Real-time
Two-way sync
Changes in DuckDB or Firebolt instantly reflect in both systems. No stale data, no manual imports.
Database ⇄ Data warehouse
DuckDB to Firebolt integration — real-time, two-way sync
Keep DuckDB and Firebolt 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
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect DuckDB and Firebolt
Connect DuckDB and Firebolt 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 DuckDB's rows in Firebolt, 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 DuckDB where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in DuckDB sync into Firebolt in real time, and result tables in Firebolt sync back into DuckDB, with schema and type mapping between the two systems handled for you.
Common use cases
- Keep dimension tables aligned with source systems while high-volume event data loads through separate batch pipelines.
- Sync aggregated results from Firebolt back to operational tools that need computed metrics (reverse ETL).
- Use DuckDB as a transform step: read synced Parquet exports, aggregate with SQL, and write results back to an operational database.
- Sync SaaS data to Parquet on object storage and query it with DuckDB without standing up a warehouse.
Serve warehouse results at database speed
Aggregates or model outputs computed in Firebolt sync into DuckDB, where whatever reads from that database gets them without querying the warehouse.
Fresh analytics without loading windows
Because changes stream continuously, analysts query current data instead of waiting for last night's load.
Offload heavy reads
Point analytical queries at the synced copy in Firebolt and keep DuckDB focused on its operational workload.
What you can sync between DuckDB and Firebolt
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.
| DuckDB objects | Firebolt objects | |
|---|---|---|
| Tables Columnar tables created via SQL; the destination for materialized sync data. | Tables Managed columnar tables written with SQL; the main sync destination. | |
| Views SQL views used to shape or filter data for downstream consumers. | External tables References to files in object storage used to stage bulk loads. | |
| External files (Parquet/CSV/JSON) Files DuckDB queries in place without loading, common as a sync interchange format. | Views Curated query surfaces commonly used as sources for reverse ETL. | |
| Attached databases Additional database files or external systems attached into one session for cross-source queries. | Aggregating indexes Precomputed rollups maintained at write time; incremental loads update them automatically. | |
| Database files Single-file .duckdb databases that jobs read and write directly on disk or object storage. | Engines Compute resources that must be running for a sync to read or write. | |
| Schemas Namespaces within a database used to organize tables in sync outputs. | Databases Logical containers holding the tables a sync targets. |
What ships with DuckDB ⇄ Firebolt
Connect DuckDB and Firebolt for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every DuckDB–Firebolt connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever DuckDB or Firebolt 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 DuckDB or Firebolt record.
Observability
Monitoring
Track your DuckDB ⇄ Firebolt sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between DuckDB and Firebolt.
How the DuckDB and Firebolt connectors work
DuckDB
- Integration surface
- In-process SQL engine via client libraries (Python, Node.js, JDBC, CLI); no server or network API by default
- Authentication
- None built in; access control is file-system level (MotherDuck adds token auth for its hosted service)
- Change detection
- Polling or full re-reads; no change feed or transaction log API
- Capabilities
- read · write
- Rate limits
- No API rate limits; throughput is bounded by local compute and I/O
Firebolt
- Integration surface
- SQL over a REST API, with JDBC, Python, and Node.js SDKs
- Authentication
- Service account credentials (client ID and secret) exchanged for OAuth 2.0 tokens
- Change detection
- Polling; Firebolt is an analytics destination and does not expose a change feed
- Capabilities
- read · write
- Rate limits
- No fixed request quota; throughput depends on the engine size attached to the workload
How it works
How to connect DuckDB to Firebolt — 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 DuckDB and Firebolt with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
DuckDB
connected
Firebolt
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the DuckDB and Firebolt 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 · DuckDB ⇄ FireboltCustomers 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.
DuckDB FireboltCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
DuckDB and Firebolt integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between DuckDB and Firebolt: authenticate both systems, choose the objects to sync (such as DuckDB's Tables and Views), map fields visually, and changes propagate both ways in milliseconds — no code required.
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On the Firebolt side: External tables, Views, Aggregating indexes, Engines, plus custom fields where Firebolt exposes them. On the DuckDB side: External files (Parquet/CSV/JSON), Attached databases, Database files, Schemas. Stacksync auto-detects both schemas and converts types between the two systems.
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Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
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Common patterns for DuckDB and Firebolt: Serve warehouse results at database speed; Fresh analytics without loading windows; Offload heavy reads. Aggregates or model outputs computed in Firebolt sync into DuckDB, where whatever reads from that database gets them without querying the warehouse.
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DuckDB: In-process SQL engine via client libraries (Python, Node.js, JDBC, CLI); no server or network API by default. Authentication: None built in; access control is file-system level (MotherDuck adds token auth for its hosted service). Firebolt: SQL over a REST API, with JDBC, Python, and Node.js SDKs. Authentication: Service account credentials (client ID and secret) exchanged for OAuth 2.0 tokens. Stacksync manages authentication, retries, and rate limits on both sides.
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Firebolt: Compute is organized into engines that start and stop independently of storage, so sync schedules interact with engine availability and cost. DuckDB: Concurrency is single-writer: one process holds write access to a database file at a time, which shapes how sync jobs schedule writes. Stacksync's field mapping accounts for these differences between DuckDB and Firebolt without custom code.
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.
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:
Related integrations
Every pair below is a real-time, two-way sync. Search all 386 integrations available for DuckDB and Firebolt.
Popular · 8 of 386
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DuckDB ⇄ ClickHouse -
DuckDB ⇄ Cloudera Data Platform -
DuckDB ⇄ Dremio -
DuckDB ⇄ Exasol -
Firebolt ⇄ SQL Server -
Firebolt ⇄ Postgres Heroku -
Firebolt ⇄ Airtable -
Firebolt ⇄ AWS Aurora MySQL
No integrations match .
