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Data warehouse ⇄ Analytics

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

Keep Databricks and Treasuredata 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 Treasuredata

Put the same events, users, and metrics on both sides: Treasuredata and Databricks stay current in real time, in both directions.

Treasuredata is where teams explore, visualize, and report; Databricks is the store of record that holds the raw tables and full history behind those views. The two overlap wherever the same events, users, and metrics matter to both, and when the bridge between them is a nightly export or a hand-built extract, dashboards lag the warehouse and analysts spend the morning arguing over whose number is right.

Stacksync syncs Query Jobs, Databases, Tables, Master (Parent) Segments in Treasuredata with Volumes, SQL Warehouses, Change Data Feed, Catalogs in Databricks field by field, in real time, and in both directions. You decide which system owns which fields, and Stacksync resolves conflicts by rules you set. Whether the flow is warehouse tables feeding live reports or captured events and segments landing back in Databricks, every copy stays consistent.

Common use cases

  • 01 Push Predictive Segment propensity scores from TD onto customer records in an operational database or CRM for prioritization and lead routing.
  • 02 Write CRM and product-usage records into TD Tables so they become source tables feeding parent-segment unification.
  • 03 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
  • 04 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.

Common sync patterns

Where Treasuredata produces segments or scores: results back to the warehouse

Cohorts, segments, and computed metrics defined in Treasuredata write to Databricks as tables the rest of the stack can query and join.

Shared user and account keys

Users and accounts tracked in Treasuredata line up with the customer or user rows in Databricks on a stable key, so both sides count the same population.

Corrections propagate instead of reloading

When a record is fixed or backfilled on one side, the change reaches the other without a full reload, keeping history consistent across both.

What you can sync between Databricks and Treasuredata

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 Treasuredata objects How this pairing syncs
Views Curated read-only projections used as sync sources for downstream tools. Tables Columnar log tables in TD's Plazma storage; every row carries a mandatory `time` column (Unix epoch) that Stacksync uses as the incremental watermark and partition key. Synced two-way with warehouse or database tables. Views is specific to Databricks and Tables to Treasuredata — each maps to any object or custom field on the other side.
Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. Master (Parent) Segments Unified customer profiles assembled from multiple source tables in Audience Studio; read out to push enriched attributes onto CRM or warehouse records. Materialized Views is specific to Databricks and Master (Parent) Segments to Treasuredata — each maps to any object or custom field on the other side.
Volumes Unity Catalog file storage used for staging bulk loads. Segments Campaign subsets of a parent segment; membership read out to activate audiences in downstream systems, or audience flags written back onto records. Volumes is specific to Databricks and Segments to Treasuredata — each maps to any object or custom field on the other side.
SQL Warehouses The compute endpoint a sync connects to for query execution. Journeys Timeline-based event sequences in Audience Studio; stage and membership read out for reporting and cross-system activation. SQL Warehouses is specific to Databricks and Journeys to Treasuredata — each maps to any object or custom field on the other side.
Change Data Feed Row-level change records on Delta tables that drive incremental reads. Predictive Segments AI/ML-scored segments; propensity scores read out and written onto customer records in a CRM or database for prioritization. Change Data Feed is specific to Databricks and Predictive Segments to Treasuredata — each maps to any object or custom field on the other side.
Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. Scheduled Queries Cron-scheduled Presto/Trino (or Hive) jobs that materialize results into result tables; Stacksync reads those materialized tables downstream. Catalogs is specific to Databricks and Scheduled Queries to Treasuredata — each maps to any object or custom field on the other side.

How changes propagate between Databricks and Treasuredata

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.

Databricks Treasuredata Sub-second propagation

DetectionChanges in Databricks are captured at the source via change data capture — no polling loop against its API. Delta Lake Change Data Feed for row-level changes.

DeliveryEach detected change is written to Treasuredata through its API, with automatic retries and rate-limit backoff.

Treasuredata Databricks Interval-based propagation

DetectionStacksync polls Treasuredata for changes on an incremental schedule, reading only records changed since the previous pass. Polling on the mandatory `time` column (Unix-epoch partition key) or an updated-at column.

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

Rate-limit considerations

  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
  • Treasuredata: TD does not publish a fixed request-per-second cap; query throughput is bounded by the account's compute resource pool and large reads/exports run as asynchronous jobs.
What ships with Databricks ⇄ Treasuredata

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks or Treasuredata 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 Treasuredata record.

Observability

Monitoring

Track your Databricks ⇄ Treasuredata 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 Treasuredata.

How the Databricks and Treasuredata 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

Treasuredata

Integration surface
TD API v3 (REST) for databases, tables, and jobs, plus the Audience API (REST) for CDP segments and journeys
Authentication
API key sent as an `Authorization: TD1 <api_key>` header (per-user or account key from the TD Console); requests go to the region-specific endpoint (e.g. api.treasuredata.com for US, with separate EU and Tokyo endpoints)
Change detection
Polling on the mandatory `time` column (Unix-epoch partition key) or an updated-at column; TD stores append-oriented columnar data with no per-row CDC stream, so incremental syncs query for rows past a stored watermark
Capabilities
read · write
Rate limits
TD does not publish a fixed request-per-second cap; query throughput is bounded by the account's compute resource pool and large reads/exports run as asynchronous jobs
How it works

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

    Choose tables

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

Databricks and Treasuredata 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 424 integrations available for Databricks and Treasuredata.

Popular · 5 of 424
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