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Different modeling artifacts in SAP HANA

Before creating a new SAP HANA modeling artifact it is of paramount importance to know which types of modeling artifacts will be suitable for our business need. Depending on the scenario you are working in, your choice might vary. Factors that should be taken into consideration are:

  • The write- or read-intensive scenario, so as to decide between column and row storage
  • Data access need and resources for the replication method available
  • The application or client to be supported with your information model as different clients expose/consume data differently
  • Performance and functionality, as not all views can give the same functionality or performance

The first thing we need to decide is do we need a column table or row store. Once we have decided, we can have a glance at the following table and choose the views, if our requirement can be fulfilled by any of these:

Modeling principles

The following illustration shows the various considerations that we should be aware of and we should consider –avoid transfer of large result sets, and so on during HANA modeling. The idea is to filter the data at the lowest layer and only bring in data that is actually required by the query.

Modeling principles

Modeling Principles – consideration to make

The following decision tree can help us decide which view to use or what artifacts we should model. You can start with the very requirement and follow the decision tree flowchart to drill to what view you should use:

Modeling principles

A flowchart to help you to choose the appropriate view

When deciding which views will be most efficient for your information model, it is recommended to follow the decision tree as a guide to identify the views you want to create based on analysis of requirements. For the best performance and ease of maintenance, it is beneficial to stay on the top three levels of the decision tree whenever possible.

We will discuss view-specific recommendation in Chapter 4, Creating SAP HANA Artifacts Attribute Views and Analytical Views, when we discuss each view in detail.

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