Skip to content
Database ⇄ CRM

Jdbc to Xactly integration — real-time, two-way sync

Keep Jdbc and Xactly 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
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Jdbc and Xactly

Treat Xactly 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 Transactions (Commission & Bonus), Participants (Payees), Positions & Titles, Quotas / Targets from Xactly into Columns, Primary keys & indexes, Schemas & catalogs, Stored procedures & functions in Jdbc with real-time, bi-directional sync. Read CRM records with plain queries; write updates from your application and they appear in Xactly 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 Push approved Payment Summary amounts from Incent into a payroll or accounts-payable system so payouts are paid from the mastered comp result.
  • 02 Replicate Products and Customer/Account master from CRM/ERP into Xactly so crediting rules resolve against consistent reference data.
  • 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

Trigger workflows from CRM changes

Field and stage updates in Xactly arrive as row changes in Jdbc, ready to drive jobs and notifications.

Query the CRM like a database

Accounts, contacts, and custom objects from Xactly 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 Xactly, giving go-to-market teams live product context.

What you can sync between Jdbc and Xactly

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 Xactly objects How this pairing syncs
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. Participants (Payees) Sales reps and payees keyed to positions; loaded and updated from HRIS/HCM source data through Connect (write) and read for roster reporting, so read and write. Stored procedures & functions is specific to Jdbc and Participants (Payees) to Xactly — each maps to any object or custom field on the other side.
Sequences Server-generated identity values; relevant when writing rows into tables whose keys are assigned by the database rather than the source system. Positions & Titles Org-hierarchy positions and titles that credits and quotas roll up to; loaded and maintained through Connect (write) and read to resolve the hierarchy, so read and write. Sequences is specific to Jdbc and Positions & Titles to Xactly — each maps to any object or custom field on the other side.
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. Quotas / Targets Period quota and target values per position or plan; loaded from planning tools through Connect (write) and read for attainment reporting, so read and write. Tables is specific to Jdbc and Quotas / Targets to Xactly — 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. Payment Summary (Payable) Approved payable amounts per participant per period; read as the output that feeds payroll and accounts payable, so effectively read-only. Views is specific to Jdbc and Payment Summary (Payable) to Xactly — 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. Products & Customers Product and customer/account master used in crediting rules and reporting; loaded and updated from CRM/ERP through Connect (write) and read for lookups, so read and write. Columns is specific to Jdbc and Products & Customers to Xactly — 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. Orders Sales order and transaction records loaded into Incent as the raw input for crediting and calculation; created and updated through Connect load and ETL steps (write) and read back for reconciliation, so read and write. Primary keys & indexes is specific to Jdbc and Orders to Xactly — each maps to any object or custom field on the other side.

How changes propagate between Jdbc and Xactly

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 Xactly 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 applied to Xactly as a row-level write, with types converted between the two schemas.

Xactly Jdbc Interval-based propagation

DetectionStacksync polls Xactly for changes on an incremental schedule, reading only records changed since the previous pass. No CDC log for external tools to consume.

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.
  • Xactly: Xactly does not publish numeric per-second rate limits. The Connect platform enforces query concurrency and long-running-query limits, so large extracts are paged and heavy loads/queries run as asynchronous Connect jobs (submit, then retrieve results) rather than row-by-row calls; batch loads are the supported path for high volume.
What ships with Jdbc ⇄ Xactly

Connect Jdbc and Xactly for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Jdbc–Xactly connection.

Real-time

Two-way sync

Changes in Jdbc or Xactly instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Jdbc or Xactly 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 Xactly record.

Observability

Monitoring

Track your Jdbc ⇄ Xactly 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 Xactly.

How the Jdbc and Xactly 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.

Xactly

Integration surface
Xactly Connect REST API v2 (JSON), plus ODBC/JDBC drivers over the same ANSI-SQL data model. Incent data is exposed as SQL-queryable objects (for example xactly_order, xactly_credit, xactly_transaction, xactly_payment); data is loaded and extracted through Connect load/query steps and server-side ETL Pipelines. The base host is region/pod-specific (for example https://<pod>.xactlycorp.com).
Authentication
OAuth 2.0 via the Xactly Connect API Gateway (bearer tokens issued on behalf of an Xactly Incent user), with HTTP Basic authentication using a dedicated Xactly Connect service-user's credentials also supported for the Connect REST API v2. The connecting user needs Connect/API access plus the relevant object permissions in Incent.
Change detection
No CDC log for external tools to consume. Incremental sync uses SQL predicates on modified/last-updated timestamp columns (for example WHERE modified_date > watermark) against Connect's queryable objects, or scheduled Connect ETL Pipelines that pull deltas since the last run. Xactly Connect has no outbound HTTP webhooks, so change detection is pull/ETL-based.
Capabilities
read · write
Rate limits
Xactly does not publish numeric per-second rate limits. The Connect platform enforces query concurrency and long-running-query limits, so large extracts are paged and heavy loads/queries run as asynchronous Connect jobs (submit, then retrieve results) rather than row-by-row calls; batch loads are the supported path for high volume.
Xactly setup guide
How it works

How to connect Jdbc to Xactly — 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 Jdbc and Xactly 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
    Jdbc connected
    Xactly connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Jdbc and Xactly 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 · Jdbc ⇄ Xactly
    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
    Jdbc Xactly
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
Try interactive demo
FAQ

Jdbc and Xactly 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
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:

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 454 integrations available for Jdbc and Xactly.

Popular · 4 of 454
All Jdbc integrations All Xactly integrations Browse all integrations
Coworkers laughing in front of a laptop in a casual office setting

Your last integration took months.
Your next one takes a prompt.

Book a demo Tour the platform on your own