Real-time
Two-way sync
Changes in BigQuery or Dremio instantly reflect in both systems. No stale data, no manual imports.
Data warehouse
BigQuery to Dremio integration — real-time, two-way sync
Keep BigQuery and Dremio in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
- SOC 2 and 6 other compliance frameworks
- POC with real engineers in minutes
Adopted by fast-scaling companies moving mission-critical data in real time
Case study
Migrated from MuleSoft
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Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
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Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect BigQuery and Dremio
Keep tables consistent across BigQuery and Dremio, for a migration, a multi-warehouse stack, or a dataset two platforms both need.
Companies end up with two warehouses for practical reasons: a migration in progress, teams that standardized on different platforms, an acquisition, or tools that only connect to one of them. The result is the same dataset maintained twice, with duplicated pipelines and numbers that almost match.
Stacksync syncs tables between BigQuery and Dremio continuously, in either or both directions. Rows changed on one platform appear on the other within seconds, with schema and type mapping handled, so both warehouses answer questions with the same data.
Common use cases
- 01 Activate modeled BigQuery tables by syncing computed attributes back into sales and marketing tools
- 02 Maintain a customer master table in BigQuery joined across CRM, billing, and support sources
- 03 Publish operational database tables into Iceberg via Dremio so the lakehouse reflects current application state.
- 04 Consolidate data from multiple lake sources through one Dremio semantic layer into a single warehouse target.
Common sync patterns
Consolidation after M&A
Bring the acquired company's warehouse data across continuously instead of through one-off dumps.
Migration without a big bang
When one platform is replacing the other, keep tables mirrored while workloads move over gradually, and cut over with nothing to backfill.
Serve tools that only connect to one platform
Mirror the datasets a BI tool, notebook, or application needs onto the platform it can actually reach.
What you can sync between BigQuery and Dremio
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 | Dremio objects | How this pairing syncs | |
|---|---|---|---|
| Clustered tables Supported; clustering is transparent to the sync. | Sources Connected storage and database systems (S3, ADLS, relational databases) Dremio queries in place. | Clustered tables is specific to BigQuery and Sources to Dremio — each maps to any object or custom field on the other side. | |
| Datasets Organizational container — you pick which dataset’s tables to sync. | Physical datasets Tables and files promoted from sources; the raw data a sync ultimately reads. | Datasets is specific to BigQuery and Physical datasets to Dremio — each maps to any object or custom field on the other side. | |
| Projects Connection scope: the service account grants access per project. | Virtual datasets (views) SQL views layering semantics over physical data; the preferred sync target for curated extracts. | Projects is specific to BigQuery and Virtual datasets (views) to Dremio — 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. | Apache Iceberg tables Lakehouse tables supporting DML and snapshot metadata usable for incremental reads. | Tables is specific to BigQuery and Apache Iceberg tables to Dremio — each maps to any object or custom field on the other side. | |
| Partitioned tables Synced like regular tables; partition columns map to target fields. | Spaces and folders Namespaces that organize virtual datasets and govern access. | Partitioned tables is specific to BigQuery and Spaces and folders to Dremio — each maps to any object or custom field on the other side. |
How changes propagate between BigQuery and Dremio
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 Dremio 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 applied to Dremio as a row-level write, with types converted between the two schemas.
Dremio BigQuery Interval-based propagation
DetectionStacksync polls Dremio for changes on an incremental schedule, reading only records changed since the previous pass. Polling via SQL.
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.
- Dremio: Bounded by engine capacity and workload management rather than API rate limits.
What ships with BigQuery ⇄ Dremio
Connect BigQuery and Dremio for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every BigQuery–Dremio connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever BigQuery or Dremio 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 Dremio record.
Observability
Monitoring
Track your BigQuery ⇄ Dremio 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 Dremio.
How the BigQuery and Dremio 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
Dremio
- Integration surface
- Arrow Flight SQL, JDBC/ODBC, and a REST API
- Authentication
- Personal access tokens or username/password; OAuth-based SSO on Dremio Cloud
- Change detection
- Polling via SQL; Iceberg table snapshots can anchor incremental reads; no consumer-facing change feed
- Capabilities
- read · write
- Rate limits
- Bounded by engine capacity and workload management rather than API rate limits
How it works
How to connect BigQuery to Dremio — 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.
- 01
Connect your apps
Authenticate BigQuery and Dremio with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
BigQuery
connected
Dremio
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the BigQuery and Dremio 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.
objects · BigQuery ⇄ DremioCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
BigQuery DremioCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
BigQuery and Dremio integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between BigQuery and Dremio: authenticate both systems, choose the objects to sync (such as BigQuery's Clustered tables and Datasets), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Stacksync is SOC 2 Type II and ISO 27001 certified with HIPAA BAA support. Data is encrypted in transit, and a zero-persistent-storage architecture means BigQuery and Dremio records are not retained after a sync operation.
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Stacksync pricing is usage-based and starts at $1,000/month, including the managed BigQuery and Dremio connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom BigQuery–Dremio integration in-house.
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Yes — Stacksync ships production-grade connectors for both BigQuery and Dremio. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection on BigQuery: 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. On Dremio: Polling via SQL; Iceberg table snapshots can anchor incremental reads; no consumer-facing change feed. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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On the BigQuery side: Tables, Partitioned tables, Clustered tables, Datasets, plus custom fields where BigQuery exposes them. On the Dremio side: Sources, Physical datasets, Virtual datasets (views), Apache Iceberg tables. Stacksync auto-detects both schemas and converts types between the two systems.
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.
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
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:
Related integrations
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