SAP HANA + Bruin

Source

Ingest SAP HANA data into your warehouse with incremental loading, quality checks, and full lineage. Defined in YAML, version-controlled in Git.

For business teams

What you get

Real-time warehouse sync
SAP HANA tables replicate to your warehouse continuously. Analytics teams work with fresh data, not yesterday's export.
Catch issues at the source
Quality checks validate SAP HANA data as it replicates. Null IDs, duplicate records, and schema drift get caught early.
Multi-source joins
Combine SAP HANA with SaaS data, APIs, and other databases in your warehouse. One Bruin pipeline handles it all.
No untracked scripts
Replication is defined in YAML, reviewed in PRs, and deployed with CI/CD. No more mystery cron jobs.

For data & engineering teams

How it works

CDC with merge strategy
Bruin handles change data capture from SAP HANA with deduplication. Schema changes are detected and handled automatically.
YAML-defined, Git-versioned
Your SAP HANA replication is a YAML file. Review in PRs, deploy with CI/CD. No more untracked database scripts.
Row-level quality checks
Validate primary keys, foreign keys, and referential integrity on every sync. Catch corruption at the source.
Multi-source pipelines
Combine SAP HANA with SaaS APIs and other databases in one pipeline. Bruin resolves cross-source dependencies.

Before you start

SAP HANA instance running

User with appropriate authorizations

Network access to HANA port

Client software compatibility

Step 1

Add your SAP HANA connection

SAP HANA connection format. Add this to your Bruin environment file — credentials are stored securely and referenced by name in your pipeline YAML.

Parameters

usernameHANA user
passwordUser password
hostHANA server hostname
portInstance port (30015 for MDC)
databaseTenant database name

connections:
  saphana:
    type: saphana
    uri: "saphana://username:password@host:port"

Step 2

Create your pipeline

Define a YAML asset that tells Bruin what to pull from SAP HANA and where to land it. This file lives in your Git repo — reviewable, version-controlled, and deployable with CI/CD.

Available tables

SALES_DATACUSTOMER_MASTERINVENTORY_FACTS

name: raw.saphana_SALES_DATA
type: ingestr

parameters:
  source_connection: saphana
  source_table: 'SALES_DATA'
  destination: bigquery

Step 3

Add quality checks

Add column-level and custom SQL checks to your SAP HANA data. If a check fails, the pipeline stops — bad data never reaches downstream models or dashboards.

Validate row counts are within expected range

Ensure primary keys are unique and not null

Catch schema drift with freshness checks

columns:
  - name: id
    checks:
      - name: not_null
      - name: unique
  - name: created_at
    checks:
      - name: not_null

custom_checks:
  - name: row count within expected range
    query: |
      SELECT COUNT(*) BETWEEN 1 AND 10000000
      FROM raw.saphana_SALES_DATA

Step 4

Run it

One command. Bruin connects to SAP HANA, pulls data incrementally, runs your quality checks, and lands clean data in your warehouse. If a check fails, the pipeline stops — bad data never reaches downstream.

Backfill historical data with --start-date

Schedule with cron or trigger from CI/CD

Full lineage from SAP HANA to your dashboards

$ bruin run .

Running pipeline...

  saphana_SALES_DATA
    ✓ Fetched 2,847 new records
    ✓ Quality: campaign_id not_null     PASSED
    ✓ Quality: spend not_null           PASSED
    ✓ Quality: no negative ad spend     PASSED
    ✓ Loaded into bigquery

  Completed in 12s

Other Database integrations

Amazon DynamoDB

ClickHouse

Couchbase

CrateDB

DuckDB

Elasticsearch

Ready to connect SAP HANA?

Start for free, or book a demo to see how Bruin handles ingestion, quality, lineage, and scheduling for your entire data stack.

Book a demo Read the docs