Consuming HANA Models in BW, New breed of info Providers

BW on HANA

We have 4 ways to make use of HANA data in BW.
1.       Virtual providers
Virtual providers which are actually meant for real-time reposting based on data from OLTP systems directly (the whole data loading into Datasource, DTP execution and loading into infosource happens just right when query is executed in real time) can be used for consuming analytic/calculation views in BW.
When we create virtual providers we have new option “Based on HANA”, which will ask you to input schema name and model name. Once model is selected then we start with modelling process in workbench. We have to select characteristics, key figures for our virtual info provider. Then we can map these to fields from HANA model (F4 help is provided). Here by providing BW Infoobjects will can make use of BW master data while reporting.  That’s all and virtual provider is ready for consumption using reporting tools.
2.       Transient providers
This infoprovider is actually meant for ad-hoc reporting by users on OLTP system directly. For these infoprovider, BW modelling is not required hence no mapping of infoobjects; also these IP can’t be transported. There are best suitable for ad-hoc reporting where in model scenario changes frequently.
        We will use this IP to publish HANA models to SAP BW. As this process is very simple, users can these IP easily and they run faster as data resides in HANA. When a model is published, its structure is read and an analytic index is created. Based on this analytic index, transient provider and consequently reporting can be done.
      Go to Tcode RSDD_HM_PUBLISH. Select model from any schema. You can assign characteristic, key figure info objects. If you want to make use of BW master data. Once an analytic index is created, corresponding IP name will be displayed.
3.       Composite provider
Composite provider can be used to combine data from SAP BW schemas and HANA models. We will build analytic index/transient providers and BW cubes initially.
Then go to Tcode RSLIMO and start creating composite IP. Select Analytic index and Cube and Drop onto pane. Say ok to “join and Union”. You can select currency from characteristic to refer to a key figure. Then composite IP is ready for reporting. You can also select UNION with constant value for an attribute, however in this case UNION accepts only two input parameters.
4.       Loading HANA via DBCONNECT into BW

If treat HANA as source system for BW (your BW system might be running on another DB also) then we can create DBCONNECT connection to HANA and can create Datasources against models in any schema. Then we can load data into BW from any model. We can create virtual providers to make used HANA data or design data flows using DSO as normal. In other words we are treating HANA as source system like ERP, CRM etc. in this scenario.

1.       Making use of HANA in various scenarios
If an ERP system is running in HANA or SAY BY Design running in HANA, then we can use ODP framework to extract data from ERP to BW. Also master data for infoobjects cane be loaded from attribute views. By making use of HANA data provisioning capabilities like SLT, SAP BODS we can get data in real time/ from heterogeneous sources and load eventually into BW for modelling.
If your BW system is running on HANA and ERP system is running on some other database, we can connect HANA as secondary database and replicate data into HANA at regular intervals. By setting this up, we can push all data intensive/slow response reports to hit HANA DB making TIME-OUT errors, a no possibility in ERP system.
Hence if HANA is used anywhere in your landscape, you can still make use of it. 

Comments

Post a Comment

Popular posts from this blog

SAP V1 V2 V3 JOBS

Image
Before starting with V-series jobs, lets review some fundamentals. Data base LUW:  It consists of sequence of DB operations(INSERT,UPDATE,DELETE) that needs to be committed to either all or none to keep DB in consistent state. COMMIT will be executed when all DB operations were successfully executed, once COMMITTED these changes can't be ROLLED BACK. This is part of traditional ACID property of any RDBMS. If a failure happens while executing the LUW(will be mostly technical like memory crunch,integrity violation or data base crashed,power failure) and when DB is restarted, DB will be ROLLed BACK to previous state. Which is nothing but the state when last COMMIT was happened. In other words, when ROLL BACK happens DB will be safely brought to previous COMMIT state. DB maintains log of actions performed since last COMMIT and as part of recovery process, all these actions will be reversed. All RDBMS will have in-built locking/multi concurrency control such that dirty READ-WRITE ope...
Read more

Virtualization with SAP HANA

Image
Virtualization Most of Corporate Enterprise warehouse Eco system consists multiple disparate  Data warehouses. reason can be attributed to mergers and acquisitions,  departments using specialized software, databases choose to support specific data needs. All these scenarios result in building multiple data marts over multiple technologies. However, to serve the purpose of single source of truth, we end up building Enterprise Data Warehouse(EDW)  by sourcing data from various data marts across organization. One way of doing is, running a project for establishing standard ETL methods to extract data from regional Data Marts and staging data into EDW. However various departmental/ regional DW already contains highly formatted, information rich and aggregated data built readily available. This implies that we might not need to 1. Perform extensive transformations 2. Source data is meta data rich 3. Less data volumes will be retrieved from source systems Following trad...
Read more

Star Join node Vs Analytic view && Attribute View vs Calculation Dimension View

Analytic views comes with constraint that Measures should be coming from a single of Data foundation, although data foundation could contain any number of tables joined. Previously, we used model multiple Analytic views and combine them using JOIN node of an calculation view, in order to facilitate to Measures from multiple tables.  Star Join solves this by allowing measures coming from multiple tables. So we don't have to use multiple Analytic view to achieve this.  However Analytic views comes enriched with master data through Attribute views. To compensate this, We have Calculation Dimensional Models introduced. These artifacts are same as  Attribute views, but can only be used in a Star Join. So this solves out Master data enrichment issue while using Star Join. Introduction of Star Join Node and Calculation Dimensional Models  certainly introduces more uses cases for Calculation views and avoids scenario of creating multiple Analytic views for e...
Read more