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Data warehouse ⇄ Human resources

Apache Impala to Lever integration — real-time, two-way sync

Keep Apache Impala 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 Apache Impala and Lever

Land the people and organization records from Lever in Apache Impala 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 Apache Impala next to everything else the company measures.

Stacksync syncs Interviews, Notes and Contacts, Opportunities, Postings from Lever into tables in Apache Impala continuously, handling API limits and schema drift as they come. The connection is bi-directional, so values computed in Apache Impala, 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 mutable reference data into Kudu tables via Impala so row-level updates are possible on the Hadoop side.
  • 02 Read new partitions incrementally from Parquet tables and land them in a cloud warehouse during migration.
  • 03 Two-way sync Opportunities and their Stages between Lever and a Postgres database so recruiting ops and analysts query the pipeline in SQL while recruiters keep working in Lever.
  • 04 Push Postings and Requisitions into an HRIS or warehouse to reconcile approved headcount against open roles.

Common sync patterns

HR data in the warehouse, minus the pipeline

People and organization records from Lever arrive in Apache Impala 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 Lever's workforce records with finance, product, or operational data already in Apache Impala 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.

What you can sync between Apache Impala 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.

Apache Impala objects Lever objects How this pairing syncs
Kudu Tables Kudu-backed tables that support row-level insert, update, upsert, and delete. 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. Kudu Tables is specific to Apache Impala and Notes and Contacts to Lever — each maps to any object or custom field on the other side.
External Tables Tables over files loaded by other tools, queryable without data movement. Opportunities The core pipeline record for a candidate applying to a role; replaced the deprecated Candidates endpoint. Created via POST /opportunities and updated (stage, archive, links, tags, sources, files) through the API, and synced two-way with a database or HRIS. External Tables is specific to Apache Impala and Opportunities to Lever — each maps to any object or custom field on the other side.
Users and Roles Principals (often via Ranger/Sentry) used to grant scoped read access. Postings Job posting records with categories, apply URLs, workplace type, and requisition codes. Can be created via POST /postings and read into a warehouse for open-role reporting. Users and Roles is specific to Apache Impala and Postings to Lever — each maps to any object or custom field on the other side.
Databases Namespaces shared with the Hive Metastore that scope tables. Requisitions Headcount/requisition records with custom requisition fields, tied to Postings; read via GET /requisitions and synced to an HRIS to reconcile approved headcount against open roles. Databases is specific to Apache Impala and Requisitions to Lever — each maps to any object or custom field on the other side.
Tables HDFS or object-storage backed tables (commonly Parquet) read at interactive speed. 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. Tables is specific to Apache Impala and Offers to Lever — each maps to any object or custom field on the other side.
Partitions Partition values used to limit scans and drive incremental reads. Users Lever team members (recruiters, hiring managers) with configurable roles; can be created via POST /users, deactivated, and reactivated through the API. Partitions is specific to Apache Impala and Users to Lever — each maps to any object or custom field on the other side.

How changes propagate between Apache Impala 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.

Apache Impala Lever Interval-based propagation

DetectionStacksync polls Apache Impala for changes on an incremental schedule, reading only records changed since the previous pass. Polling on partition or timestamp columns.

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

Lever Apache Impala 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 Apache Impala as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Apache Impala: No API quotas; concurrency is bounded by cluster resources and admission control settings.
  • 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 Apache Impala ⇄ Lever

Connect Apache Impala and Lever for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Impala–Lever connection.

Real-time

Two-way sync

Changes in Apache Impala or Lever instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Impala 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 Apache Impala or Lever record.

Observability

Monitoring

Track your Apache Impala ⇄ Lever sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Apache Impala and Lever.

How the Apache Impala and Lever connectors work

Apache Impala

Integration surface
SQL over JDBC/ODBC (HiveServer2-compatible protocol)
Authentication
Deployment-dependent: Kerberos, LDAP, or username/password
Change detection
Polling on partition or timestamp columns; no change log exposed for external consumers
Capabilities
read · write
Rate limits
No API quotas; concurrency is bounded by cluster resources and admission control settings

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 Apache Impala 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 Apache Impala 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
    Apache Impala connected
    Lever connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Apache Impala 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 · Apache Impala ⇄ 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
    Apache Impala Lever
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Apache Impala and Lever 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

Every pair below is a real-time, two-way sync. Search all 435 integrations available for Apache Impala and Lever.

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