The Open Operational Data Store (ODS) layer in the Layered Scalable Architecture (LSA++) of SAP

BW/4HANA plays a critical role in managing and processing data as part of the overall data

warehousing architecture. The Open ODS layer is designed to handle operational and near-real-time

data requirements while maintaining flexibility and performance. Below is an explanation of the

purposes of this layer and why the correct answers are A and C .

Correct Answe rs and Explanation:

A . Harmonization of data from several source systems

The Open ODS layer is often used to harmonize data from multiple source systems. This involves

consolidating and standardizing data from different sources into a unified format.

For example, if you have sales data coming from different ERP systems with varying structures or

naming conventions, the Open ODS layer can be used to align these differences before the data is

further processed or consumed for reporting.

Reference : In SAP BW/4HANA, the Open ODS layer supports harmonization by providing tools like

transformations and mappings to ensure consistency across data from disparate sources. This is

particularly important in heterogeneous landscapes where data integration is a challenge.

C . Initial staging of source system data

The Open ODS layer serves as an initial staging area for raw data extracted from source systems. It

provides a temporary storage point where data can be landed and prepared for further processing or

analysis.

This staging capability ensures that data is available in its original form (or minimally transformed)

for downstream processes, such as loading into other layers of the LSA++ architecture or enabling

real-time reporting.

Reference : The Open ODS layer is optimized for high-performance data ingestion and staging,

making it ideal for scenarios where data needs to be quickly loaded and made available for

operational reporting or further transformation.

Incorrect Options:

B . Transformations of data based on business logic

While transformations can occur in the Open ODS layer, this is not its primary purpose. The Open

ODS layer focuses on initial data staging and harmonization rather than complex business logic

transformations.

Business logic transformations are typically performed in subsequent layers of the LSA++

architecture, such as the Data Propagation Layer (DPL) or the Core Data Warehouse Layer (CDWH) .

Reference : SAP recommends keeping the Open ODS layer lightweight and focused on operational

and near-real-time use cases, avoiding heavy transformations that could impact performance.

D . Real-time reporting on source system data without staging

The Open ODS layer does support real-time reporting, but it requires data to be staged first. The layer

acts as an intermediate storage point where data is landed and processed before being made

available for reporting.

Reporting directly on source system data without staging is typically achieved through Virtual Data

Models (VDMs) or SAP HANA Live , which bypass the need for staging entirely.

Reference : The Open ODS layer is not designed for direct, real-time access to source system data

without staging. Its role is to stage and prepare data for operational reporting or further processing.

Conclusion:

The Open ODS layer in SAP BW/4HANA's LSA++ architecture is primarily used for harmonizing data

from multiple source systems and serving as an initial staging area for source system data . These

purposes align with its role in supporting operational and near-real-time reporting while maintaining

flexibility and performance. The correct answers are therefore A and C .

In the context of Operational Data Provisioning (ODP) , the operational delta queue (ODQ) is a critical

component that manages delta records for incremental data extraction. The management of the

ODQ depends on the specific ODP context, particularly whether the target system or source system is

responsible for maintaining the delta queue.

Correct Answer:

ODP_BW (Option A):

In the ODP_BW context, the operational delta queue (ODQ) is managed by the target system (SAP

BW/4HANA).

This means that SAP BW/4HANA takes responsibility for tracking and managing delta records,

ensuring that only new or changed data is extracted during subsequent loads.

This approach is commonly used when the source system does not natively support delta

management or when the target system needs more control over the delta handling process.

Why Other Options Are Incorrect:

ODP_SAP (Option B):

In the ODP_SAP context, the source system (e.g., SAP ERP) manages the operational delta queue.

This is the default behavior for SAP source systems, where the source system maintains the delta

queue and provides delta records to the target system upon request.

ODP_CDS (Option C):

The ODP_CDS context is used for extracting data from Core Data Services (CDS) views in SAP HANA or

SAP S/4HANA. In this context, delta handling is typically managed by the source system (SAP HANA

or S/4HANA) and not the target system.

ODP_HANA (Option D):

The ODP_HANA context is used for extracting data from SAP HANA-based sources. Similar to

ODP_CDS, delta handling in this context is managed by the source system (SAP HANA) rather than

the target system.

Key Points About ODP Contexts:

ODP_BW:

Delta queue is managed by the target system (SAP BW/4HANA).

Suitable for scenarios where the source system does not support delta management or when the

target system requires more control.

ODP_SAP:

Delta queue is managed by the source system (e.g., SAP ERP).

Default context for SAP source systems.

ODP_CDS and ODP_HANA:

Delta handling is managed by the source system (SAP HANA or S/4HANA).

Reference to SAP Data Engineer - Data Fabric:

SAP Note 2358900 - Operational Data Provisioning (ODP) in SAP BW/4HANA:

This note provides an overview of ODP contexts and their respective delta handling mechanisms.

SAP BW/4HANA Data Modeling Guide:

This guide explains the differences between ODP contexts and how they impact delta management

in SAP BW/4HANA.

Link: SAP BW/4HANA Documentation

By understanding the ODP context, you can determine how delta records are managed and ensure

that your data extraction processes are optimized for performance and accuracy.

SAP S/4HANA Embedded Analytics relies on the ABAP CDS (Core Data Services) view-based Virtual

Data Model (VDM). This foundation provides a unified layer for data consumption directly from

transactional data in the S/4HANA system.

ABAP CDS Views as Foundation:

CDS views define the semantic model for data and integrate seamlessly with SAP S/4HANA.

These views allow users to build advanced reporting and analytics without requiring external data

movement.

Virtual Data Model (VDM):

VDM provides a structured framework of CDS views optimized for analytics and reporting.

It includes analytical, transactional, and consumption views tailored for SAP Analytics tools.

Reference:

SAP Help Portal – S/4HANA Embedded Analytics Overview

SAP Learning Hub – ABAP CDS View Basics

In SAP BW/4HANA, a Data Transfer Process (DTP) is used to transfer data between source and target

objects. The source objects for a DTP must be compatible with the DTP's functionality, which includes

extracting, transforming, and loading data. Below is an explanation of the correct answers:

A . DataStore Object (advanced)

A DataStore Object (advanced) is a flexible and powerful object in SAP BW/4HANA that stores

detailed data for reporting and analysis. It can serve as a source for a DTP because it supports both

inbound and outbound data flows. Data from a DataStore Object (advanced) can be extracted,

transformed, and loaded into other objects such as another DataStore Object, InfoCube, or

Composite Provider.

Reference : The SAP BW/4HANA Modeling Guide confirms that DataStore Objects (advanced) are

fully supported as sources for DTPs, enabling seamless data integration.

C . InfoSource

An InfoSource acts as an intermediate layer between data sources and targets in SAP BW/4HANA. It

consolidates data from multiple sources and provides a unified structure for data transfer.

InfoSources can be used as sources for DTPs, especially when data needs to be transformed or

enriched before being loaded into a target object.

Reference : The SAP BW/4HANA Data Modeling Guide highlights that InfoSources are commonly

used as sources for DTPs to facilitate data transformation and consolidation.

Incorrect Options

B . Open ODS view

An Open ODS view is designed to provide direct access to data stored in SAP HANA tables or external

sources. While Open ODS views are useful for real-time reporting and analytics, they cannot serve as

direct sources for DTPs. Instead, they are typically consumed by queries or Composite Providers.

Reference : The SAP BW/4HANA Modeling Guide explicitly states that Open ODS views are not

supported as sources for DTPs.

D . CompositeProvider

A CompositeProvider combines data from multiple sources (e.g., InfoProviders, Open ODS views, or

HANA tables) into a unified structure for reporting. However, CompositeProviders are not designed

to act as sources for DTPs. They are primarily used for querying and reporting purposes.

Reference : The SAP BW/4HANA Query Design Guide confirms that CompositeProviders are not

supported as sources for DTPs.

When deciding between SAP HANA modeling and SAP BW/4HANA modeling , it is essential to

consider the specific requirements of the use case. SAP HANA modeling focuses on leveraging the

native capabilities of the SAP HANA database, such as advanced analytics, SQL-based development,

and real-time processing. In contrast, SAP BW/4HANA modeling is better suited for structured data

integration, harmonization, and reporting scenarios that require predefined data models and

governance.

Correct Answers:

Finding the best match using a fuzzy search (Option A):

SAP HANA provides advanced analytical capabilities, including fuzzy search , which allows you to find

approximate matches for text-based data. This feature is particularly useful for scenarios like name

matching, address validation, or duplicate detection, where exact matches are not always possible.

Fuzzy search is a native capability of SAP HANA and can be implemented directly in calculation views

or SQL scripts.

While SAP BW/4HANA can integrate with SAP HANA for such functionalities, it is more efficient to

implement fuzzy search directly in SAP HANA modeling to take full advantage of its performance and

flexibility.

Leveraging SQL in-house knowledge (Option C):

If your team has strong expertise in SQL and prefers to work with SQL-based development, SAP

HANA modeling is the better choice. SAP HANA supports SQL scripting and development natively,

allowing developers to create complex logic, transformations, and calculations directly in the

database layer.

SAP BW/4HANA, on the other hand, uses a more structured modeling approach (e.g.,

transformations, DTPs) that may not fully leverage SQL skills.

By focusing on SAP HANA modeling, you can maximize the use of in-house SQL expertise while

maintaining high performance and flexibility.

Why Other Options Are Incorrect:

Loading snapshots or deltas from different sources on a periodic basis (Option B):

This requirement is better suited for SAP BW/4HANA modeling. SAP BW/4HANA provides robust

data integration capabilities, including Data Transfer Processes (DTPs) and process chains, which are

specifically designed for loading and managing data from multiple sources. These tools offer built-in

error handling, scheduling, and monitoring features that simplify periodic data loads.

Reporting on a harmonized set of master data (Option D):

Reporting on harmonized master data is a core strength of SAP BW/4HANA. SAP BW/4HANA excels

at integrating, cleansing, and harmonizing data from disparate sources into a unified model. It also

provides features like hierarchies, key figure calculations, and query design that are optimized for

reporting. SAP HANA modeling, while powerful, does not inherently provide the same level of data

governance and harmonization capabilities.

Key Points About SAP HANA vs. SAP BW/4HANA Modeling:

SAP HANA Modeling Strengths:

Real-time analytics and advanced algorithms (e.g., predictive analytics, graph processing).

Flexibility for ad-hoc queries and custom SQL-based logic.

Native support for advanced search features like fuzzy search.

SAP BW/4HANA Modeling Strengths:

Structured data integration and harmonization.

Predefined data models and governance frameworks.

Optimized for enterprise-wide reporting and analytics.

Reference to SAP Data Engineer - Data Fabric:

SAP HANA Advanced Analytics Guide:

This guide explains how to use SAP HANA's native capabilities, including fuzzy search and SQL

scripting, for advanced analytics.

Link: SAP HANA Advanced Analytics

SAP BW/4HANA Data Integration Best Practices:

This resource highlights the strengths of SAP BW/4HANA in data integration, harmonization, and

reporting scenarios.

Reference: SAP Note 2637890 - Best Practices for Data Integration in SAP BW/4HANA.

By choosing SAP HANA modeling for requirements like fuzzy search and SQL expertise, you can

leverage the database's native capabilities and flexibility, ensuring optimal performance and

alignment with your team's skill set.