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Data warehouse ⇄ Database

Firebolt to PostgreSQL integration — real-time, two-way sync

Keep Firebolt and PostgreSQL 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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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Firebolt and PostgreSQL

Connect PostgreSQL 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 PostgreSQL'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 PostgreSQL where the services that read from it get them at normal query latency.

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

Common use cases

  • 01 Keep dimension tables aligned with source systems while high-volume event data loads through separate batch pipelines.
  • 02 Sync aggregated results from Firebolt back to operational tools that need computed metrics (reverse ETL).
  • 03 Feed reporting and BI from a continuously synced Postgres replica instead of scheduled ETL scripts
  • 04 Expose SaaS objects (CRM contacts, ERP invoices, support tickets) as Postgres tables that internal tools can query and join

Common sync patterns

Serve warehouse results at database speed

Aggregates or model outputs computed in Firebolt sync into PostgreSQL, 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 PostgreSQL focused on its operational workload.

What you can sync between Firebolt and PostgreSQL

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.

Firebolt objects PostgreSQL objects How this pairing syncs
Tables Managed columnar tables written with SQL; the main sync destination. Tables The primary sync target; rows map one-to-one to records in connected SaaS systems. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Views Curated query surfaces commonly used as sources for reverse ETL. Views Read-side projections used to expose joined or filtered data to a sync. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Aggregating indexes Precomputed rollups maintained at write time; incremental loads update them automatically. Schemas Namespaces that scope which tables a sync reads and writes. Aggregating indexes is specific to Firebolt and Schemas to PostgreSQL — each maps to any object or custom field on the other side.
Engines Compute resources that must be running for a sync to read or write. Columns Field-level mapping targets; types are mapped to the connected system's field types. Engines is specific to Firebolt and Columns to PostgreSQL — each maps to any object or custom field on the other side.
Databases Logical containers holding the tables a sync targets. Primary and Unique Keys Used as match keys for idempotent upserts and conflict resolution. Databases is specific to Firebolt and Primary and Unique Keys to PostgreSQL — each maps to any object or custom field on the other side.
External tables References to files in object storage used to stage bulk loads. JSONB Columns Hold semi-structured payloads such as nested SaaS objects or metadata. External tables is specific to Firebolt and JSONB Columns to PostgreSQL — each maps to any object or custom field on the other side.

How changes propagate between Firebolt and PostgreSQL

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.

Firebolt PostgreSQL Interval-based propagation

DetectionStacksync polls Firebolt for changes on an incremental schedule, reading only records changed since the previous pass. Polling.

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

PostgreSQL Firebolt Sub-second propagation

DetectionChanges in PostgreSQL are captured at the source via change data capture — no polling loop against its API. Logical replication (wal_level = logical) for change data capture via the "Postgres" connector.

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

Rate-limit considerations

  • Firebolt: No fixed request quota; throughput depends on the engine size attached to the workload.
  • PostgreSQL: No API rate limits; throughput is bounded by connection limits, instance resources, and replication slot throughput.
What ships with Firebolt ⇄ PostgreSQL

Connect Firebolt and PostgreSQL for flexible, real-time data sync.

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

Real-time

Two-way sync

Changes in Firebolt or PostgreSQL instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Firebolt or PostgreSQL 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 Firebolt or PostgreSQL record.

Observability

Monitoring

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

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Firebolt and PostgreSQL.

How the Firebolt and PostgreSQL connectors work

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

PostgreSQL

Integration surface
SQL wire protocol (PostgreSQL frontend/backend protocol)
Authentication
Database credentials (connection string or parameters), with optional SSL root certificate upload and optional SSH tunnel (SSH user + host); a least-privilege DB user
Change detection
Logical replication (wal_level = logical) for change data capture via the "Postgres" connector; database triggers (TRIGGER grant + stacksync_logging schema) via the trigger-based "Postgres Heroku" connector where
Capabilities
read · write · CDC
Rate limits
No API rate limits; throughput is bounded by connection limits, instance resources, and replication slot throughput
PostgreSQL setup guide
How it works

How to connect Firebolt to PostgreSQL — 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 Firebolt and PostgreSQL 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
    Firebolt connected
    PostgreSQL connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Firebolt and PostgreSQL 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 · Firebolt ⇄ PostgreSQL
    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
    Firebolt PostgreSQL
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Firebolt and PostgreSQL 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.

Learn more about security
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:

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 480 integrations available for Firebolt and PostgreSQL.

Popular · 6 of 480
All Firebolt integrations All PostgreSQL integrations Browse all integrations
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