Real-time
Two-way sync
Changes in Dremio or SAP Hana instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Database
Dremio to SAP Hana integration — real-time, two-way sync
Keep Dremio 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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Adopted by fast-scaling companies moving mission-critical data in real time
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Why teams connect Dremio and SAP Hana
Connect SAP Hana and Dremio 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 Dremio, 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 Dremio in real time, and result tables in Dremio sync back into SAP Hana, with schema and type mapping between the two systems handled for you.
Common use cases
- 01 Reverse-ETL aggregates computed over lake data out to CRMs and finance tools for business users.
- 02 Publish operational database tables into Iceberg via Dremio so the lakehouse reflects current application state.
- 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
Offload heavy reads
Point analytical queries at the synced copy in Dremio and keep SAP Hana focused on its operational workload.
Operational data in the warehouse, minus the pipeline
Rows from SAP Hana land in Dremio as they change, replacing hand-built CDC and batch extract jobs.
Serve warehouse results at database speed
Aggregates or model outputs computed in Dremio sync into SAP Hana, where whatever reads from that database gets them without querying the warehouse.
What you can sync between Dremio 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.
| Dremio objects | SAP Hana objects | How this pairing syncs | |
|---|---|---|---|
| Spaces and folders Namespaces that organize virtual datasets and govern access. | 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. | Spaces and folders is specific to Dremio and Row store tables to SAP Hana — each maps to any object or custom field on the other side. | |
| Reflections Materialized accelerations that make repeated extraction queries cheaper. | Calculation views Modeled analytic views over base tables; read-only sources for pushing aggregated or joined results into a warehouse or downstream app. | Reflections is specific to Dremio and Calculation views to SAP Hana — each maps to any object or custom field on the other side. | |
| Jobs Query execution records useful for monitoring sync workloads. | SQL views Standard database views; read-only projections synced outbound when the source data should not be exposed as raw base tables. | Jobs is specific to Dremio and SQL views to SAP Hana — each maps to any object or custom field on the other side. | |
| Sources Connected storage and database systems (S3, ADLS, relational databases) Dremio queries in place. | Schemas Namespaces that group tables and views; the connector targets a schema and enumerates its objects from the catalog to build the sync. | Sources is specific to Dremio and Schemas to SAP Hana — each maps to any object or custom field on the other side. | |
| Physical datasets Tables and files promoted from sources; the raw data a sync ultimately reads. | Sequences Server-generated key values; relevant when writing rows into tables whose identity is assigned HANA-side rather than by the source system. | Physical datasets is specific to Dremio and Sequences to SAP Hana — each maps to any object or custom field on the other side. | |
| Virtual datasets (views) SQL views layering semantics over physical data; the preferred sync target for curated extracts. | 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. | Virtual datasets (views) is specific to Dremio and Triggers to SAP Hana — each maps to any object or custom field on the other side. |
How changes propagate between Dremio 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.
Dremio SAP Hana Interval-based propagation
DetectionStacksync polls Dremio for changes on an incremental schedule, reading only records changed since the previous pass. Polling via SQL.
DeliveryEach detected change is applied to SAP Hana as a row-level write, with types converted between the two schemas.
SAP Hana Dremio 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 Dremio as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- Dremio: Bounded by engine capacity and workload management rather than API 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 Dremio ⇄ SAP Hana
Connect Dremio and SAP Hana for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Dremio–SAP Hana connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Dremio 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 Dremio or SAP Hana record.
Observability
Monitoring
Track your Dremio ⇄ 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 Dremio and SAP Hana.
How the Dremio and SAP Hana connectors work
Dremio
- Integration surface
- Arrow Flight SQL, JDBC/ODBC, and a REST API
- Authentication
- Personal access tokens or username/password; OAuth-based SSO on Dremio Cloud
- Change detection
- Polling via SQL; Iceberg table snapshots can anchor incremental reads; no consumer-facing change feed
- Capabilities
- read · write
- Rate limits
- Bounded by engine capacity and workload management rather than API 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 Dremio 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.
- 01
Connect your apps
Authenticate Dremio 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.
Dremio
connected
SAP Hana
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Dremio 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.
objects · Dremio ⇄ SAP HanaCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Dremio SAP HanaCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Dremio and SAP Hana integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Dremio and SAP Hana: authenticate both systems, choose the objects to sync (such as Dremio's Spaces and folders and Reflections), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Stacksync pricing is usage-based and starts at $1,000/month, including the managed Dremio and SAP Hana connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Dremio–SAP Hana integration in-house.
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Yes — Stacksync ships production-grade connectors for both Dremio and SAP Hana. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection on Dremio: Polling via SQL; Iceberg table snapshots can anchor incremental reads; no consumer-facing change feed. On SAP Hana: 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. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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On the Dremio side: Physical datasets, Virtual datasets (views), Apache Iceberg tables, Spaces and folders, plus custom fields where Dremio exposes them. On the SAP Hana side: Triggers, System-versioned temporal tables, System views (SYS schema), Column store tables. Stacksync auto-detects both schemas and converts types between the two systems.
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Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
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.
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
CSA STAR
DPF US-EU-UK-CH
→ 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:
Related integrations
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