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Shared (with permission) from my old friend Mico Yuk – the champion of data storytelling mastery, this is a link to learn about how you can make a difference within your organization in helping everyone more productively gain actionable insights.

BI Brainz Data Storytelling Mastery

SAP has frequently changed, improved and added front-end tools for BI, SEM, Business Objects or other reporting and development. Their documents provide menu navigation and feature guidance, but not always how best to administer, rollout and support the tools or when to use – or not use – certain features. The guides sometimes miss details important for helpdesk support, like where in the registry or file system logs and settings are. This guide helps clarify issues we have experienced that are not clear from online searches.

CDS Views backgrounder

(A SHORT CONCEPTUAL INTRODUCTION)

This guide should provide terminology and a conceptual framework for your development with some actual screens. Its not meant to be comprehensive – and as always, this technology changes. Its been put together from multiple sources and is not polished. (05/20)

Core Data Services Views

Core Data Services Views can be ABAP CDS views – created in an SAP (ECC or S4) system and enabled for delta extraction – annotated with business logic or created at the hana database level as a ‘hana information view’.

CDS is defined using an SQL-based data definition language (DDL) leveraging standard SQL with some additional concepts, such as associations, which define the relationships between CDS views, and annotations, which direct the domain-specific use of CDS artefacts. There are two types of CDS available, the HANA CDS (defined in XS engine) and ABAP CDS

These can be materialized or unmaterialized views of data in remote systems.

They can be published and consumed as an OData service

ABAP CDS views are transportable database objects and have or can generate:

• ABAP Structure
• Database structure
• OPEN SQL structure
• OData service

ABAP CDS views are stored in the data dictionary and use OPEN SQL – goal is to create views for analytics

HANA CDS views reside at the data base level in HANA XS (extended application services) and use Native SQL. Their goal is to create reusable models

Use case in S4 Embedded Analytics:

Views are classified as interface or consumption views. Interface views can be basic or composite. Composite views can be built on other composite or basic views to create VDMs or virtual datamarts which are then published and called consumable views and operate as a rubiks cube to answer business questions for related content.

WHY VDM AND CDS IN SAP S/4HANA?

Understandability of data: VDM = Business Semantics

•  View and field names, entity relationships, semantic annotations

Visibility and Reuse of Views

•  SAP-internal reuse and release to customers and partners

Principle of ONE: Model once-use everywhere

•  VDM model is leveraged in transactional apps, analytics, APIs, extension apps…
•  No need to develop framework-specific models

Execution excellence: run in SAP HANA

•  Capitalize on SQL and native SAP HANA features

Integration in the robust ABAP development environment

• Access via ABAP SQL, integration with DDIC, development tools, transports
• Authorizations, extensibility –

VDM View Stacks

HANA Information views also have several types and are built or configured as live delivered content to support analytical applications. Many delivered views are present to provide backward compatibility for traditional extractors – these are called compatibility views

LEVERAGE ABAP CDS VIEWS

Available fiori and embedded analytics content can be activated and
utilized in S4 to support reporting VDMS

See /review list in ‘Available ODP and hana views CAR and S4.xlsx” file

SAP Note 2500202 – S4TWL-BW Extractors in SAP S/4HANA

• (Version 24 from Apr 17, 2019) Lists available 1709 extractors and structures
•  Asset Management, Customer Service, Financials, Master Data, Sales, Supply Chain, Retail may be applicable. This and attached notes state what has been replaced by CDS views or deprecated.
•  Many new compatibility views can be leveraged by Gulfstream to incorporate condensed data delivery from SAP business logic – example 0FI_ACDOCA_20 (Unified Journal Entry with currency type) note 2341038 explains for this source. Sales and Billing – 2499568 – Release Information Note for BW extraction in S/4HANA Sales as part of S/4HANA on Premise edition 1709 and above

Hana CDS views can be classified as basic or composite– and when creating in hana, a materialized table needs to be defined as column or row store.

CORRECT USE OF TABLE TYPES

SAP HANA offers two storage options for tables, columnar and row-based, that are optimized for different use cases. SAP recommends adhering to the following modeling principles in SAP HANA:

•  Use columnar tables for analytical applications like SAP BW
•  Use row-based tables for transactional applications (like SAP ERP)

SAP recommends the use of columnar table structures for fact and lookup tables for optimal performance.

There are three types of hana CDS views – each with a different function but similar to past BW structures

Attribute views (think master data), Analytic views and Calculation views display differently in the hana admin perspective in eclipse.

ATTRIBUTE VIEWS – BEST PRACTICES

Attribute Views

Act as Hierarchical Lookup Data in SAP HANA

Attribute views are used to model an entity based on the relationships between attribute data contained in multiple source tables. For instance, a customer dimension view could be created from the join of the LU_CUSTOMER, LU_CUST_CITY, LU_CUST_GENDER, LU_CUST_EDUCATION, etc. tables. These are exposed, nonetheless, as one single view with all customer dimension columns.

Best Practices around using Attribute views

If more joins are defined in an attribute view, performance could be “impacted” (which might be negligible or not depending on the model, the amount of data, or the actual number of tables that need to be joined to fulfill a specific query).

Create Attribute views based on the business need. For example, if a material entity requires material type and other types of information for most business domains, it makes sense to create a holistic abstraction model of material that includes material and other material information lookup “tables”.

ANALYTICAL VIEWS – BEST PRACTICES

Analytical Views

Build the Data Foundation of a Star Schema in Memory

Analytical views provide access to data in a logical structure that supports OLAP analysis. They are used for analytical purposes where reading operations in mass data is required. According to SAP, analytical views have a high performance on SELECT and are well optimized.

In general, analytic views are used to model data that includes measures (or facts). An analytical view definition could consist, for instance, of a central fact table, ORDER_HEADER, joined with the customer dimension attribute view, other lookup tables (such as LU_EMPLOYEE) or any additional attribute views. The end result is a view that is exposed as a “denormalized” ORDER_HEADER table with all the dimensional information attached to the fact table. This is almost equivalent to a cube (and therefore the OLAP analysis reference for analytical views) that has all dimensional and factual information; to get relevant information around the central fact table, joins with already considered lookup tables are not necessary in the SQL, as these are factored in the analytical view definition already.

Best Practices around using Analytical views

Analytical views are the best in terms of performance of all modeling views to aggregate fact data. These may be potentially the most commonly used modeling views by customers (to aggregate metric data, as opposed to calculation view).

CALCULATION VIEWS – BEST PRACTICES

Calculation Views

Provide Advanced Slices of Data Using Analytic and Attribute Views

Calculation Views are used to provide composites of other views. They can be used in the same way as analytic views. In general, calculation views allow modeling more complex calculations that involve some kind of data processing (whether joining, uniting or even doing statistical analysis) on one or several data structures (tables or other information views). Calculation views can have layers of calculation logic, can include measures sourced from multiple source tables and can include advanced SQL logic. The data foundation of the calculation view can include any combination of tables, column views, attribute views and analytic views. It is possible to create joins, unions, projections, and aggregation levels on the sources.

Overall, calculation views are superior to other views in the sense that since calculation views are built on top of OLAP engine and/or join engine, calculation views can achieve complex calculations that cannot be achieved using the Attribute Views and Analytics Views. This also makes them slightly less performant.

Best Practices around using Calculation views

Use Calculation views to do advanced processing of data: joining/uniting multiple facts/analytical views, incorporating external data processing or using stored procedures for data processing (analytics libraries).

Calculation views are more costly in terms of performance due to additional processing by the calculation engine; moreover, data travels from the underlying views to the different processing steps in the calculation engine.

Overall, the calculation engine in SAP HANA adds overhead (which might be negligible, depending on the model and amount of data).

Design calculation views by enabling the transfer of the least amount of data between the calculation nodes (for example, do first a filtered projection before doing a union).

When designing calculation views, use UNION operation over JOIN. Join is more costly in terms of performance.

INFORMATION VIEW BUILDING BLOCKS

Attribute, analytical and calculation views all have the same basic elements in their definition; these elements, in SAP HANA’s terminology, are explained below.

Attributes and measures

In essence, attributes and measures represent individual columns. They form the content data that is used for modeling. Attributes represent the descriptive data like city and country; measures represent quantifiable data like revenue and quantity sold. Measures provide settings that enable currency or unit conversions, among other things in the SAP HANA model.
• Calculated columns (similar to compound metrics or formula-based attribute forms) allow defining new “derived” columns based on a formula, other columns or input parameters.

• Calculated attributes/measures are exactly the same as calculated columns.

The difference relies on the view where they are created. Calculated attributes, as of SPS5, appear only in calculation views; the difference is that calculated attributes offer two input expressions: one for the ID of the attribute and another for the description. Calculated columns simply represent one additional column.

ABAP CDS views – as delivered – more mimic traditional data extractors and structures

The analytics model is based on VDM and completely modeled in CDS

Leverages both HANA and the ABAP analytic engine

Enables multi-dimensional analysis with advanced functionality, e.g.

• Variables
• Restricted measures
• Formula aggregation
• Exception aggregation
• Hierarchies

Can be consumed via HANA and OData protocols

Delivered or created ABAP CDS views can be enabled as an ODP (Operational Data Provider) and consumed either by an ODP/ODQ subscriber or as a direct OData service.

Good primer online class (free) at UI5 Community network:

These also include newer concepts like AMDP (ABAP Managed Data Procedures)