SAP HANA Introduction
- SAP HANA - Core Architecture
- SAP HANA - Information Modeler
- SAP HANA - System Monitor
- Studio Administration View
- SAP HANA - Studio
- In-Memory Computing Engine
- SAP HANA - Overview
SAP HANA Modeling
- SAP HANA - Export and Import
- SAP HANA - Information Composer
- SAP HANA - Analytic Privileges
- SAP HANA - Calculation View
- SAP HANA - Analytic View
- SAP HANA - Attribute View
- SAP HANA - Packages
- SAP HANA - Tables
- SAP HANA - Data Warehouse
- SAP HANA - Modeling
SAP HANA Reporting
- SAP HANA - Excel Integration
- SAP HANA - Crystal Reports
- Bi 4.0 Connectivity to HANA Views
- SAP HANA - Reporting View
SAP HANA Security
- SAP HANA - Auditing
- SAP HANA - License Management
- SAP HANA - Authorization methods
- SAP HANA - Authentications
- User Administration & Management
- SAP HANA - Security Overview
SAP HANA Data Replication
- SAP HANA - MDX Provider
- SAP HANA - CTL Method
- SAP HANA - DXC Method
- SAP HANA - Log Based Replication
- SAP HANA - ETL Based Replication
- SAP HANA - Data Replication Overview
SAP HANA Monitoring
- SAP HANA - Log Configuration
- SAP HANA - High Availability
- SAP HANA - Backup & Recovery
- SAP HANA - Persistent Layer
- SAP HANA - Monitoring and Alerting
SAP HANA SQL
- SAP HANA - SQL Script
- SAP HANA - SQL Data Profiling
- SAP HANA - SQL Explain Plans
- SAP HANA - SQL Synonym
- SAP HANA - SQL Triggers
- SAP HANA - SQL Sequences
- SAP HANA - SQL Stored Procedures
- SAP HANA - SQL Expressions
- SAP HANA - SQL Functions
- SAP HANA - SQL Operators
- SAP HANA - Data Types
- SAP HANA - SQL Overview
SAP HANA Useful Resources
- SAP HANA - Discussion
- SAP HANA - Useful Resources
- SAP HANA - Quick Guide
- SAP HANA - Questions and Answers
Selected Reading
- Who is Who
- Computer Glossary
- HR Interview Questions
- Effective Resume Writing
- Questions and Answers
- UPSC IAS Exams Notes
SAP HANA - Schema in Data Warehouse
Schemas are logical description of tables in Data Warehouse. Schemas are created by joining multiple fact and Dimension tables to meet some business logic.
Database uses relational model to store data. However, Data Warehouse use Schemas that join dimensions and fact tables to meet business logic. There are three types of Schemas used in a Data Warehouse −
Star Schema
Snowflakes Schema
Galaxy Schema
Star Schema
In Star Schema, Each Dimension is joined to one single Fact table. Each Dimension is represented by only one dimension and is not further normapzed.
Dimension Table contains set of attribute that are used to analyze the data.
Example − In example given below, we have a Fact table FactSales that has Primary keys for all the Dim tables and measures units_sold and dollars_ sold to do analysis.
We have four Dimension tables − DimTime, DimItem, DimBranch, DimLocation

Each Dimension table is connected to Fact table as Fact table has Primary Key for each Dimension Tables that is used to join two tables.
Facts/Measures in Fact Table are used for analysis purpose along with attribute in Dimension tables.
Snowflakes Schema
In Snowflakes schema, some of Dimension tables are further, normapzed and Dim tables are connected to single Fact Table. Normapzation is used to organize attributes and tables of database to minimize the data redundancy.
Normapzation involves breaking a table into less redundant smaller tables without losing any information and smaller tables are joined to Dimension table.

In the above example, DimItem and DimLocation Dimension tables are normapzed without losing any information. This is called Snowflakes schema where dimension tables are further normapzed to smaller tables.
Galaxy Schema
In Galaxy Schema, there are multiple Fact tables and Dimension tables. Each Fact table stores primary keys of few Dimension tables and measures/facts to do analysis.

In the above example, there are two Fact tables FactSales, FactShipping and multiple Dimension tables joined to Fact tables. Each Fact table contains Primary Key for joined Dim tables and measures/Facts to perform analysis.
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