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Data warehouse ⇄ Database

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

Keep Databricks and Reltio 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 Reltio

Connect Reltio and Databricks with one live, two-way sync: operational rows flow into the warehouse, and computed results flow back where systems can read them fast.

Operational databases and analytical warehouses want the same data at different moments. Analysts want Reltio's rows in Databricks, current and joinable, without a change-data-capture pipeline to maintain. Engineers want the outputs of warehouse work, such as aggregates, features, and segments, available in Reltio where the services that read from it get them at normal query latency.

Stacksync covers both directions with one connection. Tables or collections in Reltio sync into Databricks in real time, and result tables in Databricks sync back into Reltio, with schema and type mapping between the two systems handled for you.

Common use cases

  • 01 Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.
  • 02 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
  • 03 Feed Interactions (transactions, visits, events) into Reltio to enrich entity profiles, then read them back for 360-degree reporting.
  • 04 Mirror the Reltio Activity Log and change history into a database for audit, lineage, and governance reporting.

Common sync patterns

Serve warehouse results at database speed

Aggregates or model outputs computed in Databricks sync into Reltio, where whatever reads from that database gets them without querying the warehouse.

Fresh analytics without loading windows

Because changes stream continuously, analysts query current data instead of waiting for last night's load.

Offload heavy reads

Point analytical queries at the synced copy in Databricks and keep Reltio focused on its operational workload.

What you can sync between Databricks and Reltio

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 Reltio objects How this pairing syncs
SQL Warehouses The compute endpoint a sync connects to for query execution. Reference Data (RDM) Managed lookup and reference values (country codes, standardized values, hierarchies); read and updated so downstream systems share consistent reference data. SQL Warehouses is specific to Databricks and Reference Data (RDM) to Reltio — 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. Entities Golden records for each configured entity type (for example Organization, Individual/Contact, Location, or Product); full CRUD via /entities, so records are created, updated, and deleted, and Reltio matches and merges them by survivorship rules. Change Data Feed is specific to Databricks and Entities to Reltio — 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. Relations Typed relationships between two entities (affiliations, hierarchies, employment, households); read and written via /relations to keep account hierarchies and affiliation graphs aligned across systems. Catalogs is specific to Databricks and Relations to Reltio — 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. Crosswalks Per-entity references to the source systems and their record IDs; written when loading records so Reltio ties each source contribution to a golden record, and read to trace lineage back to origin systems. Schemas is specific to Databricks and Crosswalks to Reltio — 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. Interactions Transactional or event records linked to entities (purchases, visits, activities); read and written via /interactions to enrich profiles and power 360-degree reporting. Delta Tables is specific to Databricks and Interactions to Reltio — each maps to any object or custom field on the other side.
Views Curated read-only projections used as sync sources for downstream tools. Matches (Potential Matches) Candidate duplicate pairs produced by match rules; read to review, and resolved with merge, unmerge, or not-a-match actions to control survivorship. Views is specific to Databricks and Matches (Potential Matches) to Reltio — each maps to any object or custom field on the other side.

How changes propagate between Databricks and Reltio

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

Reltio Databricks Interval-based propagation

DetectionStacksync polls Reltio for changes on an incremental schedule, reading only records changed since the previous pass. Polling the REST API on updateTime (epoch-ms), for example filter=gt(updateTime,<timestamp>), for entities and relations changed past a stored.

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.
  • Reltio: Reltio applies per-tenant API throttling and returns HTTP 429 (Too Many Requests) when limits are exceeded; there is no single fixed request-per-second cap published for all tenants, and throttling is tuned per tenant and environment. Large loads and reads use bulk create/update and the asynchronous export/jobs API rather than row-by-row calls.
What ships with Databricks ⇄ Reltio

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks or Reltio 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 Reltio record.

Observability

Monitoring

Track your Databricks ⇄ Reltio 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 Reltio.

How the Databricks and Reltio 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

Reltio

Integration surface
Reltio REST API (Data API) — /entities, /relations, /interactions, /activities, plus Match, RDM (reference data), and Data Change Request endpoints; base URL https://{environment}.reltio.com/reltio/api/{tenantId}
Authentication
OAuth 2.0 bearer tokens obtained from Reltio's central auth server (POST https://auth.reltio.com/oauth/token, client-credentials or password grant, application/x-www-form-urlencoded) and sent as Authorization: Bearer <token>; access tokens expire after about 60 minutes and are renewed with a refresh token, and are scoped per API (entities_api, relations_api, interactions_api, configuration_api, graphs_api)
Change detection
Polling the REST API on updateTime (epoch-ms), for example filter=gt(updateTime,<timestamp>), for entities and relations changed past a stored watermark. Reltio has no CDC log external tools consume; separately, Reltio's event streaming can publish entity change events (created, changed, removed) to a customer-configured message queue (Amazon SQS/SNS, Google Pub/Sub, Azure Service Bus, or Kafka), which is a queue feed rather than HTTP webhooks.
Capabilities
read · write
Rate limits
Reltio applies per-tenant API throttling and returns HTTP 429 (Too Many Requests) when limits are exceeded; there is no single fixed request-per-second cap published for all tenants, and throttling is tuned per tenant and environment. Large loads and reads use bulk create/update and the asynchronous export/jobs API rather than row-by-row calls.
How it works

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

    Choose tables

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

Databricks and Reltio 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 475 integrations available for Databricks and Reltio.

Popular · 4 of 475
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