SAC / Datasphereadvanced

SAP Databricks in Business Data Cloud: Zero-Copy Delta Sharing and AI/ML (Part 5)

Key takeaway

Understand SAP Databricks inside Business Data Cloud, including governed access to SAP data products, zero-copy Delta Sharing, notebooks, SQL, data engineering and AI/ML workloads.

Explanation

SAP Databricks in Business Data Cloud

What SAP Databricks adds

SAP Databricks brings a managed Databricks experience into Business Data Cloud for advanced data engineering, data science and AI/ML.

The important architectural idea is not merely “Databricks is available.”

It is the ability to work with governed SAP business data without rebuilding every pipeline from scratch.

Zero-copy Delta Sharing

Where supported, Delta Sharing can provide access to shared data without creating another full physical copy for every consumer.

Benefits:

less duplication;
faster access;
lower pipeline maintenance;
more consistent source semantics.

Zero-copy does not remove governance. Consumers still need:

access control;
ownership;
data-quality understanding;
usage monitoring.

Typical use cases

feature engineering;
forecasting;
ML experimentation;
combining SAP and non-SAP data;
notebook-based exploration;
large-scale SQL/data engineering;
publishing useful results back as governed data products where supported.

Design boundary

Do not move core ERP transaction logic into a notebook.

Databricks should enrich analytical/AI workloads while operational systems remain systems of record.

Checkpoint

You can explain the role of SAP Databricks, Delta Sharing and governed data products without describing it as a generic external data lake.

Interview takeaway

“SAP Databricks brings managed Databricks data engineering and AI/ML into Business Data Cloud. I use governed SAP data products and zero-copy Delta Sharing where appropriate, while keeping operational transaction ownership in source applications.”

Code example

ABAP Code

Governed SAP Data Product ⇄ Delta Sharing ⇄ SAP Databricks Workspace → Engineering / ML / SQL

Real project scenario

A forecasting team previously exports weekly CSVs from SAP. A governed shared-data pattern gives data scientists fresher trusted data and removes manual extraction.

Common mistakes

Treating notebooks as ERP system of record.\n- Copying the same datasets repeatedly.\n- Ignoring data-product ownership.\n- No cost/workload governance.

Best practices

Govern access.\n- Preserve business context.\n- Keep transaction systems authoritative.\n- Monitor compute/cost and data quality.

Interview angle

Explain what zero-copy sharing means and why it matters for SAP data science workloads.

Practice this lesson

Quick check

1. Explain Business Data Cloud in terms of governed business data and data products rather than only storage technology. In the SAP Business Data Cloud context, which answer shows the strongest practical judgment?

2. Scenario — Several analytics teams independently replicated sales data with different currency logic. Which response best addresses the design or operating risk in SAP Business Data Cloud Fundamentals and Data Products?

3. Scenario — A finance AI assistant used raw replicated ERP tables and produced inconsistent gross-margin results because currency and elimination logic were missing. Which response best addresses the design or operating risk in Business Data Cloud Architecture: Datasphere, Analytics, AI and Governance?

Interview questions

1. What design decisions and trade-offs matter most when implementing SAP Business Data Cloud, particularly for SAP Business Data Cloud Fundamentals and Data Products?

Answer it in your own words first, then compare.

2. A project team is working on SAP Business Data Cloud. Using the Business Data Cloud Architecture: Datasphere, Analytics, AI and Governance scenario, how would you approach the requirement from design through validation?