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Data warehouse ⇄ Database

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

Keep BigQuery and Elasticsearch 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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  • POC with real engineers in minutes

Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect BigQuery and Elasticsearch

Connect Elasticsearch and BigQuery with one live, two-way sync: operational rows flow into the warehouse, and computed results flow back where systems can read them fast.

Operational databases and analytical warehouses want the same data at different moments. Analysts want Elasticsearch's rows in BigQuery, current and joinable, without a change-data-capture pipeline to maintain. Engineers want the outputs of warehouse work, such as aggregates, features, and segments, available in Elasticsearch where the services that read from it get them at normal query latency.

Stacksync covers both directions with one connection. Tables or collections in Elasticsearch sync into BigQuery in real time, and result tables in BigQuery sync back into Elasticsearch, with schema and type mapping between the two systems handled for you.

Common use cases

  • Maintain a customer master table in BigQuery joined across CRM, billing, and support sources
  • Feed ML feature tables in BigQuery from operational systems on a continuous schedule
  • Push product catalog data from an ERP or commerce database into Elasticsearch for storefront search.
  • Mirror support tickets into an index used for full-text search and agent-assist tooling.

Offload heavy reads

Point analytical queries at the synced copy in BigQuery and keep Elasticsearch focused on its operational workload.

Operational data in the warehouse, minus the pipeline

Rows from Elasticsearch land in BigQuery as they change, replacing hand-built CDC and batch extract jobs.

Serve warehouse results at database speed

Aggregates or model outputs computed in BigQuery sync into Elasticsearch, where whatever reads from that database gets them without querying the warehouse.

What you can sync between BigQuery and Elasticsearch

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 Elasticsearch objects
Partitioned tables Synced like regular tables; partition columns map to target fields. Index templates Reusable settings and mappings applied automatically to new indices a sync creates.
Clustered tables Supported; clustering is transparent to the sync. Indices Target containers for synced records; each holds a table-like collection of JSON documents.
Datasets Organizational container — you pick which dataset’s tables to sync. Documents The unit of sync; JSON records created, updated, and deleted by _id.
Projects Connection scope: the service account grants access per project. Index mappings Field type definitions that determine how synced fields are indexed and queried.
Tables The syncable unit: only tables can be synced per the Stacksync docs. Aliases Stable read/write names that let a sync cut over between index versions without downtime.
What ships with BigQuery ⇄ Elasticsearch

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or Elasticsearch 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 Elasticsearch record.

Observability

Monitoring

Track your BigQuery ⇄ Elasticsearch 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 Elasticsearch.

How the BigQuery and Elasticsearch 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

Elasticsearch

Integration surface
REST API (JSON over HTTP)
Authentication
API keys or basic authentication; Elastic Cloud also issues service account tokens
Change detection
Polling on timestamp or sequence fields; Elasticsearch does not expose a native change feed or webhooks
Capabilities
read · write
Rate limits
No fixed request quota; throughput is bounded by cluster sizing, thread pools, and bulk queue capacity
How it works

How to connect BigQuery to Elasticsearch — 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 Elasticsearch 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
    Elasticsearch connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the BigQuery and Elasticsearch 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 ⇄ Elasticsearch
    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 Elasticsearch
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

BigQuery and Elasticsearch integration FAQ

SECURITY

Security teams love 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.

Learn more about security
SOC 2 type II
ISO 27001
HIPAA BAA
GDPR
CCPA
CSA STAR
DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS

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 386 integrations available for BigQuery and Elasticsearch.

Popular · 8 of 386
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