Real-time
Two-way sync
Changes in Databricks or Namely instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Human resources
Databricks to Namely integration — real-time, two-way sync
Keep Databricks and Namely 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 Namely
Land the people and organization records from Namely in Databricks as live tables for workforce reporting, without extract jobs, and write computed results back where Namely 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 Namely 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 Job Tiers, Groups, Teams, Reports from Namely 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 Namely 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 Provision or deprovision accounts in an IdP or directory when a Profile's user_status or start_date changes, and write the resulting user ID and email back onto the Profile.
- 04 Map Job Titles and Job Tiers into a warehouse dimension table so leveling and seniority stay consistent across HR, finance, and BI reporting.
Common sync patterns
Write-back of computed values
Segments, rollups, or risk flags computed in Databricks sync back onto the matching records in Namely, where the HR team sees them in the system they already use.
HR data in the warehouse, minus the pipeline
People and organization records from Namely arrive in Databricks as queryable tables, current within seconds instead of a day behind, replacing hand-built extract jobs.
Headcount and cost joined with everything else
Analysts combine Namely's workforce records with finance, product, or operational data already in Databricks for reporting the HR system cannot produce on its own.
What you can sync between Databricks and Namely
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 | Namely objects | How this pairing syncs | |
|---|---|---|---|
| Delta Tables The primary read and write target; operational data lands here as managed or external tables. | Job Titles Job title definitions; read and written so titles stay aligned with an HRIS, directory, or reporting dimension. | Delta Tables is specific to Databricks and Job Titles to Namely — each maps to any object or custom field on the other side. | |
| Views Curated read-only projections used as sync sources for downstream tools. | Job Tiers Leveling hierarchy grouping zero-to-many Job Titles; read to map seniority into warehouse dimension tables. | Views is specific to Databricks and Job Tiers to Namely — each maps to any object or custom field on the other side. | |
| Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. | Groups Departments and locations that organize Profiles; synced to keep org structure aligned with a warehouse or IdP. | Materialized Views is specific to Databricks and Groups to Namely — each maps to any object or custom field on the other side. | |
| Volumes Unity Catalog file storage used for staging bulk loads. | Teams Teams and team categories a Profile belongs to; read and written for org-chart and provisioning workflows. | Volumes is specific to Databricks and Teams to Namely — each maps to any object or custom field on the other side. | |
| SQL Warehouses The compute endpoint a sync connects to for query execution. | Reports Saved Namely reports returned as JSON snapshots that update instantly; read-only feeds for headcount and roster analytics. | SQL Warehouses is specific to Databricks and Reports to Namely — 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. | Profile Fields Metadata describing standard and company-defined custom field sections; read to discover schema and generate mappings. | Change Data Feed is specific to Databricks and Profile Fields to Namely — each maps to any object or custom field on the other side. |
How changes propagate between Databricks and Namely
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 Namely 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 Namely through its API, with automatic retries and rate-limit backoff.
Namely Databricks Interval-based propagation
DetectionStacksync polls Namely for changes on an incremental schedule, reading only records changed since the previous pass. No webhooks.
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.
- Namely: Namely's public docs do not publish a fixed request quota; the REST API returns standard HTTP status codes, so integrations should throttle and back off on 429/5xx responses.
What ships with Databricks ⇄ Namely
Connect Databricks and Namely for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Namely connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Databricks or Namely 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 Namely record.
Observability
Monitoring
Track your Databricks ⇄ Namely 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 Namely.
How the Databricks and Namely 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
Namely
- Integration surface
- REST API (JSON over HTTPS)
- Authentication
- OAuth 2.0 authorization-code grant, or a personal access token sent as a Bearer token; all calls run over HTTPS against https://{subdomain}.namely.com/api/v1
- Change detection
- No webhooks; integrations poll GET endpoints and compare the updated_at / created_at timestamps on Profiles and related objects to detect changes
- Capabilities
- read · write
- Rate limits
- Namely's public docs do not publish a fixed request quota; the REST API returns standard HTTP status codes, so integrations should throttle and back off on 429/5xx responses.
How it works
How to connect Databricks to Namely — 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 Databricks and Namely with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Databricks
connected
Namely
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Databricks and Namely 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 · Databricks ⇄ NamelyCustomers 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.
Databricks NamelyCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Databricks and Namely integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Databricks and Namely: authenticate both systems, choose the objects to sync (such as Databricks's Delta Tables and Views), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Yes — Stacksync ships production-grade connectors for both Databricks and Namely. 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 Databricks: Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns. On Namely: No webhooks; integrations poll GET endpoints and compare the updated_at / created_at timestamps on Profiles and related objects to detect changes. 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 Databricks side: Catalogs, Schemas, Delta Tables, Views, plus custom fields where Databricks exposes them. On the Namely side: Job Tiers, Groups, Teams, Reports. Stacksync auto-detects both schemas and converts types between the two systems.
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Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
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Common patterns for Databricks and Namely: Write-back of computed values; HR data in the warehouse, minus the pipeline; Headcount and cost joined with everything else. Segments, rollups, or risk flags computed in Databricks sync back onto the matching records in Namely, where the HR team sees them in the system they already use.
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
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 447 integrations available for Databricks and Namely.
Popular · 7 of 447
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Databricks ⇄ Greenhouse -
Databricks ⇄ Jobvite -
Databricks ⇄ Lever -
Databricks ⇄ Paylocity -
Namely ⇄ Snowflake -
Namely ⇄ BigQuery -
Namely ⇄ AWS S3
No integrations match .
