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Data warehouse ⇄ Database

Databricks to TimescaleDB integration — real-time, two-way sync

Keep Databricks and TimescaleDB 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 Databricks and TimescaleDB

Connect TimescaleDB and Databricks 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 TimescaleDB's rows in Databricks, 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 TimescaleDB where the services that read from it get them at normal query latency.

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

Common use cases

  • Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.
  • Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
  • Replicate subscription and billing events from operational Postgres tables into Timescale hypertables for time-series analysis.
  • Keep device or asset reference tables bi-directionally in sync between TimescaleDB and an ERP.

Operational data in the warehouse, minus the pipeline

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

Serve warehouse results at database speed

Aggregates or model outputs computed in Databricks sync into TimescaleDB, 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.

What you can sync between Databricks and TimescaleDB

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.

Databricks objects TimescaleDB objects
Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. Continuous Aggregates Incrementally maintained rollups that serve as pre-aggregated read sources for downstream systems.
Schemas Group tables and views; syncs typically target a dedicated schema per source system. Regular PostgreSQL Tables Relational reference data such as devices, tenants, or accounts synced alongside the series data.
Delta Tables The primary read and write target; operational data lands here as managed or external tables. Views Standard SQL views used to shape or filter data for consumers.
Views Curated read-only projections used as sync sources for downstream tools. Schemas Postgres namespaces used to separate synced datasets by team or environment.
Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. Hypertables Time-partitioned tables that hold the main time-series data; the primary read and write target in syncs.
Volumes Unity Catalog file storage used for staging bulk loads. Chunks Time-bounded partitions of a hypertable; syncs read and write through the parent hypertable and never address chunks directly.
What ships with Databricks ⇄ TimescaleDB

Connect Databricks and TimescaleDB for flexible, real-time data sync.

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

Real-time

Two-way sync

Changes in Databricks or TimescaleDB instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks or TimescaleDB 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 Databricks or TimescaleDB record.

Observability

Monitoring

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

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Databricks and TimescaleDB.

How the Databricks and TimescaleDB connectors work

Databricks

Integration surface
SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution
Authentication
Personal access tokens or OAuth machine-to-machine credentials for service principals
Change detection
Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns
Capabilities
read · write · CDC
Rate limits
Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits

TimescaleDB

Integration surface
SQL wire protocol (PostgreSQL)
Authentication
Database credentials
Change detection
Log-based capture via PostgreSQL logical decoding where the deployment allows it — hypertable changes surface on the underlying chunk tables and must be remapped to the parent — or timestamp-based polling on time columns; regular Postgres tables replicate through standard logical replication
Capabilities
read · write · CDC
Rate limits
No API rate limits; throughput is bounded by database resources and connection limits.
How it works

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

    Choose tables

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

Databricks and TimescaleDB 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 Databricks and TimescaleDB.

Popular · 8 of 386
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