SAP BW on HANA : Complete reference guide

Transcription

1 SAP BW on HANA : Complete reference guide Applies to: SAP BW 7.4, SAP HANA, BW on HANA, BW 7.3 Summary There have been many architecture level changes in SAP BW 7.4. To enable our customers to understand there is a need for complete overview about BW on HANA. Hence this article series will help you to explore BW on HANA and its unique features. It starts with BW, and then explores HANA, Finally explore BW on HANA. It gives a nutshell overview all important objects available in BW 7.4. This can be handy reference to major BW on HANA based project. Author: P. Manivannan Company: SAP Labs India Created on: 27 Aug 2015 Author Bio P Manivannan is currently working as BW Consultnat in SAP Labs India. He specializes in the BW Warehouse management area. Manivannan has very good knowledge in BW data modeling stratergies and reporting. He has been a working as lead in many end to end succesful SAP BW implementation across various industries. His overall expertise includes ABAP and other functional areas of SAP. He has also been in many complex CoE ( Center of Expertise ) projects in SAP Labs India for major global SAP customers. 1

2 Table of Contents SAP BW Overview... 3 The source of data for BW system... 4 Data flow in BI system... 5 SAP HANA Overview... 6 Exploring In-Memory analytics... 8 BW Powered by HANA The LSA and LSA++ layer LSA Layer An BW EDW with LSA is perceived as highly valuable but LSA++ Layer Open ODS layer EDW Data Propagation Layer in the LSA Architected Data Mart Layer in the LSA Virtual Data Mart Layer LSA to LSA SAP HANA Modeling in detail Data, View, Procedure and Analytic privileges overview Engines in SAP HANA BW on HANA Objects in detail Composite provider Open ODS InfoSets and HANA MultiProvider and HANA Advanced DSO New features in SAP BW Consuming HANA models in BW Transient provider Virtual provider Putting it all together, what will be the change!! Related Content Copyright

3 SAP BW Overview SAP BW is the name of DataWarehouse solution / Business intelligence / OLAP reporting product from SAP. It has various powerful features like flexible reporting tools, easy SAP ERP integration through the pre delivered business content across various application modules. It has planning functionalities and other essential features that made it one of the leading business intelligence tools in the market. The reporting, analysis, and interpretation of business data is of central importance to a company in guaranteeing its competitive edge, optimizing processes, and enabling it to react quickly and in line with the market. With Business Intelligence (BI), SAP NetWeaver provides data warehousing functionality, a business intelligence platform, and a suite of business intelligence tools with which an enterprise can attain these goals. Relevant business information from productive SAP applications and all external data sources can be integrated, transformed, and consolidated in BI with the toolset provided. BI provides flexible reporting, analysis, and planning tools to support you in evaluating and interpreting data, as well as facilitating its distribution. Businesses are able to make well-founded decisions and determine target-orientated activities on the basis of this analysis. The NetWeaver architecture and the positioning of BI in it is shown here. 3

4 The detailed BI Architecture can be seen here. The source of data for BW system Data can be extracted from SAP, Non SAP, DBMS, Web service and other flat files into BW to do analytical reporting. It can be seen here as below 4

5 Data flow in BI system When the data is extracted to BW, there are many layers to transform and cleanse the data as given below. InfoObjects: The basic building blocks in BW system and divided into characteristics & Key figures. Datasource: It is a structure which is created in the source system and replicated to the BW system InfoSource: A non-persistent structure consisting of InfoObjects for joining two transformations. Use PSA: Persistent staging area, it is the first inbound layer into BW Scheduler: It is used to schedule the BW extraction. It has options for Init, Full and delta extraction. InfoPackage: It is used to schedule a extraction from SAP Source system or Non SAP source system. Monitor: The data extraction status and result can be checked here. DSO: It is the InfoProvider that contains the data InfoCube: It is an InfoProvider which is modeled based in the extended star schema. DTP: It is used to extract data within the BW layer. Transformation: The transformation logic is done by this object and it exists between any two DTP Process chain: It is a BW tool to automate the extraction process Open hub destination: It is the object that allows you to distribute data from a BI system to non-sap data marts, analytical applications, and other applications Note: When you have any issues on the above warehouse management layer, raise an OSS incident in component BW-WHM-DST and its related fourth level sub component. The fourth level depends on BW object like DTP, Process chain etc etc. 5

6 SAP HANA Overview SAP HANA is an in-memory data platform that is deployable as an on premise appliance, or in the cloud. It is a revolutionary platform that's best suited for performing real-time analytics, and developing and deploying real-time applications. HANA is The Platform for Next-Generation Applications and Analytics SAP HANA converges database and application platform capabilities in-memory to transform transactions, analytics, text analysis, predictive and spatial processing so businesses can operate in real-time. By eliminating the divide between transactions and analytics, SAP HANA allows you to answer any business question anywhere in real time To learn more on HANA you can refer many official sites like hana.sap.com it is updated regularly.. What is the reason for performance degradation? To overcome the I/O bottleneck the platform is changed like this. This avoids the bottleneck between CPU and disks. The advantage of CPU Multicore, parallel processing helps in further performance gain. 6

7 So this helps to achieve all objectives equally broad and deep analysis along with real time and high speed computing put together in a simple method. That is the power of HANA. 7

8 So we get real time analytics of all real time application in a real time platform. This helps to transform customers business into a real time business. Exploring In-Memory analytics In Business intelligence (BI), in-memory analytics is a methodology used to solve complex and timesensitive business scenarios. It works by increasing the speed, performance and reliability when querying data. With SAP In-Memory technology there are various advantages as described below. It is a combination of real time analytics with speed and deep data. 8

9 SAP In memory database overview This combines the advantages of row store and column store technology. OLTP systems instantly record business events as they happen, such as the sale of a piece of inventory. As such, system designs focus on quickly handling large numbers of small, simultaneous transactions. OLAP systems provide analysis of the data provided by OLTP systems to support business decisions. They are designed to handle a relatively small number of often complex transactions. How data is stored in Row and column store database? In row storage, the data sequence consists of the data fields in one table row. In column storage, the data sequence consists of the entries in one table column. Columnar storage may be more efficient in the common case where a given column includes only a relatively Small number of distinct values. Consider this is as the table 9

10 How this table can be represented in Row and column store database? BW Powered by HANA This makes BW smarter, simpler and more efficient. Some of the advantages includes Alignment of database and application server to enable push-down of functionalities SAP HANA takes over processing of major data warehousing tasks Excellent performance in reporting and data loading Simplifies, increases agility, reduces complexity, and combines the strengths of SAP BW and SAP HANA In BW powered by HANA you can get the best of both worlds SAP BW contribution to architecture Manage complexity & semantics Govern the lifecycle SAP HANA contribution to architecture High performance on large data Maximum flexibility, agile modeling So how does the change look when you move to In-Memory based? 10

11 The LSA and LSA++ layer This is the part that you need to know importantly. This helps you to do effective data modeling in implementation projects. LSA Layer An BW EDW with LSA is perceived as highly valuable but Costly: building it, moving steadily data to and within the EDW Not flexible enough: EDW/LSA standards, central development, overall responsiveness to business needs (operational & agile BI) There was DTP and transformation, DSO and cube which were standard BW Objects. This layer was used prior to HANA 11

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13 LSA++ Layer LSA++ inherits the service definitions of LSA EDW Layers that stand for reliability & Consistency: EDW Transformation Layer (Link between source-model and EDW-model) (EDW) Corporate Memory (Persistent ) (EDW) Propagation Layer (Persistent ) Business Transformation Layer (Link between EDW-model and Data Mart model) Architected Data Mart Layer (Persistent 13

14 Open ODS layer In the LSA++ for BW on SAP HANA, the Open ODS layer is used to integrate data into the Data Warehouse. This offers the same functionality as the classic data acquisition layer, but with more flexible data integration possibilities. This layer encompasses the classic BW data acquisition layer, but it also offers more extensive and more flexible options for data integration: everything from consuming and combining to physical integration. This allows data sources to be consumed virtually: the data sources are made known to the BW by means of a modeling object named Open ODS view. They can be used directly in BW for query purposes, for example, without needing for separate BW persistence. These data models can be located in an external schema (of the SAP HANA database in BW), which is not managed by BW. Alternatively, the models can be located in other databases, which are connected to the SAP HANA database in BW using SAP HANA Smart Data Access. EDW Data Propagation Layer in the LSA++ In the LSA++, the data propagation layer consists of SAP HANA-optimized DataStore objects. This offers the following advantages: Greater flexibility due to faster activation times and loading times Flexible modeling Greater flexibility as all data is visible in the data propagation layer Greater flexibility due to queries directly on the data propagation layer Architected Data Mart Layer in the LSA++ In the LSA++, the data propagation layer consists of SAP HANA-optimized InfoCubes and/or SAP HANAoptimized DataStore objects (in the Business Transformation layer). Virtual Data Mart Layer If you are using an SAP HANA database for your BW system, the virtual data mart layer replaces the virtualization layer. The virtual data mart layer contains all InfoProviders that combine data using join or union, without saving the result: MultiProvider, Composite Provider access data directly in the SAP HANA database, to allow queries on the data: Composite Provider, Open ODS View Open ODS View Open ODS views enable you to define BW data models for objects like database tables. database views or BW DataSources (for direct access). These data models allow flexible integration without the need to create InfoObjects. This flexible type of data integration makes it possible to consume external data sources in BW without staging, combine data sources with BW models and physically integrate (load) external data sources by creating DataSources. Advanced DSO The DataStore object (advanced) consists of a maximum three tables: the inbound table, the change log and he table of active data. You might not need all three tables. This depends on how you want to use the DataStore object. 14

15 Composite provider In a Composite Provider, you can merge data from BW InfoProviders with data from SAP HANA views using union and join, or just merge data from BW InfoProviders and SAP HANA views. The Composite Provider's join and union operations are performed in SAP HANA. You can run queries on Composite Providers just like on all other BW InfoProviders. You acquire SQL access to the data if you generate a SAP HANA view from the Composite Provider. ODP It is a data provisioning aspect that allows you to eliminate PSA as a staging area. It enables extract once deploy many architecture. It has unified configuration and monitoring for all provider and subscriber types. 15

16 LSA to LSA Migrate to HANA optimized object 2. Streamline EDW core ( Reduce number of persistent layer) 3. Enhance virtualization layer (Like using composite provider etc) 4. Introduce additional ++ layer ( Open ODS, Agile data mart, Work space) 5. Resulting LSA ++ ready SAP HANA Modeling in detail We have below modeling terminologies in HANA Data View Procedure Analytic privileges Data, View, Procedure and Analytic privileges overview Data Here we have Attribute and measures Attributes: It is a descriptive data; it is like characteristics in BW. We also have calculated attributes Measures: Data can be quantified and calculated, It is like Key figure in BW We have calculated measures and restricted measures. View We have below View Attribute View Analytic view Calculation view Attribute View: If you see like BW, Attribute view is like dimension table, but the difference is that it can be reused and not struck up to a single model. This can be regarded as master data tables. It can be linked to fact table in analytic views Analytic views If you see like BW, Analytic view is compared to InfoCube (or) Infoset in a ERP. It contain once central fact table with transaction to report on with number of tables or attribute views. 16

17 Analytic view does not store data, it is read from join of tables Joins and calculated measures are done in run time. Calculation view This can be referred as combination of tables, attribute views, analytical views to deliver complex business requirement. This is similar to MultiProvider There are 2 types of calculation view Graphical calculation view (No SQL) Script calculation view ( SQL, CE Functions). Procedures this is functions which are reusable witting script calculation views. Analytic privileges It determines who has access to which report including restriction on row level data. Engines in SAP HANA OLAP Engine > this called when any query is run on Analytic view Join Engine > Used when you execute any Attribute view Calculation engine > this is used to execute Calculation view in SAP HANA Row engine > Does operation on row based table, Window function SQL Engine > Native SQL BW on HANA Objects in detail Composite provider If we need to join during query execution, before BW 7.30, we have below options MultiProvider > Based on Union operation Infoset > Based on Join operation But from BW 7.30, HANA Composite provider was introduced. Composite provider in nutshell: It is an InfoProvider; you can join InfoObjects, DSO, SPO (Semantically partitioned objects), HANA Views like Analytic or calculation views using Join (or) Union to make data available for reporting. The editor for composite provider is purely based on Eclipse which is shipped as a part of HANA studio It is NOT possible to create composite provider in BW Administrative workbench. Open ODS Open ODS enables us to integrate external data in your DataWarehouse without need to stage data first. It also offers the flexibility to enrich external data with OLAP functionality even though data is not in data warehouse. 17

18 When to use Open ODS? It is used in case when we have multiple non SAP Disparate sources and do not want data to reside in my DataWarehouse but we need to leverage the BW core capabilities like master data and OLAP aggregation Open ODS view is useful when you want to bring a DB table or View into BW system We can create open ODS view in BW and HANA system. When to use composite provider and Open ODS view? Composite provider is well suited in case if the information is already in place Open ODS view is suited in case the DB tables are still in raw format. InfoSets and HANA InfoSets are NOT optimized to use in combination with HANA DB. The reason for above is once you define Infoset in HANA DB the join logic is same like the traditional RDBMS, So there will not be much benefit in using HANA. The efficient approach is to push down the join execution to HANA DB level by defining a composite provider. MultiProvider and HANA There is no run time difference between composite provider and MultiProvider as analytic manager in BW is well optimized for HANA. Hence there is no need to migrate existing MultiProvider into composite provider. Advanced DSO Advanced DSO Consist of maximum three tables Inbound table Change log Table of active data Note: We might not need all three tables; it depends on how we model the DSO. Data flow in Advanced DSO Data is initially loaded in Inbound table The data is either read from inbound table directly or it is activated first and then read from table of active data The change log contains the change history for the delta update from DSO to other target Important settings in Advanced DSO Write change log the delta is saved in change log, this is used to extract delta. Keep Inbound data and extract from Inbound table No data is saved in change log and the extraction is read from inbound for delta Unique data records you can choose this if you only load unique data records All characteristics as key, reporting on union of inbound and active table Here all characteristics are included in key, system across union of inbound and active tables in query. You should only load additive delta 18

19 Data is aggregated This is compared to cube Inbound as extended table if you use Sybase IQ as extended storage for BW this can be choose. It means data is stored only in persistency layer. Smart data access Smart data access in a virtualization technique. SDA enables SAP HANA to combine data from heterogeneous sources like Teradata, Sybase IQ, and Hadoop etc. Smart data access is a technology which enables remote data access as if they are local tables in SAP HANA without copying into HANA. Data required from other sources will remain in virtual tables Virtual tables will point to remote tables in different data sources. It will enable real time data access; it will not affect SAP DB. You can write SQL queries in SAP HANA which could operate on virtual table. HANA query processor optimizes the query then extract relevant part of query in the target DB, Return the result of query to HANA and complete operation. 19

20 New features available in SAP BW 7.4 BW Modeling tool in eclipse ( New) Open ODS View ( New) Composite provider ( New) Generation of SAP HANA Views ( New) Virtual master data ( Enhanced) Open hub destination ( Enhanced) Transformation ( Changed) Master data maintenance ( New) DataWarehouse workbench ( Enhanced) DB Connect ( Enhanced) Replication of DataSource ( Enhanced) Transfer using ODP (Enhanced) BW Provisioning from SAP System with ODP (New) BW data mart with ODP ( New) SAP HANA smart data access ( New ) Remodeling ( Enhanced) Consuming HANA models in BW Transient provider If you want to use SAP BW OLAP functions to report on SAP HANA Analytic or calculation view, you can publish these SAP HANA models to SAP BW system. Useful transaction: RSDD_HM_PUBLISH. Note: Transient provider cannot be transported; it has to be created in each system. Key features of transient provider Its meta data in BW is not persisted but always generated at run time BEx queries built on top can adapt to change automatically as far as possible Navigational attributes of assigned InfoObjects cannot be used Cannot be used in MultiProvider Can only be used in composite provider in order to merge with other InfoProvider It is exposed to BEx and BI tools Virtual provider It is a type of InfoProvider which reads data from BW InfoCube. Its characteristics and key figure are created based on data field in the HANA Model and InfoCube is constructed using the characteristics as dimensions and key figures duly filled in. 20

21 Advantages of Virtual provider: When compared to transient provider here InfoCube acts as data provider which helps in multi-level reporting We can create virtual provider based on SAP HANA model. This can be used in MultiProvider. Navigational attributes can be used BW Analysis authorization apply This can be transported Data read across at query runtime is not through SQL but by analysis APU that read InfoCube, DSO. So we can push down operation to HANA instead of doing in application server. If you see the above picture you can clearly understand when to use Transient provider, Virtual provider and how composite provider can be used in BW 7.4. You can also see that query can be built on top of composite provider, Transient provider and MultiProvider. Do see this picture many times to get more clarity of BW on HANA modeling scenarios; it will help to design effective HANA modeling in major projects. 21

22 Putting it all together, what will be the change in BW 7.4!! Let us see how SAP BW on RDBMS is going to be converted in SAP BW 7.4 on HANA. This diagram will help to choose the BW 7.4 Objects when modeling a HANA based BW system. These objects will be replaced SAP BW on RDBMS SAP BW 7.4 on HANA Composite provider Composite provider MultiProvider Infoset Virtual provider Open ODS View c InfoObject InfoObject Hybrid Provider InfoCube DSO PSA Advanced DSO Conclusion The objective of this article is to present all the essential features of BW on HANA in a nut shell to assist consultants and architects to implement BW powered by HANA more effectively. I hope all major areas have been covered in this article. 22

23 Related Content SAP BW 7.4 Help page NetWeaver overview SAP BI Architecture HANA.SAP.COM Analyzing Business as It Happens, SAP and Intel LSA++ SCN Copyright 2015 SAP SE SE or an SAP SE affiliate company. All rights reserved. No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP SE. The information contained herein may be changed without prior notice. Some software products marketed by SAP SE and its distributors contain proprietary software components of other software vendors. National product specifications may vary. These materials are provided by SAP SE and its affiliated companies ( SAP SE Group ) for informational purposes only, without representation or warranty of any kind, and SAP SE Group shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP SE Group products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty. SAP SE and other SAP SE products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE in Germany and other countries. Please see for additional trademark information and notices. 23