQuery

Integration surface
GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs
Authentication
Google Cloud service account: create a dedicated service account, grant roles (BigQuery Data Editor, BigQuery Job User, Cloud Functions Service Agent, Cloud Run Developer, Eventarc Event Receiver
Change detection
Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen") with a Cloud Run "secure portal for real-time notification service in
Capabilities
read · write · CDC
Rate limits
Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes
BigQuery setup guide

Splunk

Integration surface
REST API (management API + HTTP Event Collector)
Authentication
HTTP Basic (username/password), or a session key from POST /services/auth/login sent as Authorization: Splunk <key>, or a bearer authentication token (Authorization: Bearer <token>). The HTTP Event Collector uses its own per-input token (Authorization: Splunk <hec-token>). Management API defaults to port 8089; HEC to port 8088 (443 on Splunk Cloud).
Change detection
Time-range searches over indexed events (earliest/latest on _time or _indextime); events are immutable once indexed, so incremental extraction advances a time cursor rather than a modified-date CDC feed. Config objects such as saved searches and KV Store are polled; alerts can push via a saved-search webhook action.
Capabilities
read · write · webhooks
Rate limits
The management REST API throttles through search-concurrency quotas — max concurrent searches per user and role, plus per-CPU historical search limits in limits.conf — rather than a fixed per-request rate; exceeding quota queues or blocks jobs. HEC has per-token and per-instance throughput limits, and Splunk Cloud adds ingestion and API limits on top.
How it works

How to connect BigQuery to Splunk — three steps, no code

Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.

  1. 01

    Connect your apps

    Authenticate BigQuery and Splunk with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    BigQuery connected
    Splunk connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the BigQuery and Splunk objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · BigQuery ⇄ Splunk
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    BigQuery Splunk
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

BigQuery and Splunk integration FAQ

SECURITY

Security teams trust Stacksync

As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.

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SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 514 integrations available for BigQuery and Splunk.

Popular · 8 of 514
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