Two-way sync
Changes in Attio or Jdbc instantly reflect in both systems. No stale data, no manual imports.
Keep Attio and Jdbc in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
Product and engineering teams constantly need CRM data, and the CRM API is a poor way to get it: rate limits, pagination, custom objects, and integration code that breaks when an admin renames a field. What they actually want is the data in Jdbc, where it can be queried and joined like everything else.
Stacksync mirrors People, Companies, Users, Deals from Attio into Schemas & catalogs, Stored procedures & functions, Sequences, Tables in Jdbc with real-time, bi-directional sync. Read CRM records with plain queries; write updates from your application and they appear in Attio with validation intact. Go-to-market teams keep working in the CRM, engineers keep working in the database, and neither has to think about the other.
Back-office apps read and write the synced tables; Stacksync handles the Attio API, limits, and retries.
Field and stage updates in Attio arrive as row changes in Jdbc, ready to drive jobs and notifications.
Accounts, contacts, and custom objects from Attio become tables in Jdbc you can join with application data directly.
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.
| Attio objects | Jdbc objects | How this pairing syncs | |
|---|---|---|---|
| Deals Pipeline records; read out for revenue reporting and written to from automation. | Tables The base relational tables in the target database; synced two-way as rows over SQL, with each table's primary key driving upserts and row-level updates. | Deals is specific to Attio and Tables to Jdbc — each maps to any object or custom field on the other side. | |
| Workspaces Synced with incremental and full sync per the Stacksync docs. | Views Stored SELECT queries exposed like tables; read-only projections synced outbound when raw base tables should not be exposed downstream. | Workspaces is specific to Attio and Views to Jdbc — each maps to any object or custom field on the other side. | |
| Custom objects Workspace-defined objects that behave like standard ones in the API. | Columns Per-table fields with data types, nullability, and defaults; enumerated via DatabaseMetaData.getColumns to auto-generate and type-check field mappings. | Custom objects is specific to Attio and Columns to Jdbc — each maps to any object or custom field on the other side. | |
| People Standard person object; synced with marketing tools and warehouse person tables. | Primary keys & indexes Key and index definitions read via DatabaseMetaData; the primary key is required for reliable upserts, and indexes on the cursor column keep incremental polling fast. | People is specific to Attio and Primary keys & indexes to Jdbc — each maps to any object or custom field on the other side. | |
| Companies Standard company object; matched to billing and product accounts in two-way syncs. | Schemas & catalogs Namespaces that group tables and views; the connector targets a schema/catalog and lists its objects from the JDBC metadata to build the sync. | Companies is specific to Attio and Schemas & catalogs to Jdbc — each maps to any object or custom field on the other side. | |
| Users Synced with incremental and full sync per the Stacksync docs. | Stored procedures & functions Server-side routines callable via JDBC CallableStatement; invoked for custom read or write logic when a table-level mapping is not enough. | Users is specific to Attio and Stored procedures & functions to Jdbc — each maps to any object or custom field on the other side. |
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.
DetectionAttio notifies Stacksync of record changes through webhook events. Webhooks on record and list-entry events, with polling as a fallback.
DeliveryEach detected change is applied to Jdbc as a row-level write, with types converted between the two schemas.
DetectionStacksync polls Jdbc for changes on an incremental schedule, reading only records changed since the previous pass. No native change feed.
DeliveryEach detected change is written to Attio through its API, with automatic retries and rate-limit backoff.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Attio–Jdbc connection.
Changes in Attio or Jdbc instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever Attio or Jdbc data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.
Handle millions of events per minute without losing a single Attio or Jdbc record.
Track your Attio ⇄ Jdbc sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between Attio and Jdbc.
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 Attio and Jdbc 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 Attio and Jdbc 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 Attio and Jdbc: authenticate both systems, choose the objects to sync (such as Attio's Deals and Workspaces), map fields visually, and changes propagate both ways in milliseconds — no code required.
Yes — Stacksync ships production-grade connectors for both Attio and Jdbc. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
Change detection on Attio: Webhooks on record and list-entry events, with polling as a fallback. On Jdbc: No native change feed. Incremental sync polls a cursor column - an updated_at timestamp or an auto-incrementing key - to pull new and changed rows; detecting deletes needs soft-delete flags or database triggers writing to a shadow table. No webhooks. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
On the Attio side: People, Companies, Users, Deals, plus custom fields where Attio exposes them. On the Jdbc side: Schemas & catalogs, Stored procedures & functions, Sequences, Tables. 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.
Common patterns for Attio and Jdbc: Internal tools without API code; Trigger workflows from CRM changes; Query the CRM like a database. Back-office apps read and write the synced tables; Stacksync handles the Attio API, limits, and retries.
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 455 integrations available for Attio and Jdbc.