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Human resources ⇄ Data warehouse

Ceridian Dayforce to Databricks integration — real-time, two-way sync

Keep Ceridian Dayforce and Databricks 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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Why teams connect Ceridian Dayforce and Databricks

Land the people and organization records from Ceridian Dayforce in Databricks as live tables for workforce reporting, without extract jobs, and write computed results back where Ceridian Dayforce can use them.

Workforce data is some of the most requested data in the warehouse and some of the most awkward to move: the record types are many, the fields carry sensitive personal information, the APIs are strict, and hand-built extract jobs go stale or break quietly. Whether Ceridian Dayforce is the system of record for employees and payroll, for candidates and applications, or for learners and course completions, the reporting belongs in Databricks next to everything else the company measures.

Stacksync syncs Employee Punches & Raw Clock Entries, Employee Schedules, Org Units, Departments, Jobs & Positions, Time Away From Work & Balances from Ceridian Dayforce into tables in Databricks continuously, handling API limits and schema drift as they come. The connection is bi-directional, so values computed in Databricks, such as headcount rollups, cost allocations, or attrition risk flags, can be written back to fields in Ceridian Dayforce where the HR team acts on them. You configure which records and fields cross over, and in which direction, instead of maintaining pipeline code.

Common use cases

  • 01 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
  • 02 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
  • 03 Replicate Compensation Summary and pay data into a warehouse for headcount, labor-cost, and compensation reporting without manual BI exports.
  • 04 Sync time data by posting raw clock entries from a third-party scheduling tool into Dayforce, or pulling Employee Punches and Schedules into a workforce-analytics database.

Common sync patterns

Fresh data instead of last night's load

Because changes stream continuously, reports query current workforce data rather than waiting for an overnight load window to finish.

Queryable history for planning and audit

A continuously synced copy in Databricks gives you a durable, queryable record of how Ceridian Dayforce's records change over time, for headcount planning and audit questions.

Write-back of computed values

Segments, rollups, or risk flags computed in Databricks sync back onto the matching records in Ceridian Dayforce, where the HR team sees them in the system they already use.

What you can sync between Ceridian Dayforce and Databricks

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.

Ceridian Dayforce objects Databricks objects How this pairing syncs
Employment Status Effective-dated employment status and type (active, leave, terminated) that drives active/terminated state for downstream provisioning; read, and writable as new dated status records. Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. Employment Status is specific to Ceridian Dayforce and Catalogs to Databricks — each maps to any object or custom field on the other side.
Work Assignments Effective-dated position, department, location, and org-unit assignment; the most-synced record for org charts, provisioning, and analytics. Read and updatable via PATCH. Schemas Group tables and views; syncs typically target a dedicated schema per source system. Work Assignments is specific to Ceridian Dayforce and Schemas to Databricks — each maps to any object or custom field on the other side.
Compensation Summary & Pay Adjustments Compensation Summary returns pay rate and frequency for reporting; Employee Pay Adjustments are written as effective-dated records via PATCH to post pay changes back to Dayforce. Delta Tables The primary read and write target; operational data lands here as managed or external tables. Compensation Summary & Pay Adjustments is specific to Ceridian Dayforce and Delta Tables to Databricks — each maps to any object or custom field on the other side.
Employee Punches & Raw Clock Entries Time-and-attendance punches; raw clock entries are added with POST and punches removed with DELETE, so time data flows both ways between Dayforce and scheduling or workforce tools. Views Curated read-only projections used as sync sources for downstream tools. Employee Punches & Raw Clock Entries is specific to Ceridian Dayforce and Views to Databricks — each maps to any object or custom field on the other side.
Employee Schedules Shift and schedule assignments; created and updated through POST and PATCH schedule endpoints and paged with pageSize, used to push schedules from a WFM tool or pull them for analytics. Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. Employee Schedules is specific to Ceridian Dayforce and Materialized Views to Databricks — each maps to any object or custom field on the other side.
Org Units, Departments, Jobs & Positions Configuration reference data; POST creates org units and departments and PATCH updates job, position, department, and org-unit data. Used to resolve and validate XRefCodes when mapping employee writes. Volumes Unity Catalog file storage used for staging bulk loads. Org Units, Departments, Jobs & Positions is specific to Ceridian Dayforce and Volumes to Databricks — each maps to any object or custom field on the other side.

How changes propagate between Ceridian Dayforce and Databricks

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.

Ceridian Dayforce Databricks Sub-second propagation

DetectionCeridian Dayforce notifies Stacksync of record changes through webhook events. No CDC stream.

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

Databricks Ceridian Dayforce 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 written to Ceridian Dayforce through its API, with automatic retries and rate-limit backoff.

Rate-limit considerations

  • Ceridian Dayforce: Dayforce does not publish numeric rate limits; thresholds vary by tenant and exceeding them returns HTTP 429, so large employee reads are paged (pageSize) and throttled with backoff. Employee list calls return XRefCodes only, so each employee needs a follow-up GET.
  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
What ships with Ceridian Dayforce ⇄ Databricks

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Ceridian Dayforce or Databricks 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 Ceridian Dayforce or Databricks record.

Observability

Monitoring

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

Trading partners

EDI

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

How the Ceridian Dayforce and Databricks connectors work

Ceridian Dayforce

Integration surface
REST API (Dayforce RESTful Web Services, v1/v2), JSON payloads
Authentication
HTTP Basic authentication with a dedicated Web Services user plus the client namespace (Company ID), or an OAuth 2.0 access token from the Dayforce identity endpoint (POST dfid.dayforcehcm.com/connect/token, client_id Dayforce.HCMAnywhere.Client). Token-based auth is recommended, and the Web Services role with the Read Data subfeature must be enabled on the account.
Change detection
No CDC stream. Broad change detection uses delta polling on GET Employees with filterUpdatedStartDate/filterUpdatedEndDate and filterUpdatedEntities to pull only records changed in a window. Dayforce also has native Event Notifications (the Notification Publisher) for lifecycle events — hire, rehire, terminate, and workflow validation — delivered by push to a receiver service or by querying unacknowledged notifications; the publisher runs roughly hourly and each notification must be acknowledged.
Capabilities
read · write · webhooks
Rate limits
Dayforce does not publish numeric rate limits; thresholds vary by tenant and exceeding them returns HTTP 429, so large employee reads are paged (pageSize) and throttled with backoff. Employee list calls return XRefCodes only, so each employee needs a follow-up GET.
Ceridian Dayforce setup guide

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
How it works

How to connect Ceridian Dayforce to Databricks — 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 Ceridian Dayforce and Databricks 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
    Ceridian Dayforce connected
    Databricks connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

Ceridian Dayforce and Databricks integration FAQ

SECURITY

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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

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