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Data warehouse ⇄ Analytics

Google Cloud Platform to Treasuredata integration — real-time, two-way sync

Keep Google Cloud Platform 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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Why teams connect Google Cloud Platform and Treasuredata

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

Treasuredata is where teams explore, visualize, and report; Google Cloud Platform 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 Master (Parent) Segments, Segments, Journeys, Predictive Segments in Treasuredata with BigQuery datasets, BigQuery tables, Cloud SQL databases, Cloud Storage objects in Google Cloud Platform 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 Google Cloud Platform, 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 Publish change events to Pub/Sub so downstream services react to record updates as they happen.

Common sync patterns

Shared user and account keys

Users and accounts tracked in Treasuredata line up with the customer or user rows in Google Cloud Platform 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.

One number both sides agree on

Metrics and aggregates stay aligned between the two systems, so a figure shown in Treasuredata matches the Google Cloud Platform table it was built from instead of drifting between refreshes.

What you can sync between Google Cloud Platform 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.

Google Cloud Platform objects Treasuredata objects How this pairing syncs
Firestore documents Document data read and written through the Firestore API for app-facing syncs. 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. Firestore documents is specific to Google Cloud Platform and Tables to Treasuredata — each maps to any object or custom field on the other side.
Spanner tables Strongly consistent relational tables accessed via SQL for transactional workloads. 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. Spanner tables is specific to Google Cloud Platform and Master (Parent) Segments to Treasuredata — each maps to any object or custom field on the other side.
BigQuery datasets Namespaces that group tables; syncs target tables within a dataset. Segments Campaign subsets of a parent segment; membership read out to activate audiences in downstream systems, or audience flags written back onto records. BigQuery datasets is specific to Google Cloud Platform and Segments to Treasuredata — each maps to any object or custom field on the other side.
BigQuery tables The primary analytics destination, written through load jobs or the Storage Write API and queried with SQL. Journeys Timeline-based event sequences in Audience Studio; stage and membership read out for reporting and cross-system activation. BigQuery tables is specific to Google Cloud Platform and Journeys to Treasuredata — each maps to any object or custom field on the other side.
Cloud SQL databases Managed Postgres, MySQL, and SQL Server instances synced like ordinary relational databases. Predictive Segments AI/ML-scored segments; propensity scores read out and written onto customer records in a CRM or database for prioritization. Cloud SQL databases is specific to Google Cloud Platform and Predictive Segments to Treasuredata — each maps to any object or custom field on the other side.
Cloud Storage objects Staging area for file-based bulk loads into BigQuery and other services. Scheduled Queries Cron-scheduled Presto/Trino (or Hive) jobs that materialize results into result tables; Stacksync reads those materialized tables downstream. Cloud Storage objects is specific to Google Cloud Platform and Scheduled Queries to Treasuredata — each maps to any object or custom field on the other side.

How changes propagate between Google Cloud Platform 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.

Google Cloud Platform Treasuredata Sub-second propagation

DetectionGoogle Cloud Platform pushes changes as they happen — webhook events backed by change data capture. Varies by service: log-based CDC on Cloud SQL (logical replication or binlog, also via Datastream), Pub/Sub for event delivery, polling for BigQuery.

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

Treasuredata Google Cloud Platform 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 Google Cloud Platform as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Google Cloud Platform: Quotas are set per service and per project; BigQuery, Pub/Sub, and Cloud SQL each enforce their own 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 Google Cloud Platform ⇄ Treasuredata

Connect Google Cloud Platform and Treasuredata for flexible, real-time data sync.

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Google Cloud Platform 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 Google Cloud Platform or Treasuredata record.

Observability

Monitoring

Track your Google Cloud Platform ⇄ Treasuredata sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Google Cloud Platform and Treasuredata.

How the Google Cloud Platform and Treasuredata connectors work

Google Cloud Platform

Integration surface
Per-service REST and gRPC APIs; BigQuery speaks SQL and Cloud SQL exposes standard database wire protocols
Authentication
IAM service accounts with OAuth 2.0 tokens
Change detection
Varies by service: log-based CDC on Cloud SQL (logical replication or binlog, also via Datastream), Pub/Sub for event delivery, polling for BigQuery tables
Capabilities
read · write · CDC · webhooks
Rate limits
Quotas are set per service and per project; BigQuery, Pub/Sub, and Cloud SQL each enforce their own 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 Google Cloud Platform 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 Google Cloud Platform 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
    Google Cloud Platform connected
    Treasuredata connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Google Cloud Platform 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 · Google Cloud Platform ⇄ 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
    Google Cloud Platform Treasuredata
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Google Cloud Platform 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
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ISO 27001
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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 413 integrations available for Google Cloud Platform and Treasuredata.

Popular · 4 of 413
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