SAP HANA ----DATA MODELING OVERVIEW

 SAP HANA - Overview and Architecture

SAP HANA - Overview and Architecture

HANA is High-Performance Analytic Appliance is an in memory appliance for SAP systems. Below are the notes/highlights of HANA for the webinar I attended recently.

Overview and Architecture of HANA

• What is HANA ? - In memory computing engine
• In memory computing studio as a fronend for modleing and administration. 
• HANA is connected ERP systems, Frontend modeling studio can be used for load control and replication server management 
• Two types of Relational Data stores in HANA : Row Store, Column Store 
• SAP BOBJ tools can directly report HANA 
• Data from HANA can also be used in MS Excel
• Row Store – Traditional Relational Database , the difference is that all the rows are in memory in HANA where as they are stored in a hard drive in traditional databases. 
• Column Store – The data is stored in columns like in SAP BWA 
• Persistency Layer: In memory is great by it is volatile and data can be lost with power outage or hardware failures. To avoid this HANA has a Persistencey Layer component which makes sure that all the data in memory is also store in a hard drive which is not volatile 
• Session Management: This component takes care of logon services 
• Two processing engines – Well, data is in memory which is good but How do I extract/report on the data? HANA has two processing engines one is based on SQL which accepst SQL queres and the other one is based on MDX .  
• HANA Supports Sybase Replication Server – Sybase Replication Server can be used for realtime synchronization of data between ERP and HANA 

Modeling Studio

Using Modeling Studio you can,

• Specify which tables are stored in HANA, first part is to get the meta data and then schedule data replication jobs 
• Manage Data Services to load the data from SAP BW and other 3^rd party systems. 
• Manage connections to ERP instances, current release does not support connecting to several ERP instances 
• Use Dataservices to for the modeling 
• Do modeling in HANA itself (This is independent of Dataservices).
• You can also do modeling can also be done in Business Objects Universes which is nothing but joining fact and dimensional tables.

Reporting

• Client tools can access HANA directly, Like MS EXCEL, SAP BI 4.0 Reporting tools, Dashboard Design Tool (Xcelsius)etc can also access HANA directly. 
• Third party reporting tools can leverage ODBC, JDBC and ODBO (for MDX requests) drivers in HANA for reporint. 
• HANA supports BICS interface

Request Processing and Execution Control

•  SQL Script, MDX statemenst are passed to calculation modles. Optiomizer which is included in caluculation engine optimizes for better performance.
•  Calc Engine :
•  Modeler can define data sources as inputs and different operations (join, aggreagation, projection) on top of them for data manipulation
•  The calc engine will break up a model into sub processes for optimized performance on cost based.
•  System will use maximum resources to achive max through put
•  Planning Enigne : Will be included in next release. Will include planning functions like distribute and copy functions.

ROW Store

• One of the relational engines to store data in row format.
• Pure in-memory store (Future versions will also have an option of disk based store)
• In memory object store (in future) for live cache functionality
• Transactions Version Memory is the heart of row store
• Row store architecture
• Write operation mainly go into "Transactional Version Memory"
• INSERT also writes to persisted segment
• Moves visible version from memory to persisted segment
• Clears outdated record versions from Transactional Version memory
• Row Store tables have a primary index
• Row ID maps to primary key
• Secondary indexes can be created
• Row ID contains the segment and the page for the record
• Indexes in row store only exist in memory
• Index definition stored with table meta

Column Store

• Improves read functionality significantly, also improves write functionality
• Highly compressed data
• No real files, virtual files
• Optimizer and Executer – Handles queries and execution plan
• Delta data for fast write
• Asynchronous delta merge
• Consistent view Manager
• Main store compressed and read optimized – Data is read from Main Store
• Delta Store – Write optimized – for write operations.
• Asynchronous merge move the data from delta store to main store
• Compression by create dictionary and applying further compression methods
• Even during the merge operation, the columnar table will still be available for read and write operations. To fulfil this, a second delta and main storage are used internally
• Merge operation can also be triggered manually with an SQL command

Persistence Layer

• Peristence Layer is needed as Main memory is volatile
• Provides Backup and Restore functionality
• One Persistency Layer takes care of both row and column stores
• Regular Save Points
• Logs capturing DB transactions since last save point
• Actions during system restart
• Last savepoint must be restored plus undo logs must be read and uncommitted atransactions saved with last save point and apply redo logs
• Complete content of row store is loaded into memory during start procees
• Flags can be set for column store to specify which tables are loaded during system restart

Modeling

• Modeling only possible for Column tables
• Information Modeler only works for column tables
• Replication servers create tables in column store per default
• Data Services creates tables in column store per default
• SQL to create column table: Create COLUMN TABLE
• Store can changed with ALTER TABLE
• System tables are create where they fit best
• Schema SYS -> chaces, administrative table of engine
• Tables from stastics server

In-Memory Computing Studio

• Build with java based eclipse
• Navigator to access different HANA systems on left, Quick Launch View at the middle and Properties view at the bottom.
• Information Modler Features:
• Database views
• Choice to publish anc consume at 4 levels of modeling
• Attribute view, analytic view ...
• Physical tables and Information Models
• Import/export models, data source shcemas, mass and selective load
• Landscapes
• The models are just virtual definitions they don't store actual data
• Analytic Views are like cube model where Transaction Data is connected to attribute view
• Calc View – With custom functions and calculations
• Modeling Process Flow
• Import Source System Metadata
• Create Information Models
• Consume using BICS, SQL or MDX
• Infromation Modeler Terminology
• Attributes – Characteristics
• Measure – Key Figures
• Attribute Views – Dimentions
• Analytic Views – Cubes
• Calculation Views – Similar to Virtual provider concept in BW
• Hierarcheis
• Leveled – based on multiple attributes
• Parent-child hierarchy
• Analytic Privilege – Security Object
• Navigation View
• HANA instance -> Hana srver name and instance numbe -> user database schema -> views functions and tables
• Information Modles – Attribute, Analytic, Calculation Views and Analytic Previlege
• Attribute View :
• Attributes add context to data
• Attributes are modeled using attributes views
• Can be regarded as Master Data Tables
• Can be linked to fact tables in Analytical Views
• A measure e.g. weight can be defined as an attributes
• Table Joins and properties
• Leftouter,rightouter, full outer or text table
• Cardinality 1:1, N:1, 1:N
• Language Column
• Content Views and Functions will be shipped with HANA
• Analytics View:
• Similar to Cube
• Analytic Views does not sotre any data. The data is stored in column store table or view based on Analytical View structure
• Attributes and Measures – Like key figures
• Data Preive – Similar to listcube functionality
• Calculation View:
• Define table outpu Structure
• Write SQL statement
• Ensure the selected fields correspons to previously defined output structes
• SQL Scripts unlike SQL procedure can't change any data they are read only