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Data warehouse ⇄ Database

Databricks to SAP Hana integration — real-time, two-way sync

Keep Databricks and SAP Hana 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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Why teams connect Databricks and SAP Hana

Connect SAP Hana 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 SAP Hana'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 SAP Hana where the services that read from it get them at normal query latency.

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

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 Detect inserts, updates, and deletes on high-write tables via AFTER triggers or a last-modified column and replicate the deltas downstream near-real-time.
  • 04 Read SYS.TABLES and TABLE_COLUMNS system views to auto-discover schema and generate field mappings when new tables are added to the sync.

Common sync patterns

Serve warehouse results at database speed

Aggregates or model outputs computed in Databricks sync into SAP Hana, 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 SAP Hana focused on its operational workload.

What you can sync between Databricks and SAP Hana

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 SAP Hana objects How this pairing syncs
Schemas Group tables and views; syncs typically target a dedicated schema per source system. Schemas Namespaces that group tables and views; the connector targets a schema and enumerates its objects from the catalog to build the sync. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Volumes Unity Catalog file storage used for staging bulk loads. Triggers AFTER INSERT/UPDATE/DELETE triggers capture changes into a shadow/logging table so updates and deletes are detected near-real-time without full scans. Volumes is specific to Databricks and Triggers to SAP Hana — each maps to any object or custom field on the other side.
SQL Warehouses The compute endpoint a sync connects to for query execution. System-versioned temporal tables Column store tables with a history table tracking each row's validity period; used to reconstruct update and delete history for change capture. SQL Warehouses is specific to Databricks and System-versioned temporal tables to SAP Hana — 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. System views (SYS schema) Catalog and monitoring views such as SYS.TABLES and TABLE_COLUMNS; read to discover schema and auto-generate field mappings for new tables. Change Data Feed is specific to Databricks and System views (SYS schema) to SAP Hana — 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. Column store tables HANA's default table type, optimized for analytics; synced two-way as rows over the SQL interface with the primary key driving upserts and delete tracking. Catalogs is specific to Databricks and Column store tables to SAP Hana — 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. Row store tables OLTP-oriented tables held fully in memory; read and written the same way through the SAP HANA client for high-write operational tables. Delta Tables is specific to Databricks and Row store tables to SAP Hana — each maps to any object or custom field on the other side.

How changes propagate between Databricks and SAP Hana

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 SAP Hana 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 applied to SAP Hana as a row-level write, with types converted between the two schemas.

SAP Hana Databricks Sub-second propagation

DetectionChanges in SAP Hana are captured at the source via change data capture — no polling loop against its API. Polling on a last-modified timestamp column, or AFTER INSERT/UPDATE/DELETE triggers writing to a shadow table.

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.
  • SAP Hana: No SaaS-style request quota; throughput is bounded by connection limits, statement memory caps, and workload-class admission control (concurrency/memory limits) that share capacity with production queries.
What ships with Databricks ⇄ SAP Hana

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks or SAP Hana 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 SAP Hana record.

Observability

Monitoring

Track your Databricks ⇄ SAP Hana 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 SAP Hana.

How the Databricks and SAP Hana 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

SAP Hana

Integration surface
SQL over the SAP HANA client (JDBC/ODBC drivers); OData/REST via XS for app-layer access
Authentication
Dedicated database user credentials over an encrypted TLS connection (password sent hashed); Kerberos, SAML, JWT, and X.509 certificate authentication are also supported. SAP HANA Cloud enforces TLS and IP allowlisting.
Change detection
Polling on a last-modified timestamp column, or AFTER INSERT/UPDATE/DELETE triggers writing to a shadow table; system-versioned temporal tables and SLT/SDI provide trigger- and log-based CDC. No native webhooks.
Capabilities
read · write · CDC
Rate limits
No SaaS-style request quota; throughput is bounded by connection limits, statement memory caps, and workload-class admission control (concurrency/memory limits) that share capacity with production queries.
How it works

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

    Choose tables

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

Databricks and SAP Hana 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 584 integrations available for Databricks and SAP Hana.

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