Concept card

SAP Datasphere — Open SQL Schemas

As of 2026-07-23

Edited by Cédric-Olivier Mâry, sole editor and publication director — 27 years in the SAP ecosystem. How these pages are produced, and where automation is used.

What is SAP Datasphere — Open SQL Schemas?

Open SQL Schemas are a bring-your-own-SQL escape hatch inside Datasphere's governance perimeter — tables there get no catalog discovery, lineage, or model support, so using them as default leaves a tenant with no semantic layer.

What it is

An Open SQL Schema is a SAP Datasphere construct that exposes a standard SAP HANA Cloud database schema directly to authorised users and external tools — bypassing Datasphere's modelling layer and letting developers run native HANA SQL (DDL + DML), connect any HDBSQL/JDBC client, and use HANA developer tooling against tables that nonetheless belong to the Datasphere space's storage and governance perimeter. It is the bring-your-own-SQL escape hatch when Datasphere's graphical/modelling surfaces are too restrictive for a specific use case.

Why it matters

They solve three real needs — native HANA stored procedures, direct external-tool access (SAP Analysis for Office, third-party BI, Python/R), and git-versioned code-first ETL.
Lineage stops at the schema boundary, and analytic models can't be built directly on Open SQL Schema tables — they must be exposed via a Datasphere view first.
The most common mistake is defaulting to Open SQL Schema as the primary modelling layer instead of Datasphere spaces, local tables, and views.

Key points

Direct HANA Cloud schema access inside a Datasphere space — native SQL (DDL + DML), HDBSQL/JDBC clients, HANA developer tooling.
Three use cases — HANA-native procedure/view development; direct external-tool access (Analysis for Office, BI tools, Python/R); code-first ETL with git-versioned scripts.
Shares storage, security, memory with Datasphere modelling schemas in the same tenant — but content is NOT in the catalog, lineage stops at schema boundary.
Analytic models cannot be built directly on Open SQL Schema tables — expose via a Datasphere view first.
Anti-pattern — Open SQL Schema as primary modelling layer; defeats Datasphere's semantic + lineage value.
SAP Datasphere — Open SQL Schemas is mastered only when it changes a named buyer decision.
Start with the semantic contract and control model before demonstrating the tool.
Use current SAP, analyst, study, KG, and news signals as evidence, not decoration.
Separate verified facts from directional trends and modeled assumptions.
Define owner, metric, threshold, support path, and rollback before scaling.

Terms used on this page

Open SQL Schema: A SAP Datasphere construct exposing a standard SAP HANA Cloud database schema directly to authorised users and external tools, bypassing Datasphere's modelling layer; for HANA-native development and code-first patterns inside the Datasphere governance perimeter.
HDBSQL: The SAP HANA command-line SQL client; the canonical way to run native SQL against an Open SQL Schema from a developer workstation or CI pipeline.
Consumption view: A Datasphere object that exposes a curated, business-semantic view of underlying tables for downstream analytic models; the canonical way to expose Open SQL Schema content to analytic models.
Bring-your-own-SQL: The architectural pattern of providing developers with raw SQL access alongside a higher-level modelling surface; SAP Datasphere implements this via Open SQL Schemas.
Decision owner: The accountable person who accepts the trade-off and funds the next action.
Semantic contract: The shared definition of business terms, metrics, entities, and access rules used by tools and teams.
Control plane: The layer that applies policy, access, lineage, monitoring, and escalation across the operating model.
Evidence grade: A label that separates verified fact, directional signal, modeled assumption, and field observation.

Sources

Full card available to members. What the full card adds: the full decision framework · the SAP vs Snowflake / Databricks / Fabric comparison · the common pitfalls and their fix · the cheat sheet · the architecture schemas · the code blocks.

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