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Data warehouse ⇄ Developer tools

Databricks to Ldap integration — real-time, two-way sync

Keep Databricks and Ldap 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 Ldap

Close the gap between analytics and operations: Databricks holds the record while Ldap runs the day-to-day work, and Stacksync keeps the two in step in real time, in both directions.

Databricks is the central store where teams keep Views, Materialized Views, Volumes, SQL Warehouses for reporting and analysis; Ldap runs the operational side of engineering work — tracking issues, moving messages and events, watching systems, and managing users and access. The two overlap wherever the same operational data matters to both: the Operational attributes, Schema (subschema subentry), Entries, Person entries (inetOrgPerson / user) produced in Ldap are exactly what analysts want to measure in Databricks, and the curated rows in Databricks are what should drive the next action in Ldap. When that overlap is bridged by nightly ETL or hand-written scripts, dashboards lag a day behind reality and the tools that should react to warehouse signals never see them.

Stacksync syncs Views, Materialized Views, Volumes, SQL Warehouses in Databricks with Operational attributes, Schema (subschema subentry), Entries, Person entries (inetOrgPerson / user) in Ldap field by field, in real time, and in both directions. You decide which system owns which fields; Stacksync matches records on a stable external key, keeps every copy consistent, and resolves conflicts by rules you set — so analytics and operations work from the same current data instead of two drifting copies.

Common use cases

  • 01 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
  • 02 Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.
  • 03 Incrementally sync changed entries by polling modifyTimestamp, keeping a downstream store fresh without dumping the whole directory each cycle.
  • 04 Keep group member and uniqueMember attributes aligned with application entitlement tables so directory access reflects the right teams as they change.

Common sync patterns

Backfill history, then stay live

Load the existing set of Operational attributes, Schema (subschema subentry), Entries, Person entries (inetOrgPerson / user) into Databricks once, then keep it current with every change — you get full history plus real-time updates without a separate pipeline.

No batch jobs to babysit

New and changed records move field by field the moment they change, replacing scheduled ETL and one-off scripts that fail quietly and leave stale rows behind.

One shared record, kept consistent

Where both systems track the same entity, a change on either side propagates to the other, ending the manual reconciliation between the operational copy and the warehouse copy.

What you can sync between Databricks and Ldap

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 Ldap objects How this pairing syncs
Volumes Unity Catalog file storage used for staging bulk loads. Entries The fundamental unit of an LDAP directory, each addressed by a distinguished name (DN) and typed by its objectClass; synced two-way as records, with the DN driving upserts and attribute-level updates. Volumes is specific to Databricks and Entries to Ldap — each maps to any object or custom field on the other side.
SQL Warehouses The compute endpoint a sync connects to for query execution. Person entries (inetOrgPerson / user) People held under objectClasses such as inetOrgPerson, person, or Active Directory user; synced two-way to create accounts and update attributes like mail, cn, and telephoneNumber. SQL Warehouses is specific to Databricks and Person entries (inetOrgPerson / user) to Ldap — 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. Groups (groupOfNames / posixGroup) Group entries whose member or uniqueMember attributes list DNs; synced two-way to add and remove membership as teams and entitlements change. Change Data Feed is specific to Databricks and Groups (groupOfNames / posixGroup) to Ldap — each maps to any object or custom field on the other side.
Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. Organizational units (ou) Container entries that structure the DIT and scope which subtree a sync reads or writes; used to limit a sync to a branch such as ou=People,dc=corp,dc=com. Catalogs is specific to Databricks and Organizational units (ou) to Ldap — each maps to any object or custom field on the other side.
Schemas Group tables and views; syncs typically target a dedicated schema per source system. Attributes The typed name/value pairs on each entry (cn, mail, memberOf, objectClass); the schema defines which attributes each objectClass allows, and mappings are built per attribute. Schemas is specific to Databricks and Attributes to Ldap — each maps to any object or custom field on the other side.
Delta Tables The primary read and write target; operational data lands here as managed or external tables. Operational attributes Server-maintained metadata such as modifyTimestamp, createTimestamp, entryUUID, entryCSN, and uSNChanged; read to drive incremental polling and to key entries stably across syncs. Delta Tables is specific to Databricks and Operational attributes to Ldap — each maps to any object or custom field on the other side.

How changes propagate between Databricks and Ldap

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 Ldap 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 Ldap through its API, with automatic retries and rate-limit backoff.

Ldap Databricks Interval-based propagation

DetectionStacksync polls Ldap for changes on an incremental schedule, reading only records changed since the previous pass. No universal native change feed.

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.
  • Ldap: LDAP defines no request quota; throughput is bounded by the server's sizelimit and timelimit administrative limits, connection and thread caps, and the CPU/IO it shares with live authentication traffic, so large syncs can contend with production lookups and must page results.
What ships with Databricks ⇄ Ldap

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks or Ldap 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 Ldap record.

Observability

Monitoring

Track your Databricks ⇄ Ldap 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 Ldap.

How the Databricks and Ldap 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

Ldap

Integration surface
LDAPv3 protocol (RFC 4511) over TCP, typically LDAPS on port 636 or StartTLS on 389; reaches any LDAPv3 directory server - OpenLDAP, Microsoft Active Directory, 389 Directory Server, Oracle Unified Directory, PingDS/ForgeRock, NetIQ eDirectory, Apache Directory.
Authentication
A bind DN and password (simple bind), typically over TLS, using a service account with rights to the target subtree. SASL mechanisms - GSSAPI/Kerberos, DIGEST-MD5, or EXTERNAL with a client certificate - are available where the server supports them.
Change detection
No universal native change feed. Incremental sync polls the modifyTimestamp operational attribute (or entryCSN / uSNChanged on servers that expose them) for entries changed since the last cursor; detecting deletes needs a server changelog or tombstones because a plain search cannot see removed entries. Some servers add Persistent Search or RFC 4533 content-sync (syncrepl), but that support is server-specific. No webhooks.
Capabilities
read · write
Rate limits
LDAP defines no request quota; throughput is bounded by the server's sizelimit and timelimit administrative limits, connection and thread caps, and the CPU/IO it shares with live authentication traffic, so large syncs can contend with production lookups and must page results.
How it works

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

    Choose tables

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

Databricks and Ldap 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.

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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 429 integrations available for Databricks and Ldap.

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