Real-time
Two-way sync
Changes in Jdbc or Kommo instantly reflect in both systems. No stale data, no manual imports.
Database ⇄ CRM
Jdbc to Kommo integration — real-time, two-way sync
Keep Jdbc and Kommo 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
Case study
Migrated from MuleSoft
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Migrated from Celigo
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Migrated from Matillion
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Migrated from Fivetran
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Migrated from Celigo
Why teams connect Jdbc and Kommo
Treat Kommo like part of your database: its records live in Jdbc as real tables, and writes in either place sync to the other in seconds.
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 Pipelines & Statuses, Tasks, Notes, Custom Fields from Kommo into Views, Columns, Primary keys & indexes, Schemas & catalogs in Jdbc with real-time, bi-directional sync. Read CRM records with plain queries; write updates from your application and they appear in Kommo 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.
Common use cases
- 01 Mirror pipeline stage changes into BI tools for forecasting
- 02 Route leads that originate in WhatsApp or Instagram conversations into other systems of record
- 03 Incrementally sync a high-volume table by polling an updated_at or auto-increment column, keeping a downstream store fresh without full reloads.
- 04 Consolidate several databases from different vendors into one relational store by syncing each through its own JDBC driver.
Common sync patterns
Query the CRM like a database
Accounts, contacts, and custom objects from Kommo become tables in Jdbc you can join with application data directly.
Product events onto CRM records
Signup, usage, or lifecycle changes written to Jdbc sync onto the matching records in Kommo, giving go-to-market teams live product context.
Internal tools without API code
Back-office apps read and write the synced tables; Stacksync handles the Kommo API, limits, and retries.
What you can sync between Jdbc and Kommo
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.
| Jdbc objects | Kommo objects | How this pairing syncs | |
|---|---|---|---|
| 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. | Tasks Follow-up records keep rep activity consistent across systems. | Tables is specific to Jdbc and Tasks to Kommo — each maps to any object or custom field on the other side. | |
| Views Stored SELECT queries exposed like tables; read-only projections synced outbound when raw base tables should not be exposed downstream. | Notes Free-text and system notes attach context to synced leads and contacts. | Views is specific to Jdbc and Notes to Kommo — each maps to any object or custom field on the other side. | |
| Columns Per-table fields with data types, nullability, and defaults; enumerated via DatabaseMetaData.getColumns to auto-generate and type-check field mappings. | Custom Fields Per-entity custom fields hold data written from external databases and enrichment. | Columns is specific to Jdbc and Custom Fields to Kommo — each maps to any object or custom field on the other side. | |
| 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. | Users Account users map lead ownership to people in other systems. | Primary keys & indexes is specific to Jdbc and Users to Kommo — each maps to any object or custom field on the other side. | |
| 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. | Chats Messenger conversations from WhatsApp, Instagram, and other channels attach to leads and contacts. | Schemas & catalogs is specific to Jdbc and Chats to Kommo — each maps to any object or custom field on the other side. | |
| 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. | Leads The central pipeline record; external signups and form fills sync in as leads. | Stored procedures & functions is specific to Jdbc and Leads to Kommo — each maps to any object or custom field on the other side. |
How changes propagate between Jdbc and Kommo
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.
Jdbc Kommo Interval-based propagation
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 Kommo through its API, with automatic retries and rate-limit backoff.
Kommo Jdbc Sub-second propagation
DetectionKommo notifies Stacksync of record changes through webhook events. Webhooks on record add and update events, plus polling for backfill.
DeliveryEach detected change is applied to Jdbc as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- Jdbc: No SaaS-style request quota. Throughput is bounded by the target database's max connections and connection-pool size, plus the CPU and I/O it shares with production queries, so heavy syncs can contend with live workloads.
- Kommo: Subject to per-account request rate limits.
What ships with Jdbc ⇄ Kommo
Connect Jdbc and Kommo for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Jdbc–Kommo connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Jdbc or Kommo 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 Jdbc or Kommo record.
Observability
Monitoring
Track your Jdbc ⇄ Kommo sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Jdbc and Kommo.
How the Jdbc and Kommo connectors work
Jdbc
- Integration surface
- JDBC API (java.sql / javax.sql) executing SQL through a JDBC driver, typically a pure-Java Type 4 driver; reaches any relational database with a driver - PostgreSQL, MySQL, SQL Server, Oracle, IBM DB2, and others - via a JDBC URL such as jdbc:postgresql://host:5432/db.
- Authentication
- A database user's username and password supplied in the JDBC connection (DriverManager or a DataSource), typically over a TLS/SSL-encrypted connection. Some drivers add Kerberos, integrated Windows auth, or cloud IAM-token auth, but the available methods depend on the target database and its driver.
- Change detection
- 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.
- Capabilities
- read · write
- Rate limits
- No SaaS-style request quota. Throughput is bounded by the target database's max connections and connection-pool size, plus the CPU and I/O it shares with production queries, so heavy syncs can contend with live workloads.
Kommo
- Integration surface
- REST API
- Authentication
- OAuth 2.0 with refresh tokens
- Change detection
- Webhooks on record add and update events, plus polling for backfill
- Capabilities
- read · write · webhooks
- Rate limits
- Subject to per-account request rate limits
How it works
How to connect Jdbc to Kommo — 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.
- 01
Connect your apps
Authenticate Jdbc and Kommo with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Jdbc connected
Kommo
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Jdbc and Kommo 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.
objects · Jdbc ⇄ KommoCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Jdbc KommoCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Jdbc and Kommo integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Jdbc and Kommo: authenticate both systems, choose the objects to sync (such as Jdbc's Tables and Views), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Stacksync is SOC 2 Type II and ISO 27001 certified with HIPAA BAA support. Data is encrypted in transit, and a zero-persistent-storage architecture means Jdbc and Kommo records are not retained after a sync operation.
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Stacksync pricing is usage-based and starts at $1,000/month, including the managed Jdbc and Kommo connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Jdbc–Kommo integration in-house.
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Yes — Stacksync ships production-grade connectors for both Jdbc and Kommo. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection 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. On Kommo: Webhooks on record add and update events, plus polling for backfill. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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On the Kommo side: Pipelines & Statuses, Tasks, Notes, Custom Fields, plus custom fields where Kommo exposes them. On the Jdbc side: Views, Columns, Primary keys & indexes, Schemas & catalogs. Stacksync auto-detects both schemas and converts types between the two systems.
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.
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
CSA STAR
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:
Related integrations
Every pair below is a real-time, two-way sync. Search all 443 integrations available for Jdbc and Kommo.
Popular · 6 of 443
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Jdbc ⇄ Outreach -
Jdbc ⇄ Salesloft -
Kommo ⇄ RavenDB -
Kommo ⇄ Redis Enterprise -
Kommo ⇄ SAP ASE (Sybase) -
Kommo ⇄ Scaleway Postgres
No integrations match .
