Warum sap hana sql data warehousing
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Agilität, Cloud, Offenheit sind nur einige wichtige Anforderungen, die an moderne Data-Warehouse-Systeme gestellt werden. Lange Zeit stand SAP mit ihrer Lösung nicht für diese Art, ein Data Warehouse zu bauen. Aber gilt das noch? Hier zeigen wir Ihnen, wie ein SQL Data Warehouse auf der HANA-Plattform aufgebaut wird, wie es im Kern funktioniert und welche Entwicklungswerkzeuge genutzt werden, um Ihre Anforderungen umzusetzen. Dabei lernen Sie die Stärken des SAP-Ansatzes anhand eines realen Kunden-Beispiels kennen.
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Similaire à Warum sap hana sql data warehousing
Similaire à Warum sap hana sql data warehousing (20)
Warum sap hana sql data warehousing
- 1. Warum SAP HANA SQL Data Warehousing? KOSTENLOSES LIVE-WEBINAR
- 2. INHALTE Warum HANA SQL Data Warehousing? HANA als Plattform nutzen SAP HANA im DWH-Kontext Werkzeuge des HANA SQL DWH Praxisbeispiel HANA SQL DWH ISR Information Products AG | isr.de2
- 3. Modell-driven Agile Cloud Ready Openness (Software-based standards) Warum HANA SQL Data Warehousing? ISR Information Products AG | isr.de3
- 4. ISR Information Products AG | isr.de4 Model-driven Ansatz über alle Ebenen Conceptual Model Business Model Source Physical Model Virtual Analytical Model Physical Model Core Data Vault Physical Model Data Aquisition Physical Model Sources mapping generate reverse engineer
- 5. ISR Information Products AG | isr.de5 Agilität durch DevOps und Kontinuitätsprozesse Continuous Feedback ReleasePlan Model Develop Operate Deploy DESIGNTIME RUNTIME
- 6. ISR Information Products AG | isr.de6 Plattformübergreifend durch Cloud Readiness Containerisierung der Transporteinheiten Infrastruktur unabhängige MTAR Transporteinheit MTA Applikationen aus skalierbaren Mikro-Services
- 7. SAP HANA PLATFORM Source Code Management Build ServerIDE Modelling OperationETL DB XSA HDI >_ SAP Web IDE Eclipse VS Code Bitbucket GitLab GitHub Jenkins Bamboo Travis SAP SDI Talend Informatica SAP PD Visio Erwin SAP DWF Automic Munin ISR Information Products AG | isr.de7 Offenheit für die Wahl der Entwicklungstools
- 8. 8 HANA Plattform Data Warehouse • Green Field • Hybrid Scenario • Migration und Konvertierung Customer-Applikation • SOA-Architecture • XS Service Migration • End-User Applikations Company -Services • Operative Systems • Reporting Apps • SAP Analytics Cloud SAP HANA XSA: SAP HANA Extended Applikation Services Advanced • Applikationsserver innerhalb der HANA DB • Unterstützt unterschiedliche Entwicklungssprachen, wie NodeJS, Java and SQL • Managet die individuellen Entwicklungsumgebungen in Sandboxen • Stellt die Applikationsplattform dar • Entkoppelung der Infrastruktur und Entwicklung • Open Source Basis, Public Version ist Cloud Foundry ISR Information Products AG | isr.de ENTFESSELUNG DER HANA DATENBANK MIT XSA APPLIKATIONSSERVER
- 9. ISR Information Products AG | isr.de9 HANA Plattform Zeit und Ortsunabhängige Zusammenarbeit Geteiltes Code Repository Continuous integration / delivery DIE ZENTRALEN VORTEILE VON XSA IM ENTWICKLUNGSPROZESS
- 10. Aktueller Data-Warehouse-Kontext ANFORDERUNGEN UND KNACKPUNKTE EINES DATA WAREHOUSE Analysis Planning Reporting Predictive ESTABLISHED BUSINESS INTELLIGENCE Economically oriented Production oriented Data MartData Mart Virtual Data Mart DATA WAREHOUSE - CONSISTENT TECHNOLOGY AND METHODS Purchasing Logistics Production Sales & Marketing Value Creation Big Data Machine Data Sensor DataSD FI / CO PP MM Manufacturing Execution System CUSTOMERS
- 11. ISR Information Products AG | isr.de11 Entwicklungswerkzeuge für ein HANA SQL DWH ENTWICKLUNGSWERKZEUGE IN AKTION In WebIDE: • Create SDI Flowgraphs • Create Calculation Views In PowerDesigner: • Create Data Models (Business Model, Data Vault Model) • Generate Objects 2 3 HANA HANA Database Git Jenkins (CI/CD) SAP Analytics Cloud XSA / Cloud Foundry CREATE MODELS PROVIDE SOURCE CODE DEPLOYMENT INSTRUCTIONS DEPLOYMENT TRACK DEVELOPMENT TRACK DEPLOYMENT SAP WebIDE Source Systems SAP Smart Data Integration CONNECT SOURCES EXPORT MODELS WORK WITH SOURCE CODE FRONTEND ACCESS JSON Flatfile DATA IMPORT Further formats 3rd Party Frontend 1 4 Issue Management Tool SAP PowerDesigner
- 12. BERICHT AUS DER PRAXIS Start Punkt Quellsysteme SAP BW OSCARE, SAP ERP, SAP CRM welche in einem DB2-DWH integriert wurden, soll durch eine neue moderne und agile Architektur für die Zukunft ersetzt werden ISR Lösung • Bewertung der DWH- Varianten HANA SQL DW, BW/4 HANA und DB2 Blu anhand unterschiedlicher Use Cases • Architektur – Empfehlung HANA SQL DWH • Detailliertes Konzept für eine Zielarchitektur auf der Hana Plattform • Durchgängige Modelgetriebene & Agile Entwicklungsmethodik nach DevOps und Scrum • DWH Automatisierung & Test Automatisierung • Aktuelle in der Entwicklung/ Projektstart 2017 Technologien • HANA 2.0, HANA XSA, Jenkins, Git, Java, Node.js 1 PHASE: HANA SQL INTEGARTION ARCHITEKTUR+ IMPLEMENTIERUNG ISR Information Products AG | isr.de12 AUSZUG ONLY VIRTUAL CRM / ERP / OSCARE BW / Flatfiles SINGLE PERSISTENCE Data Vault DV4HANA Data Marts DB DB DB … Micro Strategy SAPHANAPlattformSourceSystems Harmonization Business Apps
- 13. BERICHT AUS DER PRAXIS Start Punkt • Cloudbasierte mobile Anwendung welche Kundenspezifische und Businessdaten bereitstellt ISR Lösung • Lösungsdesign und Implementierung • Business and Technische Konzeption Microbasierte Service Anforderungen • SAP HANA Platform mit XSA Services (Odata, ActiveMQ, etc.) • Synchrone und asynchrone Kommunikation • Realtime Replication und Transformation von SAP Data • Implementierung eines Node.js Producer welcher JSON-Nachrichten asynchron zu Apache Kafka sendet • Stabilität, Generisch, Ausfallsicher, Lastverteilung Technologien • HANA 2.0, HANA XSA, Jenkins, Docker, Java, Node.js, Apache Kafka 2 PHASE: MICROSERVICE ARCHITEKTUR (1 MIO KUNDEN) HANA SQL PLATTFORM, XSA APACHE KAFKA ISR Information Products AG | isr.de13 CRM / SQL DWH / BW XSA SAP HANA Persistence REST API Producer Integration DB DB DB … REST APIREST API Consumer / App. DMZ–24/7SAPDomain Pull (https) Push (JSON) Replication Odata AUSZUG
- 14. BERICHT AUS DER PRAXIS ZAHLEN, DATEN, FAKTEN ISR Information Products AG | isr.de14 AUSZUG + 35 Stories + 3 Applications + 5 Sprint teams + 3 Week Sprints + 10 Repositories < 4700 Merges < 280000 Commits + 40 Developers + 55 Business + 6 Scrum + 5800 Builds + 4900 Objects + 150 Releases + 10 Sourcesystems + 22 Services > 2 B Datasets + 5 Systems + 12 Tennants + 15 XSA Spaces SEIT2017
- 15. Fazit 1 2 3 4 Model-driven Agile Cloud Ready Openness Modernes, Zukunfts-orientiertes Data Warehouse + + +
- 16. ISR Information Products AG | isr.de16 WIE GEHT ES WEITER? SAP Schulung - SAP HANA SQL Data Warehousing HDW410 zur Schulung
- 17. Ich freue mich auf Ihren Anruf. ISR Information Products AG Am Mittelhafen 14 | 48155 Münster ISR Information Products AG | isr.de17 DOMINIK FISCHERSenior Consultant | SAP Information Management
- 18. ISR Information Products AG | isr.de18
Notes de l'éditeur
- Agile & Flexible: Quick Time to market Short Time to value / Shorten innovation cycle with faster development and deployment process Reduced complexity Increased development performance High Quality over time as well Adaptability Using automation to harness advantages of automated testing Fokus the work of developers on the critical tasks Cloud Ready: Flexible deployment options (onPrem / Cloud) Future proven architecture Independency of technical basis Model Driven: Communication basis with the business Modelling the Business* Transparency Industry standardized notations High Quality Consistent meta data Industrialize data warehouse development Openess: War of talents Sharded work repository for development objects Parallel development Openess of development tools High Quality over time as well
- 1. Aggregated CDM: Conventionally, modelling starts with the aggregated CDM that gives an overview of the content that is supposed to get integrated into the DWH (big picture). Only few information is necessary: Which entities are in the scope of the project? What is the business-related definition of the entity? Which connections exist between the entities? What is the Business Key? Does the subsequent modelling of an object take place top down or bottom up? A Conceptual Data Model (CDM, also called Business Model) is created as an Entity Relationship Model (ER Model). It contains the entities, the attributes of the entities and the relationships between the entities. As it is an abstract, business-oriented model the CDM also contains the definitions for the business relevance of the entities and attributes. Therefore it also serves as the basis for the Business Glossary. For the reference Data Warehouse architecture, we start with an aggregated CDM. This is a CDM that only contains the entities, the respective business key attributes and the relationships between the entities. 2. Utilizing top down modelling, the detailed CDM is derived from the aggregated CDM. It is less abstract and includes all attributes and all data domains (data types and lengths). However, it does not yet contain database-specific objects (e.g. views, indexes, etc.). 3. Based on the detailed CDM, a physical data model is created by the business intelligence architect. In our approach it is based on the Data Vault modelling method which will be discussed in more detail later. When the PDM is fully specified, it contains all database objects that are necessary for the implementation (e.g. tables, indexes, constraints, triggers, etc.) 4. Utilizing bottom up modelling, instead, the structure of the considered entity is adopted in the EAD from the source system via reverse engineering. This constitutes the basis for subsequent physical modelling. 5. Analogous to step 3, a physical data model is created by the business intelligence architect. 6. In the raw data warehouse (RAW) and in the business integrated data warehouse (BID), information is technically modelled and not supposed to be accessed by end users. Only when information reaches the analytical layer, the technical view gets transformed into a business view. This is achieved through Calculation Views which are modelled in a physical model in SAP HANA EAD. Technologically, these Calculation Views represent the analytical objects from a business view. For every entity modelled in the CDM a Calculation Views is built in the analytical layer. Consistency between CDM and analytical model is assured. 7. A number of analytical objects can be modelled to Cubes (multidimensional objects) using transactional data. These cubes are represented on SAP HANA by a star join. Cubes are also modelled in a physical data model in SAP HANA EAD. It is important that different data models know each other and that the objects inside the models are related. This prevents the loss of transparency in large data warehouse applications and ensures a good overview at all times. Also, this constitutes the basis for Data Lineage.