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Database ⇄ CRM

AWS Aurora MySQL to Xactly integration — real-time, two-way sync

Keep AWS Aurora MySQL 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.

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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect AWS Aurora MySQL and Xactly

Treat Xactly like part of your database: its records live in AWS Aurora MySQL 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 AWS Aurora MySQL, where it can be queried and joined like everything else.

Stacksync mirrors Positions & Titles, Quotas / Targets, Payment Summary (Payable), Products & Customers from Xactly into Tables, Rows, Columns, Primary keys and indexes in AWS Aurora MySQL 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 Replicate Products and Customer/Account master from CRM/ERP into Xactly so crediting rules resolve against consistent reference data.
  • 02 Mirror commission Transactions and Credits into a data warehouse to power rep-facing dashboards and comp-plan analytics without manual exports.
  • 03 Let operations teams edit records in a spreadsheet-style tool with changes written back to Aurora safely.
  • 04 Give backend services read and write access to ERP or billing data by syncing it into Aurora tables the application already queries.

Common sync patterns

Query the CRM like a database

Accounts, contacts, and custom objects from Xactly become tables in AWS Aurora MySQL you can join with application data directly.

Product events onto CRM records

Signup, usage, or lifecycle changes written to AWS Aurora MySQL sync onto the matching records in Xactly, 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 Xactly API, limits, and retries.

What you can sync between AWS Aurora MySQL 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.

AWS Aurora MySQL objects Xactly objects How this pairing syncs
Foreign keys Express relationships that syncs preserve when mapping to related objects elsewhere. 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. Foreign keys is specific to AWS Aurora MySQL and Products & Customers to Xactly — each maps to any object or custom field on the other side.
Stored procedures and triggers Existing database logic keeps firing on rows written by a sync. 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. Stored procedures and triggers is specific to AWS Aurora MySQL and Orders to Xactly — each maps to any object or custom field on the other side.
Databases (schemas) Logical namespaces that scope which tables a sync connection can see. Credits Crediting records that tie an order to a participant and position; system-calculated credits are read, while manual and adjustment credits are loaded through Connect, so read and write. Databases (schemas) is specific to AWS Aurora MySQL and Credits to Xactly — each maps to any object or custom field on the other side.
Tables The primary sync unit; each table maps one-to-one to a table or object in the paired system. Transactions (Commission & Bonus) Calculated commission and bonus line items produced by Incent's calculation engine; read as the output of comp runs for reporting and downstream payout, so effectively read-only results. Tables is specific to AWS Aurora MySQL and Transactions (Commission & Bonus) to Xactly — each maps to any object or custom field on the other side.
Rows Inserted, updated, and deleted individually or in bulk during two-way syncs. 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. Rows is specific to AWS Aurora MySQL and Participants (Payees) to Xactly — each maps to any object or custom field on the other side.
Columns MySQL data types are mapped to the paired system's field types during schema setup. 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. Columns is specific to AWS Aurora MySQL and Positions & Titles to Xactly — each maps to any object or custom field on the other side.

How changes propagate between AWS Aurora MySQL 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.

AWS Aurora MySQL Xactly Sub-second propagation

DetectionChanges in AWS Aurora MySQL are captured at the source via change data capture — no polling loop against its API. Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns as a fallback.

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

Xactly AWS Aurora MySQL 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 AWS Aurora MySQL as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • 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 AWS Aurora MySQL ⇄ Xactly

Connect AWS Aurora MySQL and Xactly for flexible, real-time data sync.

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever AWS Aurora MySQL 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 AWS Aurora MySQL or Xactly record.

Observability

Monitoring

Track your AWS Aurora MySQL ⇄ Xactly sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between AWS Aurora MySQL and Xactly.

How the AWS Aurora MySQL and Xactly connectors work

AWS Aurora MySQL

Integration surface
SQL wire protocol (MySQL-compatible), standard MySQL drivers and JDBC
Authentication
Database credentials, optionally AWS IAM database authentication, over TLS
Change detection
Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns as a fallback
Capabilities
read · write · CDC

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 AWS Aurora MySQL 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 AWS Aurora MySQL 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
    AWS Aurora MySQL connected
    Xactly connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the AWS Aurora MySQL 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 · AWS Aurora MySQL ⇄ 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
    AWS Aurora MySQL Xactly
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

AWS Aurora MySQL 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.

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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 473 integrations available for AWS Aurora MySQL and Xactly.

Popular · 7 of 473
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