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Data warehouse ⇄ CRM

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

Keep Databricks 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 Databricks and Xactly

Sync Xactly into Databricks continuously and push warehouse results back onto CRM records, one two-way connection instead of two pipelines.

The CRM feeds the warehouse and the warehouse should feed the CRM: relationship data flows one way, and computed scores, segments, and customer context flow back. Most teams build the first half as a batch pipeline and never quite get to the second.

Stacksync does both with one connection. Products & Customers, Orders, Credits, Transactions (Commission & Bonus) from Xactly land in Databricks as live tables, updated within seconds, and columns computed in Databricks write back to fields in Xactly. There is no separate ETL and reverse-ETL stack to stitch together and no jobs to babysit.

Common use cases

  • 01 Mirror commission Transactions and Credits into a data warehouse to power rep-facing dashboards and comp-plan analytics without manual exports.
  • 02 Sync Participants, Positions, Titles, and Quotas from an HRIS/HCM and planning tools into Xactly so the org hierarchy and targets stay aligned with headcount changes.
  • 03 Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.
  • 04 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.

Common sync patterns

Scores and segments back on the record

Lead scores, churn risk, or usage segments computed in Databricks appear as fields in Xactly, where the people working accounts actually see them.

A single customer view

Join Xactly's relationship data with billing, product, and support data in Databricks to build the customer picture the CRM alone cannot hold.

Cleanup that sticks

Deduplication and normalization done in Databricks can be written back, so warehouse-side cleanup actually fixes the CRM.

What you can sync between Databricks 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.

Databricks objects Xactly objects How this pairing syncs
Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. 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. Materialized Views is specific to Databricks and Transactions (Commission & Bonus) to Xactly — each maps to any object or custom field on the other side.
Volumes Unity Catalog file storage used for staging bulk loads. 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. Volumes is specific to Databricks and Participants (Payees) to Xactly — each maps to any object or custom field on the other side.
SQL Warehouses The compute endpoint a sync connects to for query execution. 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. SQL Warehouses is specific to Databricks and Positions & Titles to Xactly — each maps to any object or custom field on the other side.
Change Data Feed Row-level change records on Delta tables that drive incremental reads. 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. Change Data Feed is specific to Databricks and Quotas / Targets to Xactly — each maps to any object or custom field on the other side.
Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. Payment Summary (Payable) Approved payable amounts per participant per period; read as the output that feeds payroll and accounts payable, so effectively read-only. Catalogs is specific to Databricks and Payment Summary (Payable) to Xactly — each maps to any object or custom field on the other side.
Schemas Group tables and views; syncs typically target a dedicated schema per source system. 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. Schemas is specific to Databricks and Products & Customers to Xactly — each maps to any object or custom field on the other side.

How changes propagate between Databricks 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.

Databricks Xactly Sub-second propagation

DetectionChanges in Databricks are captured at the source via change data capture — no polling loop against its API. Delta Lake Change Data Feed for row-level changes.

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

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

Rate-limit considerations

  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
  • 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 Databricks ⇄ Xactly

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks 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 Databricks or Xactly record.

Observability

Monitoring

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

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Databricks and Xactly.

How the Databricks and Xactly connectors work

Databricks

Integration surface
SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution
Authentication
Personal access tokens or OAuth machine-to-machine credentials for service principals
Change detection
Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns
Capabilities
read · write · CDC
Rate limits
Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits

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 Databricks 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 Databricks 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
    Databricks connected
    Xactly connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

Databricks 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 573 integrations available for Databricks and Xactly.

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