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Data warehouse ⇄ Human resources

Cloudera Data Platform to Lever integration — real-time, two-way sync

Keep Cloudera Data Platform and Lever 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 Cloudera Data Platform and Lever

Land the people and organization records from Lever in Cloudera Data Platform as live tables for workforce reporting, without extract jobs, and write computed results back where Lever 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 Lever is the system of record for employees and payroll, for candidates and applications, or for learners and course completions, the reporting belongs in Cloudera Data Platform next to everything else the company measures.

Stacksync syncs Opportunities, Postings, Requisitions, Offers from Lever into tables in Cloudera Data Platform continuously, handling API limits and schema drift as they come. The connection is bi-directional, so values computed in Cloudera Data Platform, such as headcount rollups, cost allocations, or attrition risk flags, can be written back to fields in Lever 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 Sync curated CDP tables into an operational Postgres so applications query a low-latency copy instead of hitting the cluster.
  • 02 Publish CRM or ERP records into CDP so enterprise analytics runs alongside existing data lake workloads.
  • 03 Push Postings and Requisitions into an HRIS or warehouse to reconcile approved headcount against open roles.
  • 04 When an Opportunity is marked hired, write the candidate and its Offer into an HRIS or onboarding system to start employee provisioning.

Common sync patterns

Headcount and cost joined with everything else

Analysts combine Lever's workforce records with finance, product, or operational data already in Cloudera Data Platform 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 Cloudera Data Platform gives you a durable, queryable record of how Lever's records change over time, for headcount planning and audit questions.

What you can sync between Cloudera Data Platform and Lever

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.

Cloudera Data Platform objects Lever objects How this pairing syncs
Iceberg tables Open table format tables in newer CDP versions, with snapshot metadata usable for incremental reads. Offers Offer records attached to an Opportunity with status and offer-form fields; exposed read-only through GET /opportunities/:id/offers, so they sync outbound to an HRIS or onboarding system when a candidate reaches the offer stage. Iceberg tables is specific to Cloudera Data Platform and Offers to Lever — each maps to any object or custom field on the other side.
Views SQL views that can present curated, sync-ready projections of raw lake data. Users Lever team members (recruiters, hiring managers) with configurable roles; can be created via POST /users, deactivated, and reactivated through the API. Views is specific to Cloudera Data Platform and Users to Lever — each maps to any object or custom field on the other side.
Partitions Table partitions (often by date) that incremental extraction jobs use to scope reads. Stages Pipeline stage definitions that Opportunities move through; read to model funnel state and stage transitions in a database. Partitions is specific to Cloudera Data Platform and Stages to Lever — each maps to any object or custom field on the other side.
Object store / HDFS files Underlying Parquet or ORC files on HDFS or cloud storage backing the tables. Feedback Interview feedback and scorecard forms attached to Opportunities; created via POST /opportunities/:id/feedback and consolidated into a warehouse for interviewer analytics. Object store / HDFS files is specific to Cloudera Data Platform and Feedback to Lever — each maps to any object or custom field on the other side.
Databases Logical namespaces in the shared Hive Metastore that group tables for access control and syncs. Interviews Scheduled interview panel events with times and interviewers; read for scheduling reporting and time-to-hire metrics, and creatable via the panels endpoint. Databases is specific to Cloudera Data Platform and Interviews to Lever — each maps to any object or custom field on the other side.
Hive tables Warehouse tables queried over JDBC/ODBC; classic managed tables are append-oriented. Notes and Contacts Free-text Notes on Opportunities plus the underlying Contact (person) that dedupes multiple Opportunities; notes are posted via POST /opportunities/:id/notes and contact-level tags, sources, and links can be added back for attribution. Hive tables is specific to Cloudera Data Platform and Notes and Contacts to Lever — each maps to any object or custom field on the other side.

How changes propagate between Cloudera Data Platform and Lever

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.

Cloudera Data Platform Lever Interval-based propagation

DetectionStacksync polls Cloudera Data Platform for changes on an incremental schedule, reading only records changed since the previous pass. Polling via SQL on timestamp or partition columns.

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

Lever Cloudera Data Platform Sub-second propagation

DetectionLever notifies Stacksync of record changes through webhook events. Webhooks for candidate and application lifecycle events (applicationCreated, candidateStageChange, candidateArchiveStateChange, candidateHired,.

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

Rate-limit considerations

  • Cloudera Data Platform: Constrained by cluster capacity and admission control rather than API rate limits.
  • Lever: 10 requests/second per API key with a token-bucket burst to ~20/s; POSTs that create candidates/applications are throttled to roughly 2/second. Sustained overage returns 429 with Retry-After. List endpoints are cursor-paginated at up to 100 records per page.
What ships with Cloudera Data Platform ⇄ Lever

Connect Cloudera Data Platform and Lever for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Cloudera Data Platform–Lever connection.

Real-time

Two-way sync

Changes in Cloudera Data Platform or Lever instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Cloudera Data Platform or Lever 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 Cloudera Data Platform or Lever record.

Observability

Monitoring

Track your Cloudera Data Platform ⇄ Lever sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Cloudera Data Platform and Lever.

How the Cloudera Data Platform and Lever connectors work

Cloudera Data Platform

Integration surface
JDBC/ODBC over Hive and Impala SQL endpoints, plus REST management APIs
Authentication
Kerberos, LDAP, or workload user credentials, often brokered through the Knox gateway
Change detection
Polling via SQL on timestamp or partition columns; no consumer-facing change feed
Capabilities
read · write
Rate limits
Constrained by cluster capacity and admission control rather than API rate limits

Lever

Integration surface
REST Data API (api.lever.co/v1)
Authentication
API key over HTTP Basic auth (key as username, blank password) for internal integrations, or OAuth 2.0 with 1-hour access tokens for partner integrations (auth.lever.co)
Change detection
Webhooks for candidate and application lifecycle events (applicationCreated, candidateStageChange, candidateArchiveStateChange, candidateHired, interview created/updated/deleted), plus incremental polling via created_at and updated_at range filters on Opportunities
Capabilities
read · write · webhooks
Rate limits
10 requests/second per API key with a token-bucket burst to ~20/s; POSTs that create candidates/applications are throttled to roughly 2/second. Sustained overage returns 429 with Retry-After. List endpoints are cursor-paginated at up to 100 records per page.
Lever setup guide
How it works

How to connect Cloudera Data Platform to Lever — 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 Cloudera Data Platform and Lever 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
    Cloudera Data Platform connected
    Lever connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Cloudera Data Platform and Lever 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 · Cloudera Data Platform ⇄ Lever
    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
    Cloudera Data Platform Lever
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Cloudera Data Platform and Lever 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
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→ 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 436 integrations available for Cloudera Data Platform and Lever.

Popular · 7 of 436
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