SAP HANA interview preparations Day 2

Data Provisioning

Important links

https://blogs.sap.com/2016/07/05/the-various-data-provisioning-options-for-hana/

https://www.guru99.com/data-provisioning-sap-hana.html

https://blogs.sap.com/2014/11/27/hana-sp9-data-provisioning-overview/


Major ETL Tools

BODS/  SAP Data Services

SDA

SLT

SDI

BODS

works with any source and any target, most widely used ETL tool. It focuses on Batch performance, connectivity to any source and any target also provides fairly complex transformations.

Project Use

Source: flat files of two type delimited and fixed width
Target : SAP HANA tables (columnar ) called landing layer

Transformation : mostly 1 to 1 direct mapping, cleansing like removal of trailing white spaces, leading zeros, junk characters .

Structure :

JOBS -  Workflow - Data flow - Pre-processor - Post processor


SDA

(Smart Data Access) enables use of virtual tables in HANA,  These are HANA objects that point to an external table and provides data remotely to HANA, does not store any data in HANA. It is just a pointer to a remote table.

Project Use: Get ECC reference data like province code, activity code, contract treatment code etc.

How to :

Creating virtual table
                Right click, -> new virtual table
                Browse -> select table

Creating Data sources
               Provisioning-> Remote Source -> Create new remote Source
               Provide connection details
               Run


Connection managed by separate team

SLT/SDI not used


SDI
 Smart Data Integration, SAP built all in one solution for all data provisioning needs
Any source target is only HANA
 Supports batch and real time
allows transformation

SLT SAP Landscape Transformation
 Trigger based Data replication from any source  to HANA
Real time
Source ECC
Target HANA

Data Science Voyage Notes - 1

I have started with Udacity course on Problem Solving with Advanced Analytic.

Today I completed lesson 1 of this course on the Analytical Problem Solving Framework.

This course introduces the CRISP-DM methodology or the Cross Industry Process for Data Mining, initially built for data mining problem but useful for all types of business problems. The framework used in this course is generalized from CRISP-DM.

This framework uses a six step process for problem solving 

1. Business Issue Understanding
2. Data Understanding
3. Data Preparation
4. Analysis/ Modelling
5. Validation
6. Presentation

Interview preprations SAP HANA Day 1

Questions :

Q 1 : Difference between Catalog and Content folder in SAP HANA Studio project explorer.

The Catalog View gives a view on the runtime objects of the database artifacts. The first level you see under this view is the schema to which the runtime objects are assigned. In case you create e.g. a table using DDL statement CREATE TABLE you see created runtime object for the table under the schema which was used in the statement (but the table is not visible in the Content view).

The Content View gives a view on the design time objects of e.g. database artifacts (and further static content) stored in the HANA repository (therefore the objects are also called repository objects). Under the Content View the design time objects are organized in packages (1 to n levels). The activation of a design time object for a database object leads to the creation of the runtime object in the specified database schema. So if you create e.g. a table via an hdbtable artifact (e.g. mypackage.test.TestTable.hdbtable) and activate it you can see in the Content view the design time object "TestTable.hdbtable" in package "mypackage.test" and in the Catalog view under the schema you defined in the design object the runtime object "mypackage.test::TestTable".

source : https://archive.sap.com/discussions/thread/3845399

Q2:  Difference Between HANA Modeler and Developer

Modeler perspective 

You can model entities in SAP HANA using the Modeler perspective, which includes graphical data modeling tools that allow you to create and edit data models (content models) and stored procedures. With these tools, you can also create analytic privileges that govern the access to the models, and decision tables to model related business rules in a tabular format for decision automation.

You can create the following types of information views:

● Attribute Views 

● Analytic Views 

● Calculation Views

Task that can be performed using Modeler Perspective 

Task	Meaning
Import metadata	Create tables by importing the table definitions from the source systems using the Data Services infrastructure.
Load data	Load data into the table definitions imported from the source system using the Load Controller, SAP Sybase Replication Server or SAP Landscape Transformation, and from flat files.
Create packages	Logically group objects together in a structured way.
Create information views	Model various slices of the data stored in the SAP HANA database. Information views are often used for analytical use cases, such as operational data mart scenarios or multidimensional reporting on revenue, profitability, and so on.
Create procedures	Create procedures using SQLScript for implementing a complex logic that cannot be achieved using other objects.
Create analytic privileges	Control which data that individual users sharing the same data foundation or view can see.
Import SAP BW objects	Import SAP BW objects into SAP HANA, and expose them as information views.
Create decision tables	Create a tabular representation of related rules using conditions and actions.
Import and export objects	Import and export the content objects from and to the client and server location.

Development Perspective 

Used for programming applications, that is, creating development objects that access or update the data models, such as server-side JavaScript or HTML files.

Provides views and menu options that enable you to perform all the tasks relating to application development on SAP HANA XS, for example: to manage application-development projects, display content of application packages, and browse the SAP HANA repository. You can also define your data-persistence model here by using design-time artifacts to define tables, views, sequences, and schemas.