The C_HAMOD certification validates data provisioning and data modelling skills in SAP HANA Cloud and SAP HANA. This guide organises preparation into five phases, from database fundamentals to final assessment readiness, with hands-on exercises and checklists you can tick off as you go.
Preparing for an SAP certification means understanding how database components work, how information moves between systems, and how analytical models produce accurate results. For data engineers, SAP HANA brings these responsibilities together, and strong results come from database knowledge, SQL skills, data integration experience, and analytical modelling working side by side.
This C_HAMOD Preparation Guide is designed for professionals preparing for the SAP Certified - Data Engineer - SAP HANA certification. It breaks preparation into manageable phases, so you build a technical foundation first and then move into more advanced operations. The aim is skills you can apply independently.
SAP identifies the certification by the examination code C_HAMOD. It validates data provisioning and data modelling skills in SAP HANA Cloud and SAP HANA. The current details on the SAP official certification page are summarised below.
| Examination detail | Current information |
|---|---|
| Certification | SAP Certified - Data Engineer - SAP HANA |
| Exam code | C_HAMOD |
| Assessment format | System-based |
| Duration | 3 hours |
| Passing score | 60% |
| Language | English |
| Data Access | 41 to 50% |
| Calculation Views | 41 to 50% |
The assessment is system-based, so candidates apply their technical knowledge inside the SAP environment. Data Access and Calculation Views carry equal weight, which makes consistent preparation across both areas the best plan.
A good grasp of relational databases makes advanced SAP HANA operations easier to follow. Start with tables, rows, columns, schemas, primary keys, foreign keys, relationships, constraints, and data types. Then move to SQL: SELECT statements and WHERE conditions, ORDER BY and GROUP BY clauses, INNER JOIN and LEFT OUTER JOIN, aggregate functions such as SUM, COUNT, and AVG, subqueries, expressions, and the handling of NULL values and duplicate records.
Practical exercise: create or inspect a small dataset of customers, products, and sales transactions. Write queries that retrieve customer information, filter transactions, calculate sales totals, and group results by product category. Check each result by hand against a small sample of records, which shows how queries behave and exposes mistakes in joins and aggregations.
Foundation checklist: