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Database ⇄ Data warehouse

Google Cloud Spanner to Yellowbrick integration — real-time, two-way sync

Keep Google Cloud Spanner and Yellowbrick 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

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Why teams connect Google Cloud Spanner and Yellowbrick

Connect Google Cloud Spanner and Yellowbrick 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 Google Cloud Spanner's rows in Yellowbrick, 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 Google Cloud Spanner where the services that read from it get them at normal query latency.

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

Common use cases

  • 01 Sync CRM and marketing data into Yellowbrick tables so it can be joined with large fact tables for enterprise BI.
  • 02 Land ERP transactional extracts in Yellowbrick for finance and supply-chain analytics.
  • 03 Push billing or entitlement changes from finance tools into Spanner tables the application reads at runtime.
  • 04 Use change streams to feed near-real-time copies of operational tables into an analytics warehouse.

Common sync patterns

Offload heavy reads

Point analytical queries at the synced copy in Yellowbrick and keep Google Cloud Spanner focused on its operational workload.

Operational data in the warehouse, minus the pipeline

Rows from Google Cloud Spanner land in Yellowbrick as they change, replacing hand-built CDC and batch extract jobs.

Serve warehouse results at database speed

Aggregates or model outputs computed in Yellowbrick sync into Google Cloud Spanner, where whatever reads from that database gets them without querying the warehouse.

What you can sync between Google Cloud Spanner and Yellowbrick

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 Spanner objects Yellowbrick objects How this pairing syncs
Databases Top-level containers that scope schema and sync configuration. Databases Top-level containers for schemas and tables. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Tables Relational tables mapped one-to-one to sync targets. Tables Columnar MPP tables; the primary targets for warehouse syncs. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Views Read-only projections useful for shaping data before it leaves Spanner. Views Logical views used to shape reads for BI and downstream syncs. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Interleaved tables Child rows physically co-located with parents; synced as related records. Schemas Namespaces used to organize synced datasets by source or domain. Interleaved tables is specific to Google Cloud Spanner and Schemas to Yellowbrick — each maps to any object or custom field on the other side.
Secondary indexes Used to make incremental read queries efficient on non-key columns. Users and Roles Access-control objects that govern what a sync service account can read and write. Secondary indexes is specific to Google Cloud Spanner and Users and Roles to Yellowbrick — each maps to any object or custom field on the other side.

How changes propagate between Google Cloud Spanner and Yellowbrick

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 Spanner Yellowbrick Sub-second propagation

DetectionChanges in Google Cloud Spanner are captured at the source via change data capture — no polling loop against its API. Change streams (log-style CDC), or timestamp-based polling queries.

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

Yellowbrick Google Cloud Spanner Interval-based propagation

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

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

Rate-limit considerations

  • Google Cloud Spanner: Throughput is bounded by the instance's provisioned compute capacity rather than a fixed API quota.
  • Yellowbrick: No API rate limits; throughput depends on cluster sizing, and bulk loads should use ybload rather than row-by-row inserts.
What ships with Google Cloud Spanner ⇄ Yellowbrick

Connect Google Cloud Spanner and Yellowbrick for flexible, real-time data sync.

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Google Cloud Spanner or Yellowbrick 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 Spanner or Yellowbrick record.

Observability

Monitoring

Track your Google Cloud Spanner ⇄ Yellowbrick 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 Spanner and Yellowbrick.

How the Google Cloud Spanner and Yellowbrick connectors work

Google Cloud Spanner

Integration surface
gRPC/REST client API with SQL query surface (GoogleSQL and PostgreSQL-interface dialects)
Authentication
Google Cloud IAM (service accounts)
Change detection
Change streams (log-style CDC), or timestamp-based polling queries
Capabilities
read · write · CDC
Rate limits
Throughput is bounded by the instance's provisioned compute capacity rather than a fixed API quota.

Yellowbrick

Integration surface
SQL wire protocol (PostgreSQL-compatible) with JDBC/ODBC drivers; bulk loading via the ybload utility
Authentication
Database credentials, with LDAP and Kerberos options in enterprise deployments
Change detection
Polling on timestamp columns; no exposed transaction-log CDC
Capabilities
read · write
Rate limits
No API rate limits; throughput depends on cluster sizing, and bulk loads should use ybload rather than row-by-row inserts.
How it works

How to connect Google Cloud Spanner to Yellowbrick — 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 Spanner and Yellowbrick 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 Spanner connected
    Yellowbrick connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Google Cloud Spanner and Yellowbrick 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 Spanner ⇄ Yellowbrick
    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 Spanner Yellowbrick
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Google Cloud Spanner and Yellowbrick 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 354 integrations available for Google Cloud Spanner and Yellowbrick.

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