Provisioning & replication in SAP Hana

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Published Date

22nd September, 2020




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Provisioning & replication 

It is nothing to bring data from different sources like SAP & NON SAP system. There are three Replication types in HANA

  1. Log-based
  2. ETL
  3. LT (trigger-based)

  The main purpose of the above replication type to bring data from the different source system to the tables under catalog:

  We can also bring data from the data store in data services – by using provisioning as well as we can bring tables to form the different system by using provisioning (LT Replication).


(A) Modeling at HANA STUDIO (administrator) developer):-

  • Schema is to organize columns views, tables a vies, & procedures.
  • Every object comes under ‘schema’ only
  • For HANA modeling we need to follow Data warehouse concepts like MDX, ER-Model.

1.Creating views:

(a) for creating view right-click on the table select new view, in the data foundation pane add multiple tables based on our Requirement (b) Join multiple tables by using inner, outer (Lift, Right & full) Joins. (c) Select Require column in column pane & activate  

2.Creating column views:

There are three types of views:

1) attribute

2) analytic Views

3) calculated  

1) Attribute view:-

  • Right-click on the attribute, create new attributes, select required tables
  • & Join them data foundation
  • Drag required field from tables and make at least one attribute as a key attribute
  • Create a customized attribute based on our requirement as well as (level based & parent-child)

 2) Analytic view:-

  • Right-click on packages - - > Create new analytic
  • & select the required table (at least one fact table)
  • & select Required attribute views
  • If we take multiple tables Joins them in the data foundation pane
  • Select the required fields from the table as attributes and measures.
  • Create custom calculated attributes, calculated measures & the variables restricted measures.
  • In Logic – view pane connect our data foundation table to selected attributed view tables
  • & activate

 3) Calculated views:-

  • Right-click on package   - - >    create new calculate view
  • Sele cam create two types of calculated views based on DRU requirements
  1. Graphical &
  2. Script


  • Select the required tables
  • Select attribute, calculate & analytical views based on our requirement.
  • Connect tables views, by using union, projection, Join (inner, outer, Left, Right, full) joins
  • And connect them to output structure clarity objects as attributes, measurer
  • Create custom measures like calculated measures & variables


  • Provide code for selecting fields from tables by SQL statements in the script pane
  • & create output parameters as well as variables
  • In the output, component classify objects as measures and create custom variables and
  • Activate

 4.Creating Analytical Privileges:-

  • The main purpose is to create user, roll access permissions on information models (views)
  • Select views
  • Select objects in selected views
  • Select data in selected views
  • Provide to uses &
  • Activate

Checkout Our  Frequently asked SAP Hana Interview Questions

5.Creating procedures:-

  • By default system coil curate produce for each script for calculated vies
  • even we can create a customized procedure
  • for a procedure, we need to procedure script

 (b) modeling at information composer:-

  • an information composer is a tool for creating at the client level
  • The main actions in Information composer on i) uploading excel files into HANA DB ii) creating an information model (calculated view)

  NOTE:- whenever we upload an excel file into the HANA system will create a table That table will appear under ‘IC – tables” catalog   NOTE:- Whenever we create models at information composer those views appears under “ _ sys – BIC” catalog  

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1.Uploading data into the database

  • We need to upload excel ( .xls, .xlsx .csv) files.
  • After uploading we need to perform cleansing:- Merging USA United States of America. As a single file.
  • Classily measures & attributes ( the column which contains number as data type)

2.Modeling (creating information models)

  • Select source A (Table Uploaded file), analytic, attribute, calculated)
  • Select source B (Table Uploaded file), analytic, attribute, calculated)
  • Connect source A & B, By using union, join (inner, Left outer Join, Right / full out Join)
  • Create custom Colum (attributes/measures)
  • And manage custom – columns & activate


He can give access levels, roles & users by providing

  1. SQL privileges
  2. Analytic Privileges
  3. System Privileges
  4. Package privilege

NOTE:- Whenever we provide analytic privileges to uses, that user can access, for specified data in specified views.  

NOTE:- Users in HANA & users in Business objects in the same  


We can have two types of end-users

1) Excel client

2) Business objects enterprise  

1) Excel Client:-

We can generate reports in the form of excel file to bring the data from tables & views  


We can bring data from only analytic & calculate vies.

  • We can bring data form views by using a connection called “form connection wizard”

(Import data for unlisted format by using the data connection wizard and OLEDB)

  • We can bring data from tables by using connection “MICROSOFT QUERY” (import data for an unlisted format by using the Microsoft query wizard and ODBC

2) Business object enterprise:-

We can connect business object enterprise by using HANA DB ODBC connection to the semantic layers We have two of semantic layers:- 1) IDT (Information Design tool) 2) UDT (universe Design Tool)

  • For UDT, create connection b/w universe & HANADB, ODBC connection
  • Create universe bared on reports
  • For IDF, create connection b/w ODBC & TDT public connection into repository create data foundation & select tables/view from schemas, design structures, based on reports create business layers and select our foundation activate publish business layer and we can publish into the system repository
  • We can create four types of reports
  • Analysis
  • Explorer
  • web
  • Dashboards
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