A Few Cool Features in BW 7.4 on HANA that Make a Difference



Similar documents
An Overview of SAP BW Powered by HANA. Al Weedman

RDP300 - Real-Time Data Warehousing with SAP NetWeaver Business Warehouse October 2013

SAP Business Warehouse Powered by SAP HANA for the Utilities Industry

SAP BW 7.4 Real-Time Replication using Operational Data Provisioning (ODP)

SAP BW on HANA : Complete reference guide

SAP HANA Live & SAP BW Data Integration A Case Study

A Practical Guide to SAP" NetWeaver Business Warehouse (BW) 7.0

SAP Cloud for Sales Integration to SAP ERP 6.0 End-to-end master data synchronization and process integration

BW362 SAP NetWeaver BW, powered by SAP HANA

BW362 SAP BW powered by SAP HANA

SAP BW: The Real-time Data Application Platform How SAP BW uses the SAP database options

The safer, easier way to help you pass any IT exams. SAP Certified Application Associate - SAP HANA 1.0. Title : Version : Demo 1 / 5

Exploring the Synergistic Relationships Between BPC, BW and HANA

Toronto 26 th SAP BI. Leap Forward with SAP

IBM DB2 specific SAP NetWeaver Business Warehouse Near-Line Storage Solution

SAP HANA SAP s In-Memory Database. Dr. Martin Kittel, SAP HANA Development January 16, 2013

BW362. SAP BW powered by SAP HANA COURSE OUTLINE. Course Version: 10 Course Duration: 5 Day(s)

DMM301 Benefits and Patterns of a Logical Data Warehouse with SAP BW on SAP HANA

[Analysts: Dr. Carsten Bange, Larissa Seidler, September 2013]

Providing real-time, built-in analytics with S/4HANA. Jürgen Thielemans, SAP Enterprise Architect SAP Belgium&Luxembourg

LearnSAP. SAP Business Intelligence. Your SAP Training Partner. step-by-step guide Camden Lane, Pearland, TX 77584

SAP BusinessObjects (BI) 4.1 on SAP HANA Piepaolo Vezzosi, SAP Product Strategy. Orange County Convention Center Orlando, Florida June 3-5, 2014

SAP BODS - BUSINESS OBJECTS DATA SERVICES 4.0 amron

Predictive Analytics Powered by SAP HANA. Cary Bourgeois Principal Solution Advisor Platform and Analytics

Configuration and Utilization of the OLAP Cache to Improve the Query Response Time

Developing a Successful HANA Analytics Roadmap. Rob Jerome Director, Business Intelligence

Mastering the SAP Business Information Warehouse. Leveraging the Business Intelligence Capabilities of SAP NetWeaver. 2nd Edition

Sales Planning and Forecast

BW-EML SAP Standard Application Benchmark

Leveraging BI Tools & HANA. Tracy Nguyen, North America Analytics COE April 15, 2016

SAP Business Intelligence Adoption V7.41:Software and Delivery Requirements. SAP Business Intelligence Adoption August 2015 English

SAP NetWeaver Information Lifecycle Management

IMPLEMENTATION OF DATA WAREHOUSE SAP BW IN THE PRODUCTION COMPANY. Maria Kowal, Galina Setlak

Business Intelligence Competency Partners Untangling the Confusion What SAP BW powered by HANA and HANA LIVE mean to your organization

Exploring SAP NetWeaver BW on SAP HANA in combination with SAP BusinessObjects BI 4.x

Accelerating the path to SAP BW powered by SAP HANA

SAP HANA implementation on SLT with a Non SAP source. Poornima Ramachandra

SAP BW powered by SAP HANA: Understanding the Impact of HANA Optimized InfoCubes Josh Djupstrom SAP Labs

SAP BW 7.40 Near-Line Storage for SAP IQ What's New?

Demonstration of SAP Predictive Analysis 1.0, consumption from SAP BI clients and best practices

SAP BusinessObjects BI Clients

SAP BW Columnstore Optimized Flat Cube on Microsoft SQL Server

Analysis Office and EPM Add-In - Convergence Alexander Peter, SAP SE SESSION CODE: BI70

An In-Depth Look at In-Memory Predictive Analytics for Developers

SAP Predictive Analytics Roadmap Charles Gadalla SAP SESSION CODE: #####

Welcome to the new SAP Predictive Analytics 2.0!

SAP HANA. Markus Fath, SAP HANA Product Management June 2013

Understanding the SAP BI Strategy

In-memory databases and innovations in Business Intelligence

CBW NLS IQ High Speed Query Access to Database and Nearline Storage

The New Face of Business Intelligence for SAP Customers

Srini Santhanam, Capgemini US, LLC Integration of Complex SAP and Non-SAP Applications Through SAP BusinessObjects Data Services for the Fortune 500

How to create connections with SAP BusinessObjects BI 4.0

Enhance your Analytics using Logical Data Warehouse and Data Virtualization thru SAP HANA smart data access SESSION CODE: 0210

Course Outline. Business Analysis & SAP BI (SAP Business Information Warehouse)

SAP S/4HANA Embedded Analytics

Ingo Hilgefort. Integrating SAP. Business Objects BI with SAP NetWeaver. Bonn Boston

Best practices in HANA implementations - real client experience

Data Warehouses and Business Intelligence ITP 487 (3 Units) Fall Objective

CBW NLS High Speed Query Access to Database and Nearline Storage

Innovate and Grow: SAP and Teradata

SAP BO 4.1 COURSE CONTENT

SAP BusinessObjects Design Studio Overview. Jie Deng, Product Management Analysis Clients November 2012

Toward Variability Management to Tailor High Dimensional Index Implementations

Integrating SAP and non-sap data for comprehensive Business Intelligence

SAP HANA Live for SAP Business Suite. David Richert Presales Expert BI & EIM May 29, 2013

Reporting and Analysis with SAP BusinessObjects

Bangkok, Thailand 22 May 2008, Thursday

SAP and Hortonworks Reference Architecture

Big Data Analytics Using SAP HANA Dynamic Tiering Balaji Krishna SAP Labs SESSION CODE: BI474

Business Intelligence In SAP Environments

Consumption of OData Services of Open Items Analytics Dashboard using SAP Predictive Analysis

Franco Furlan Middle and Eastern Europe CoE for Analytics

Building Real-Time Analytics Apps with HANA

SAP Business Objects BO BI 4.1

Business Intelligence mit SAP: Strategie, Neuerungen, Nutzen. Andreas Forster / Solution Advisor June 2013

PBS Information Lifecycle Management Solutions for SAP NetWeaver Business Intelligence 3.x and 7.x

Understanding DSO (DataStore Object) Part 1: Standard DSO

TE's Analytics on Hadoop and SAP HANA Using SAP Vora

SAP BUSINESS OBJECTS BO BI 4.1 amron

Empowered Self-Service with SAP HANA and SAP Lumira. Dennis Scoville BI Evangelist Business Intelligence & Technology Honeywell Aerospace

Extending The Value of SAP with the SAP BusinessObjects Business Intelligence Platform Product Integration Roadmap

SAP BusinessObjects BI Clients. January 2016

SAP BO 4.1 Online Training

SAP HANA Reinventing Real-Time Businesses through Innovation, Value & Simplicity. Eduardo Rodrigues October 2013

SAP Database Strategy Overview. Uwe Grigoleit September 2013

SAP SE - Legal Requirements and Requirements

SAP Solution Brief SAP Technology SAP HANA. SAP HANA An In-Memory Data Platform for Real-Time Business

How to Archive Data from SAP NetWeaver BW to SAP Sybase IQ as Near line Storage

SAP BusinessObjects Cloud

Trends, Strategy, Roadmaps and Product Direction for SAP BI tools in SAP HANA environment

Data as a Service Virtualization with Enzo Unified

Session Code*: 0310 Demystifying Authentication and SSO Options in Business Intelligence. Greg Wcislo

Business Objects BI Platform 4.x with SAP NetWeaver

SAP BusinessObjects Business Intelligence (BOBI) 4.1

Transcription:

A Few Cool Features in BW 7.4 on HANA that Make a Difference Here is a short summary of new functionality in BW 7.4 on HANA for those familiar with traditional SAP BW. I have collected and highlighted a few features out of many that I believe are crucial for understanding the benefits of BW on HANA SP8 and making architectural decisions. Table of Contents A Few Cool Features in BW 7.4 on HANA that Make a Difference... 1 Advanced DSOs... 1 Composite providers... 3 Open ODS views... 4 SLT sources... 4 HANA Analysis Process in BW... 6 BW Workspaces... 7 Landscape simplification, LSA++... 8 Mixed scenarios... 8 Advanced DSOs The ADSO is the only object you need for persistence. It replaces all previous types of DSOs, PSA, infocube, Hybrid Provider. Purpose of ADSO: only one modelling object for persistence. This raises a question on how to deal with the specific classical modeling objects in BW: The field-based structure of the PSA Write Optimized DSO The 3-table approach in standard DSO The every characteristic is key approach of the InfoCube. Page 1

The Advanced DSO manages to replace all of these objects by basically being all of them. In the ADSO settings you can specify the ADSO to behave like either one of these objects. SAP has provided specific templates for each use case: Data Acquisition Layer Corporate memory Data Propagation Layer Reporting Layer Each of these templates is made up of a specific combination of settings. The data propagation layer, for example, would require ticking the checkboxes below. These settings create an object with an active table to report on, and a change log table for further data provisioning, basically creating a classical Standard DSO. Benefits of ADSO: Simplification of object types. The ADSO is your object for persistence with settings to fine-tune it to your specific needs. Flexibility in data modeling. Because the ADSO is manageable by settings, you can start out modeling your ADSO using the Reporting Layer settings. If requirements change or you come across new insights, you can now simply change your settings to change that object into the propagation layer for example. No new object needed, this can even be done without losing data in most cases. Performance of data loads and activation is optimized for HANA as ADSO is a HANA native object. Page 2

Composite providers Composite Provider is an InfoProvider that combines data from several analytical indexes or from other InfoProviders (by JOIN or UNION) and makes this data available for reporting and analysis. Purpose of Composite Provider: one modelling InfoProvider (semantic layer) combining BW and HANA objects with either JOIN or UNION. UNION and JOIN operations are executed in HANA and not the application server. BEx queries can be created on Composite Providers as on any other BW InfoProviders. Benefits of Composite Provider: BW InfoProviders can be combined using JOIN operation allowing us to create new scenarios not possible or very expensive with standard techniques (Multiprovider, InfoSet) Ability to combine BW InfoProviders and HANA tables/views via UNION or JOIN Faster and simpler data modeling - successor to MultiProvider and InfoSet Reduce database size: virtual modelling capabilities as opposed to data flows Supports usage of inventory key figures and planning scenarios Page 3

Open ODS views 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. Purpose of ODS View: Faster agile modelling capabilities when using external data sources. ODS Views allow 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. Open ODS view allows data sources to be virtually consumed without the need for a separate BW persistence for the data. These data models can be located in a database schema (of the SAP HANA database in BW), which is not managed by BW. Alternatively, the models can be located in other databases, which can be connected to the SAP HANA database in BW using SAP HANA Smart Data Access. Benefits of Open ODS View: Flexible and easy integration of external data models Agile modeling combining infoobjects and field based modelling Integrated modelling environment between HANA and BW Enhanced business flexibility by supporting the logical EDW Reduces data modelling efforts SLT sources There are three types of data replication techniques: 1. SAP Landscape Transformation (SLT) 2. SAP Business Objects Data Services (BODS) 3. SAP HANA Direct Extractor Connection (DXC) SLT replication server allows loading and replicating data in real time from SAP and non-sap source Page 4

systems into SAP HANA environment. SLT allows real time and scheduled data replication, replicating only relevant data into HANA. Purpose of SLT Replication: Efficient way of data replication from SAP and non-sap systems to HANA. The replication server uses the Operational Data Provisioning (ODP) Framework. It is possible to connect several data providers, each with its own delta queue mechanism (ODQ). The delta queue mechanism is loaded with data from the source and there will be a database trigger for each new, changed or deleted record. You can even define how long data will be kept in the delta queue. More than one subscriber can use a defined delta queue at the same time. Benefits of SLT: Real time delta-enabled data acquisition capabilities for any source Operational Data Provisioning takes a lot of load from source systems because data is extracted exactly once and then distributed over ODP as needed, may it be in real-time or in certain time intervals Operational Data Provisioning offers a recovery mechanism with configurable data retention periods. That means you can define how long the data should be stored in the ODQ after it has been read by all subscribing applications. Highly efficient compression enables data compression rates up to 90 percent in ODQ Page 5

HANA Analysis Process in BW With the SAP HANA analysis process (HAP), BW users are able to use the functions of the SAP HANA database and can combine these with the functions in the BW system. Purpose of HAP: Execute HANA-native functions directly on BW InfoProvider data e.g. clustering, association algorithms, regression analysis, anomaly detection, weighted score, exponential smoothing, etc. Following are some of the standard functions available under Data Analysis: Supported procedures/algorithms: AFL-functions (PAL, ) L-script, R-script, SQL-script procedure. On SAP HANA database SAP recommends using HAP instead of Analysis Process Designer (APD). The process is then performed exclusively in SAP HANA, thus improving performance. HAP does not offer all features of APD however. You therefore need to check whether the functions you need are offered. If not, you can fall back on APD instead. The main difference in functionality between HAP and APD is that only InfoProviders that a column view exists for on the SAP HANA database are supported as data sources. Benefits of HAP: Execute complex and data intensive processes on HANA without losing the integrity and Page 6

integration with the BW environment Materialize the result of a HANA Analysis Process in HANA for further processing Supporting also a scheduled batch processing use case Can be embedded and monitored via DTP and Process Chains BW Workspaces A BW Workspace (agile data mart) enables business users to create ad-hoc scenarios for rapid prototyping. A BW workspace is an area where new models can be created based on central data from the BW system and local data. Workspaces can be managed and controlled by a central IT department and used by local special departments. This means you can quickly adjust to new and changing requirements. Purpose of BW Workspace: to bridge the gap between the central reporting requirements and the flexibility required locally. BW Workspace is an element of agile modelling. SAP BW Workspaces consist of an administrator tool, workspaces administration, and the BW Workspace Designer. Benefits of BW Workspace: Area where new models can be created based on central data from the SAP BW system and local data Can be managed and controlled by a central IT department and used by local special departments Page 7

Landscape simplification, LSA++ When using SAP BW on HANA a new integrated layer architecture (LSA++) is required to depict a system with different approaches in BI, their persistency layers and virtual data marts. The flexible, consistent core of the EDW remains in place. In the LSA++, special services for operational, flexible data are added to the core. The main differences to the LSA are: Queries on DataStore objects are just as fast as on InfoCubes. Therefore queries can be directly defined on the data propagation layer. You do not need an explicit reporting layer. You can define virtual data marts by using CompositeProviders that combine InfoProviders. Using special VirtualProviders and TransientProviders, you can access data directly in the SAP HANA database, to allow queries on the data. The whole model is surrounded by BW Virtual Data Mart Layer which means that we can query on any object here. Hence, we need rules, governance, service level agreements so we can report on ODS Layer objects, on EDW propagation layer objects etc. We can also potentially directly report on Corporate Memory if it makes sense or if there is a business scenario. Mixed scenarios There are a number of scenarios where HANA objects have to be accessed in BW, and reported on via BEx queries. On the other hand, there are scenarios where BW models (for example, master data objects) need to be accessed in SAP HANA via Analytic views. Data models in BW can be consumed via BEx queries by a number of BI frontend tools, such as OLAP Analysis, Web Intelligence, BO Design Studio. Alternatively, HANA models can be consumed by SAP Lumira, BO Explorer, SAP UI5, REST web services. Page 8

Consuming HANA tables/views in BW Integrate existing SAP HANA models with the new CompositeProvider in SAP BW Add BW OLAP functionalities on SAP HANA modeling scenarios Leverage SAP HANA interfaces, libraries, and tools in combination with SAP BW Consuming BW Model in HANA SAP HANA model generation triggered automatically from SAP BW Supports automatic synchronization of authorizations and impact analysis Direct consumption of SAP BW data via generated SAP HANA views, for example SAP Lumira, BO Explorer, SQL Page 9

Use SAP BW for further modeling in SAP HANA Mixed scenarios have numerous applications, and BW 7.4 on HANA have built in capabilities to expose BW data models to HANA with a few button clicks. At the same time HANA tables and views are accessible in BW 7.4 via Open ODS Views and Composite Providers. Benefits of Mixed Scenarios, combination of BW and HANA: Simplify the data modeling process Increase the agility of the Enterprise Data Warehouse Reduce the complexity of the EDW landscape Combine the strengths of the SQL oriented approach with an integrated EDW application Sergei Peleshuk has over 15 years of experience implementing BI technologies for global clients in retail, distribution, fast-moving consumer goods (FMCG), oil, and gas industries. He has helped clients to design robust BI reporting and planning capabilities, leading them through all project phases: from analysis of requirements to building BI roadmaps, technical architecture, and efficient BI teams. Sergei is an expert in modern BI tools and technologies available on the market, including SAP Business Warehouse (SAP BW), SAP HANA, BusinessObjects, and SAP Lumira. You may contact Sergei at peleshuk@biportal.org Page 10