Skip to content
Data warehouse ⇄ Human resources

BigQuery to Namely integration — real-time, two-way sync

Keep BigQuery 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.

  • 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 BigQuery and Namely

Land the people and organization records from Namely in BigQuery 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 BigQuery next to everything else the company measures.

Stacksync syncs Teams, Reports, Profile Fields, Events from Namely into tables in BigQuery continuously, handling API limits and schema drift as they come. The connection is bi-directional, so values computed in BigQuery, 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 Land CRM and ERP records in BigQuery continuously so dashboards reflect business systems without nightly batch jobs
  • 02 Activate modeled BigQuery tables by syncing computed attributes back into sales and marketing tools
  • 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

Headcount and cost joined with everything else

Analysts combine Namely's workforce records with finance, product, or operational data already in BigQuery for reporting the HR system cannot produce on its own.

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 BigQuery gives you a durable, queryable record of how Namely's records change over time, for headcount planning and audit questions.

What you can sync between BigQuery 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.

BigQuery objects Namely objects How this pairing syncs
Datasets Organizational container — you pick which dataset’s tables to sync. Profile Fields Metadata describing standard and company-defined custom field sections; read to discover schema and generate mappings. Datasets is specific to BigQuery and Profile Fields to Namely — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. Events Home-feed items such as announcements, birthdays, anniversaries, and new arrivals; typically read-only into comms tools. Projects is specific to BigQuery and Events to Namely — each maps to any object or custom field on the other side.
Tables The syncable unit: only tables can be synced per the Stacksync docs. Profiles The core employee record (personal, job, contact, and compensation fields); synced two-way via GET/POST/PUT with updated_at driving incremental polling. Tables is specific to BigQuery and Profiles to Namely — each maps to any object or custom field on the other side.
Partitioned tables Synced like regular tables; partition columns map to target fields. Job Titles Job title definitions; read and written so titles stay aligned with an HRIS, directory, or reporting dimension. Partitioned tables is specific to BigQuery and Job Titles to Namely — each maps to any object or custom field on the other side.
Clustered tables Supported; clustering is transparent to the sync. Job Tiers Leveling hierarchy grouping zero-to-many Job Titles; read to map seniority into warehouse dimension tables. Clustered tables is specific to BigQuery and Job Tiers to Namely — each maps to any object or custom field on the other side.

How changes propagate between BigQuery 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.

BigQuery Namely Sub-second propagation

DetectionChanges in BigQuery are captured at the source via change data capture — no polling loop against its API. Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen").

DeliveryEach detected change is written to Namely through its API, with automatic retries and rate-limit backoff.

Namely BigQuery 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 BigQuery as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • BigQuery: Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes.
  • 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 BigQuery ⇄ Namely

Connect BigQuery and Namely for flexible, real-time data sync.

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

Real-time

Two-way sync

Changes in BigQuery or Namely instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery 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 BigQuery or Namely record.

Observability

Monitoring

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

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between BigQuery and Namely.

How the BigQuery and Namely connectors work

BigQuery

Integration surface
GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs
Authentication
Google Cloud service account: create a dedicated service account, grant roles (BigQuery Data Editor, BigQuery Job User, Cloud Functions Service Agent, Cloud Run Developer, Eventarc Event Receiver
Change detection
Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen") with a Cloud Run "secure portal for real-time notification service in
Capabilities
read · write · CDC
Rate limits
Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes
BigQuery setup guide

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 BigQuery 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.

  1. 01

    Connect your apps

    Authenticate BigQuery 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.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    BigQuery connected
    Namely connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the BigQuery 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.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · BigQuery ⇄ Namely
    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
    BigQuery Namely
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
Try interactive demo
FAQ

BigQuery and Namely 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
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 439 integrations available for BigQuery and Namely.

Popular · 5 of 439
All BigQuery integrations All Namely 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