Two-way sync
Changes in Databricks or Google Cloud Spanner instantly reflect in both systems. No stale data, no manual imports.
Keep Databricks and Google Cloud Spanner in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
Operational databases and analytical warehouses want the same data at different moments. Analysts want Google Cloud Spanner'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 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 Databricks in real time, and result tables in Databricks sync back into Google Cloud Spanner, with schema and type mapping between the two systems handled for you.
Rows from Google Cloud Spanner land in Databricks as they change, replacing hand-built CDC and batch extract jobs.
Aggregates or model outputs computed in Databricks sync into Google Cloud Spanner, where whatever reads from that database gets them without querying the warehouse.
Because changes stream continuously, analysts query current data instead of waiting for last night's load.
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 | Google Cloud Spanner objects | |
|---|---|---|
| SQL Warehouses The compute endpoint a sync connects to for query execution. | Interleaved tables Child rows physically co-located with parents; synced as related records. | |
| Change Data Feed Row-level change records on Delta tables that drive incremental reads. | Secondary indexes Used to make incremental read queries efficient on non-key columns. | |
| Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. | Change streams Capture inserts, updates, and deletes for log-style change data capture. | |
| Schemas Group tables and views; syncs typically target a dedicated schema per source system. | Views Read-only projections useful for shaping data before it leaves Spanner. | |
| Delta Tables The primary read and write target; operational data lands here as managed or external tables. | Databases Top-level containers that scope schema and sync configuration. | |
| Views Curated read-only projections used as sync sources for downstream tools. | Tables Relational tables mapped one-to-one to sync targets. |
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Google Cloud Spanner connection.
Changes in Databricks or Google Cloud Spanner instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever Databricks or Google Cloud Spanner data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.
Handle millions of events per minute without losing a single Databricks or Google Cloud Spanner record.
Track your Databricks ⇄ Google Cloud Spanner sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between Databricks and Google Cloud Spanner.
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.
Authenticate Databricks and Google Cloud Spanner with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Pick the Databricks and Google Cloud Spanner 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.
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Yes. Stacksync provides a managed, real-time two-way integration between Databricks and Google Cloud Spanner: authenticate both systems, choose the objects to sync (such as Databricks's SQL Warehouses and Change Data Feed), map fields visually, and changes propagate both ways in milliseconds — no code required.
Stacksync pricing is usage-based and starts at $1,000/month, including the managed Databricks and Google Cloud Spanner connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Databricks–Google Cloud Spanner integration in-house.
Yes — Stacksync ships production-grade connectors for both Databricks and Google Cloud Spanner. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
Change detection on Databricks: Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns. On Google Cloud Spanner: Change streams (log-style CDC), or timestamp-based polling queries. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
On the Databricks side: Change Data Feed, Catalogs, Schemas, Delta Tables, plus custom fields where Databricks exposes them. On the Google Cloud Spanner side: Secondary indexes, Change streams, Views, Databases. Stacksync auto-detects both schemas and converts types between the two systems.
Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
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.
Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.
Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.
Securely connects to your systems with:
Every pair below is a real-time, two-way sync. Search all 386 integrations available for Databricks and Google Cloud Spanner.