SAP HANA Cloud Implementation, Modelling and Reporting

Learn to add instance mapping and adjust SAP HANA cloud configuration by binding your database instance to a cloud environment using the org ID, enabling decoupled modeling workflows.

Provision a SAP HANA cloud instance via a BTP trial account, install tools, set up SAP HANA database and data lake, perform instance mapping, and test with Database Explorer.

Connect your cloud connector from on-prem to the BTP cockpit, linking to the SAP HANA Cloud instance. Configure region, sub account, and location ID to verify an active connection.

Learn to connect your on premise SAP HANA express edition to the cloud connector, verify IP and host details, check the web ID, and establish a secure cloud-to-on-premise link.

Virtualize on-prem Hana tables to Hana cloud by creating a remote source and virtual tables, enabling remote queries and analytics through calculation views and cubes.

Learn how to import efashion data into your main SAP HANA Cloud database, create a fashion schema, and import csv datasets with header handling, column mapping, and table creation.

Modify hdbgrant file to grant the service roles and privileges for data access in the E fashion schema, binding the object owner and application user to the same role.

Explore SAP Hana Cloud's semantic layer, data lineage, and impact analysis, and learn to manage semantics, aggregations, variables, data masking, and hierarchies in the business application studio.

Clarify how measures and dimensions in data modeling enable reporting, showing sales by region and quantity by product as measures, while regions, products, and cities serve as dimensions.

Explore how measures and dimensions live in fact and dimension tables, modeled via a star schema, and distinguish physical tables from models that are not materialized in Hana.

Explore the distinction between design time and runtime calculation views in SAP HANA Cloud, including spaces, HDI persistence, and how building objects pushes changes to runtime for reporting tools.

Explore business reporting requirements for active partners, suppliers, and purchases, including top active suppliers by quantity and amount, by location, and currency, using exploratory data sets for charts and maps.

Import data into your SAP HANA cloud project by creating cur, master, and transactions folders, uploading CSV and HDB files, and deploying to persist and verify the data.

Learn to build a SAP HANA cloud business partner dimension view by projecting columns from the business partner and address tables, then joining them and configuring semantics and output naming.

Create a calculated column firm name by concatenating company name and legal form in the BP business partner calculation view, and implement it in the projection node.

Learn to answer business questions by testing data scenarios, building cubes and views, and analyzing top suppliers by quantity and value across regions.

Create a dynamic filter on measures with a runtime input parameter to filter the gross amount, such as greater than 1000, and implement the filter expression at runtime.

Map address type codes to readable labels using a calculated column with the case function in a projection, turning 0102 into residential or commercial for clearer reporting.

Create time dimension views in SAP HANA Cloud by generating time data with the update time dimension procedure, selecting granularity and the target schema and table.

Change modeling nodes on the fly while preserving mappings, replace underlying objects with data sources, preview data, and propagate or extract semantics to keep models consistent.

Explore currency conversion in a SAP HANA cloud cube by filtering to the base currency, adding a gross amount in USD, and configuring semantics with ticker tables and exchange rates.

Demystify joins by explaining how to relate data across tables, understand cardinalities—1 to 1, one to many, many to many—and implement inner, left, right, and full outer joins for reporting.

Explore how a left outer join links the business partner and address tables in a 1-to-1 setup, producing 45 rows with nulls on the right for nonmatches.

Discover how to union two region-specific data cubes (EMEA and Americas) using the union node, map columns by name, and add a flag to identify each projection in the result.

Explore the minus node to remove APJ from dataset A using projection one and projection two, map the same columns, and note how input order changes the result.