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SAP BW 7.3 on SAP HANA 1.0

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The column store uses efficient compression algorithms that help to keep all relevant

application data in memory.

Write operations on this compressed data would be costly as they would require reorganizing the

storage structure.

Therefore write operations in column store do not directly modify compressed data. All

changes go into a separate area called the delta storage. The delta storage exists only in main

memory. Only delta log entries are written to the persistence layer when delta entries are

inserted.

Delta merge operation:

The delta merge operation is executed on table level.

Its purpose is to move changes collected in write optimized delta storage into the compressed and read optimized main storage.

Read operations always have to read from both main storage and delta storage and merge the

results

The delta merge operation is decoupled from the execution of the transaction that performs the

changes. It happens asynchronously at a later point in time.

Delta merge is triggered by one of the following events:

Number of lines in delta storage for this table exceeds specified number

Memory consumption of delta storage exceeds specified limit

Merge is triggered explicitly by a client using SQL

The delta log for a columnar table exceeds the defined limit. As the delta log in truncated only

during merge operation, a merge operation needs to be performed in this case13

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EDW = DB + X

More than just a db. The purpose is to establish a well orchestrated set of powerful tools in order to:

- define a central EDW model that satisfies the need of decision makers across all areas of a

company and acts as a single point of truth for any kind of information

(Dataflow Modeler)

- define ETL processes to populate the persistency layers of the EDW Model with cleansed and

consolidated, consistent and harmonized data in an adequate timely manner (?) (periodicity) will say

periodically based on batch processes up to near-real or real time based.

(Transformations, DTPs, Source System handling, Realtime Data Acquisition (RDA))

- organize, schedule and monitor the dataflow towards and within the EDW and provide tools to

repair or redo unexpected failures during load processes.

(External ETL Processes, Metadata Management ,Process Chains, Admin Cockpit, generatingRepair Chains, manually check Error DTPs)

- Provide Data management capabilities in order to massage the data persistency according to the

specific characteristics of the data and information partitions such as actual, frequently asked data,

volatile data that is going to be updated very likely, old, read only data – with nearly no demand for

reporting, data that has to be hidden but kept for legal reasons

(BWA, BW-DB, NLS, Archive; Data Aging and Archiving strategies)

- Provide a technology for high performance OLAP processing on top of all parts of the data

resulting out of adequate modeling features (like Star Schema), particular persistency layers in themodel (granular vs. aggregated data resp. information) and sophisticated storage paradigms (Row

based vs. column based, RAM vs. disk vs. cheap mass storage)

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The main processes for the Inventory Management (for non-cumulative key figures) has moved to

the DTP

You need the following DTPs to manage inventory data

1.First, the DTP which loads the initial data set. This is indicated via the ‚Extraction Mode‘ of the

DTP

2.You need a second DTP for regular movements.

1. In case you want to load historic data you have to switch on the flag in this particular DTP in

the productive system

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Restrictions for HanaModels in BW

Providername

Limitation to catalog_name+cube_name+schema not longer than 63 (Trex API limitation)

The full model name incl. package must be <= 55 – this is because the column view must be <= 64

and the name of it for the ABAP API is _SYS_BIC:<package>/<name>.

Providername is as follows: ‘@3’ + (catalog_name+’_’+cube_name)/alias

ColumnNames

The column names must be upper case and <= 64 including suffixes .description and .currency. I.e.

an attribute with a description must be <= 52 and a measure with a currency conversion <= 55.

BW might use alias in the most cases

Infobject name is build as follows: Providername + ‘@’ + column_name/Alias

Upper-case

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The OSS message 0002987285 2011 should solve the following issue:

Currently lower case letters in a view‘s identifier (see example rkt2011/AVIEW001) are not

allowed.

This should be changed soon.

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For example,

SAP BusinessObjects Explorer is used by Molson Coors managers and analysts to access real-time

production, supply chain, and sales data in order to meet challenges of the beer business, a perishable, price-

sensitive product (beverage). SAP BusinessObjects Explorer allows the managers to manage inventory levels

more effectively and avoid stock outs, and the ability to validate market share more rapidly (from 1 day to 30

minutes).

SAP BusinessObjects Explorer

Not everyone in an organization is using BI today – typically about 15%. To address that we looked for

models of even greater simplicity, like the world of search. Think of how easy it is to find basic information

with Google. Maybe you don’t stop there, but it’s a great, simple way to begin an investigation. Shouldn’t BI

be that easy? At least to begin with.

We have now delivered on this vision.Starting with a search, and moving on to a highly-intuitive interface, users “walk” through the data, guided by

the product, to answer questions and gain insight.

By analyzing your existing BI infrastructure (“universes” for those of you familiar with the term), Explorer

understands the “shape” of the data and suggests paths through it, based on dependencies and relationships.

It knows, for example, that wine is classified by color before grape, that cars are classified by engine size,

and so on.

Explorer also adapts its visualization techniques based on the information being viewed, dynamically

choosing between pie charts, bar charts, bubble charts, and other visualization possibilities.

The majority of decision makers believe they do not have all the information needed to make good

decisions. Casual business users are not served by existing traditional BI tools, though they need access tocorporate data to do their jobs efficiently and to make informed decisions.

They are not power users or analysts who work on the data on a daily basis.

They don’t run queries like other BI users; they probably don’t understand the data structures.

BI tools available today do not serve their needs as they typically require training, understanding of analytical

methods etc.148

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For example,

SAP BusinessObjects Explorer is used by Molson Coors managers and analysts to access real-time production, supply chain, and sales data in order to meet challenges of the beer business, a perishable, price-

sensitive product (beverage). SAP BusinessObjects Explorer allows the managers to manage inventory levels

more effectively and avoid stock outs, and the ability to validate market share more rapidly (from 1 day to 30

minutes).

SAP BusinessObjects Explorer

Not everyone in an organization is using BI today – typically about 15%. To address that we looked for

models of even greater simplicity, like the world of search. Think of how easy it is to find basic information

with Google. Maybe you don’t stop there, but it’s a great, simple way to begin an investigation. Shouldn’t BI

be that easy? At least to begin with.

We have now delivered on this vision.Starting with a search, and moving on to a highly-intuitive interface, users “walk” through the data, guided by

the product, to answer questions and gain insight.

By analyzing your existing BI infrastructure (“universes” for those of you familiar with the term), Explorer

understands the “shape” of the data and suggests paths through it, based on dependencies and relationships.

It knows, for example, that wine is classified by color before grape, that cars are classified by engine size,

and so on.

Explorer also adapts its visualization techniques based on the information being viewed, dynamically

choosing between pie charts, bar charts, bubble charts, and other visualization possibilities.

The majority of decision makers believe they do not have all the information needed to make good

decisions. Casual business users are not served by existing traditional BI tools, though they need access tocorporate data to do their jobs efficiently and to make informed decisions. They are not power users or analysts who work on the data on a daily basis.

They don’t run queries like other BI users; they probably don’t understand the data structures.

BI tools available today do not serve their needs as they typically require training, understanding of analytical

methods etc.149

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Documents

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