.NET Usergroup Oldenburg 28. Mai 2015 - von Dr. Yvette Teiken
Big Data ist in aller Munde. Auch Microsoft ist mit HDInsight auf den Zug aufgesprungen. Aber wie passt das zusammen, Open Source, Hadoop und Microsoft? Wo sind die Anknüpfungspunkte zu klassischem BI? Wie werden Daten gespeichert und analysiert? Was ändert sich mit Big Data und was nicht? Unter anderem soll es gehen um.
Erstellung, Anfragen und Export von Hive Tabellen
Umsetzung von ETL-Prozessen mit Hilfe von PIG
Entwicklung nativer Map Reduce-Jobs mit C#
Interaktion mit traditionellen RDBMS und Streaming-Technologien
Datenspeicherung mit DocumentDB
Skalierung von Analysen
Enterprise Master Data Architecture: Design Decisions and OptionsBoris Otto
The enterprise-wide management of master data is a prerequisite for companies to meet strategic business
requirements such as compliance to regulatory requirements, integrated customer management, and global business process integration. Among others, this demands systematic design of the enterprise master data architecture. The current state-of-the-art, however, does not provide sufficient guidance for practitioners as it does not specify concrete design decisions they have to make and to the design options of which they can choose with regard to the master data architecture. This paper aims at contributing to this gap. It reports on the findings of three case studies and uses morphological analysis to structure design decisions and options for the management of an enterprise master data architecture.
Enterprise data architecture of complex distributed applications & servicesDavinder Kohli
The document discusses enterprise data architecture for complex distributed applications and services. It emphasizes that data is an asset that must be shared, accessible, and secured according to common terminology and definitions. It then discusses the importance of data flow/mapping as a facet of data architecture to identify data sources and relationships, and flow of information through applications. Challenges in data mapping include stakeholder buy-in, lack of data dictionaries, and evolving interfaces. The approach involves analyzing data flow/mapping from top-down business cases and bottom-up existing interfaces to reduce data movement and identify redundancy.
Real-time Enterprise Architecture mit LeanIX LeanIX GmbH
Vortrag von André Christ & Dominik Rose im Merck Innovation Center im Rahmen des EA Community Treffens Rhein Main.
===
LeanIX offers an innovative software-as-a-service solution for Enterprise Architecture Management (EAM), based either in a public cloud or the client’s data center.
Companies like Adidas, Axel Springer, Helvetia, RWE, Trusted Shops and Zalando use LeanIX Enterprise Architecture Management tool.
Free Trial: http://bit.ly/LeanIXFreeTrial
White Paper - Overview Architecture For Enterprise Data WarehousesDavid Walker
This document provides an overview of the typical architecture for an enterprise data warehouse. It discusses the core components, which include source systems, a transactional repository to store integrated data, a staging area for data loading, and data marts for analysis and reporting. It also describes additional elements that may be needed like ETL tools, data quality tools, a metadata repository, and documentation requirements. The goal is to present a design pattern for organizations to follow when implementing a large-scale enterprise data warehouse solution.
.NET Usergroup Oldenburg 28. Mai 2015 - von Dr. Yvette Teiken
Big Data ist in aller Munde. Auch Microsoft ist mit HDInsight auf den Zug aufgesprungen. Aber wie passt das zusammen, Open Source, Hadoop und Microsoft? Wo sind die Anknüpfungspunkte zu klassischem BI? Wie werden Daten gespeichert und analysiert? Was ändert sich mit Big Data und was nicht? Unter anderem soll es gehen um.
Erstellung, Anfragen und Export von Hive Tabellen
Umsetzung von ETL-Prozessen mit Hilfe von PIG
Entwicklung nativer Map Reduce-Jobs mit C#
Interaktion mit traditionellen RDBMS und Streaming-Technologien
Datenspeicherung mit DocumentDB
Skalierung von Analysen
Enterprise Master Data Architecture: Design Decisions and OptionsBoris Otto
The enterprise-wide management of master data is a prerequisite for companies to meet strategic business
requirements such as compliance to regulatory requirements, integrated customer management, and global business process integration. Among others, this demands systematic design of the enterprise master data architecture. The current state-of-the-art, however, does not provide sufficient guidance for practitioners as it does not specify concrete design decisions they have to make and to the design options of which they can choose with regard to the master data architecture. This paper aims at contributing to this gap. It reports on the findings of three case studies and uses morphological analysis to structure design decisions and options for the management of an enterprise master data architecture.
Enterprise data architecture of complex distributed applications & servicesDavinder Kohli
The document discusses enterprise data architecture for complex distributed applications and services. It emphasizes that data is an asset that must be shared, accessible, and secured according to common terminology and definitions. It then discusses the importance of data flow/mapping as a facet of data architecture to identify data sources and relationships, and flow of information through applications. Challenges in data mapping include stakeholder buy-in, lack of data dictionaries, and evolving interfaces. The approach involves analyzing data flow/mapping from top-down business cases and bottom-up existing interfaces to reduce data movement and identify redundancy.
Real-time Enterprise Architecture mit LeanIX LeanIX GmbH
Vortrag von André Christ & Dominik Rose im Merck Innovation Center im Rahmen des EA Community Treffens Rhein Main.
===
LeanIX offers an innovative software-as-a-service solution for Enterprise Architecture Management (EAM), based either in a public cloud or the client’s data center.
Companies like Adidas, Axel Springer, Helvetia, RWE, Trusted Shops and Zalando use LeanIX Enterprise Architecture Management tool.
Free Trial: http://bit.ly/LeanIXFreeTrial
White Paper - Overview Architecture For Enterprise Data WarehousesDavid Walker
This document provides an overview of the typical architecture for an enterprise data warehouse. It discusses the core components, which include source systems, a transactional repository to store integrated data, a staging area for data loading, and data marts for analysis and reporting. It also describes additional elements that may be needed like ETL tools, data quality tools, a metadata repository, and documentation requirements. The goal is to present a design pattern for organizations to follow when implementing a large-scale enterprise data warehouse solution.
Big Data Paris - A Modern Enterprise ArchitectureMongoDB
Depuis les années 1980, le volume de données produit et le risque lié à ces données ont littéralement explosé. 90% des données existantes aujourd’hui ont été créé ces 2 dernières années, dont 80% sont non structurées. Avec plus d’utilisateurs et le besoin de disponibilité permanent, les risques sont beaucoup plus élevés.
Quels sont les paramètres de bases de données qu’un décideur doit prendre en compte pour déployer ses applications innovantes?
Enterprise data serves both running business operations and managing the business. Building a successful data architecture is challenging due to data complexity, competing stakeholder interests, data proliferation, and inaccuracies. A robust data architecture must address key components like data repositories, capture and ingestion, definition and design, integration, access and distribution, and analysis.
This presentation elaborates on design decisions and design options when it comes to designing the master data architecture.
The presentation was given at the 16th Americas Conference on Information Systems (AMCIS 2010) in Lima, Peru.
The document outlines an agenda for a presentation on digital transformation, including introducing concepts like the internet of things, big data, and digital initiatives. It then provides examples of using big data for applications in areas like retail stores, disaster warnings, and outbreak detection. The document concludes by presenting a digital transformation reference model and discussing elements like digital platforms, business services, and digital organizations.
What is an API-first enterprise? Where do APIs fit into modern application architecture? Are they just new terms for SOA? Presentation from Apigee's City Tour in Paris 23 June 2016.
Meaning making – separating signal from noise. How do we transform the customer's next input into an action that creates a positive customer experience? We make the data more intelligent, so that it is able to guide our actions. The Data Lake builds on Big Data strengths by automating many of the manual development tasks, providing several self-service features to end-users, and an intelligent management layer to organize it all. This results in lower cost to create solutions, "smart" analytics, and faster time to business value.
This is the presentation for the talk I gave at JavaDay Kiev 2015. This is about an evolution of data processing systems from simple ones with single DWH to the complex approaches like Data Lake, Lambda Architecture and Pipeline architecture
Implementing a Data Lake with Enterprise Grade Data GovernanceHortonworks
Hadoop provides a powerful platform for data science and analytics, where data engineers and data scientists can leverage myriad data from external and internal data sources to uncover new insight. Such power is also presenting a few new challenges. On the one hand, the business wants more and more self-service, and on the other hand IT is trying to keep up with the demand for data, while maintaining architecture and data governance standards.
In this webinar, Andrew Ahn, Data Governance Initiative Product Manager at Hortonworks, will address the gaps and offer best practices in providing end-to-end data governance in HDP. Andrew Ahn will be followed by Oliver Claude of Waterline Data, who will share a case study of how Waterline Data Inventory works with HDP in the Modern Data Architecture to automate the discovery of business and compliance metadata, data lineage, as well as data quality metrics.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Modern Data Architecture for a Data Lake with Informatica and Hortonworks Dat...Hortonworks
How do you turn data from many different sources into actionable insights and manufacture those insights into innovative information-based products and services?
Industry leaders are accomplishing this by adding Hadoop as a critical component in their modern data architecture to build a data lake. A data lake collects and stores data across a wide variety of channels including social media, clickstream data, server logs, customer transactions and interactions, videos, and sensor data from equipment in the field. A data lake cost-effectively scales to collect and retain massive amounts of data over time, and convert all this data into actionable information that can transform your business.
Join Hortonworks and Informatica as we discuss:
- What is a data lake?
- The modern data architecture for a data lake
- How Hadoop fits into the modern data architecture
- Innovative use-cases for a data lake
Big data architectures and the data lakeJames Serra
The document provides an overview of big data architectures and the data lake concept. It discusses why organizations are adopting data lakes to handle increasing data volumes and varieties. The key aspects covered include:
- Defining top-down and bottom-up approaches to data management
- Explaining what a data lake is and how Hadoop can function as the data lake
- Describing how a modern data warehouse combines features of a traditional data warehouse and data lake
- Discussing how federated querying allows data to be accessed across multiple sources
- Highlighting benefits of implementing big data solutions in the cloud
- Comparing shared-nothing, massively parallel processing (MPP) architectures to symmetric multi-processing (
The document provides an overview of enterprise architecture. It defines enterprise architecture as the analysis and documentation of an enterprise from strategic, business, and technical perspectives. The overview discusses the key concepts of enterprise architecture including business networks, information flows, infrastructure, products/services, and transition planning. It also provides a high-level view of how enterprise architecture analyzes an organization's current and future state across technology, business, and strategy.
Describes what Enterprise Data Architecture in a Software Development Organization should cover and does that by listing over 200 data architecture related deliverables an Enterprise Data Architect should remember to evangelize.
Architecting an Enterprise API Management StrategyWSO2
The document discusses strategies for architecting an enterprise API management strategy. It covers factors to consider like whether to treat APIs as a product or tactic. It also discusses API management components like the API publisher and store. The document outlines reference architectures like using API management within an orthogonal toolset. It provides examples of API management for use cases like within a telecommunications ecosystem.
Infrastruktur agil bauen - der DBA im SAFe-UmfeldDaniel Steiger
Das Scaled Agile Framework kombiniert Ansätze aus den agilen Methoden Scrum, Kanban und Extreme Programming mit Lean Thinking sowie den von Donald G. Reinertsen formulierten Prinzipien zum Lean Product Development und ermöglicht es so, Agilität im Enterprise Umfeld und grossen Maßstab anzuwenden. Für die Entwickler tönt das sehr spannend, aber geht denn das auch im Infrastrukturbau? Klar Infrastructure-as-Code, ist ja schliesslich auch nur programmieren, aber wenn dann die gestandenen Datenbankadministratoren sich in einem agilen Team einfinden müssen, ist der Spass vielleicht schon vorbei.
In diesem Vortrag wird die Struktur von SAFe aufgezeigt und dann auf die Erfahrungen eines DBA und System-Engineer-Teams eingegangen, welches in ein bestehendes SAFe-Umfeld integriert wurde. Die DevOps-Phasen mit initialem Aufbau, Betrieb mit SLA-Verantwortung und Lifcycle werden ebenfalls vorgestellt und der Ablauf von PI (Programm Increment) über Sprints mit Userstories erläutert.
Big Data Paris - A Modern Enterprise ArchitectureMongoDB
Depuis les années 1980, le volume de données produit et le risque lié à ces données ont littéralement explosé. 90% des données existantes aujourd’hui ont été créé ces 2 dernières années, dont 80% sont non structurées. Avec plus d’utilisateurs et le besoin de disponibilité permanent, les risques sont beaucoup plus élevés.
Quels sont les paramètres de bases de données qu’un décideur doit prendre en compte pour déployer ses applications innovantes?
Enterprise data serves both running business operations and managing the business. Building a successful data architecture is challenging due to data complexity, competing stakeholder interests, data proliferation, and inaccuracies. A robust data architecture must address key components like data repositories, capture and ingestion, definition and design, integration, access and distribution, and analysis.
This presentation elaborates on design decisions and design options when it comes to designing the master data architecture.
The presentation was given at the 16th Americas Conference on Information Systems (AMCIS 2010) in Lima, Peru.
The document outlines an agenda for a presentation on digital transformation, including introducing concepts like the internet of things, big data, and digital initiatives. It then provides examples of using big data for applications in areas like retail stores, disaster warnings, and outbreak detection. The document concludes by presenting a digital transformation reference model and discussing elements like digital platforms, business services, and digital organizations.
What is an API-first enterprise? Where do APIs fit into modern application architecture? Are they just new terms for SOA? Presentation from Apigee's City Tour in Paris 23 June 2016.
Meaning making – separating signal from noise. How do we transform the customer's next input into an action that creates a positive customer experience? We make the data more intelligent, so that it is able to guide our actions. The Data Lake builds on Big Data strengths by automating many of the manual development tasks, providing several self-service features to end-users, and an intelligent management layer to organize it all. This results in lower cost to create solutions, "smart" analytics, and faster time to business value.
This is the presentation for the talk I gave at JavaDay Kiev 2015. This is about an evolution of data processing systems from simple ones with single DWH to the complex approaches like Data Lake, Lambda Architecture and Pipeline architecture
Implementing a Data Lake with Enterprise Grade Data GovernanceHortonworks
Hadoop provides a powerful platform for data science and analytics, where data engineers and data scientists can leverage myriad data from external and internal data sources to uncover new insight. Such power is also presenting a few new challenges. On the one hand, the business wants more and more self-service, and on the other hand IT is trying to keep up with the demand for data, while maintaining architecture and data governance standards.
In this webinar, Andrew Ahn, Data Governance Initiative Product Manager at Hortonworks, will address the gaps and offer best practices in providing end-to-end data governance in HDP. Andrew Ahn will be followed by Oliver Claude of Waterline Data, who will share a case study of how Waterline Data Inventory works with HDP in the Modern Data Architecture to automate the discovery of business and compliance metadata, data lineage, as well as data quality metrics.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Modern Data Architecture for a Data Lake with Informatica and Hortonworks Dat...Hortonworks
How do you turn data from many different sources into actionable insights and manufacture those insights into innovative information-based products and services?
Industry leaders are accomplishing this by adding Hadoop as a critical component in their modern data architecture to build a data lake. A data lake collects and stores data across a wide variety of channels including social media, clickstream data, server logs, customer transactions and interactions, videos, and sensor data from equipment in the field. A data lake cost-effectively scales to collect and retain massive amounts of data over time, and convert all this data into actionable information that can transform your business.
Join Hortonworks and Informatica as we discuss:
- What is a data lake?
- The modern data architecture for a data lake
- How Hadoop fits into the modern data architecture
- Innovative use-cases for a data lake
Big data architectures and the data lakeJames Serra
The document provides an overview of big data architectures and the data lake concept. It discusses why organizations are adopting data lakes to handle increasing data volumes and varieties. The key aspects covered include:
- Defining top-down and bottom-up approaches to data management
- Explaining what a data lake is and how Hadoop can function as the data lake
- Describing how a modern data warehouse combines features of a traditional data warehouse and data lake
- Discussing how federated querying allows data to be accessed across multiple sources
- Highlighting benefits of implementing big data solutions in the cloud
- Comparing shared-nothing, massively parallel processing (MPP) architectures to symmetric multi-processing (
The document provides an overview of enterprise architecture. It defines enterprise architecture as the analysis and documentation of an enterprise from strategic, business, and technical perspectives. The overview discusses the key concepts of enterprise architecture including business networks, information flows, infrastructure, products/services, and transition planning. It also provides a high-level view of how enterprise architecture analyzes an organization's current and future state across technology, business, and strategy.
Describes what Enterprise Data Architecture in a Software Development Organization should cover and does that by listing over 200 data architecture related deliverables an Enterprise Data Architect should remember to evangelize.
Architecting an Enterprise API Management StrategyWSO2
The document discusses strategies for architecting an enterprise API management strategy. It covers factors to consider like whether to treat APIs as a product or tactic. It also discusses API management components like the API publisher and store. The document outlines reference architectures like using API management within an orthogonal toolset. It provides examples of API management for use cases like within a telecommunications ecosystem.
Infrastruktur agil bauen - der DBA im SAFe-UmfeldDaniel Steiger
Das Scaled Agile Framework kombiniert Ansätze aus den agilen Methoden Scrum, Kanban und Extreme Programming mit Lean Thinking sowie den von Donald G. Reinertsen formulierten Prinzipien zum Lean Product Development und ermöglicht es so, Agilität im Enterprise Umfeld und grossen Maßstab anzuwenden. Für die Entwickler tönt das sehr spannend, aber geht denn das auch im Infrastrukturbau? Klar Infrastructure-as-Code, ist ja schliesslich auch nur programmieren, aber wenn dann die gestandenen Datenbankadministratoren sich in einem agilen Team einfinden müssen, ist der Spass vielleicht schon vorbei.
In diesem Vortrag wird die Struktur von SAFe aufgezeigt und dann auf die Erfahrungen eines DBA und System-Engineer-Teams eingegangen, welches in ein bestehendes SAFe-Umfeld integriert wurde. Die DevOps-Phasen mit initialem Aufbau, Betrieb mit SLA-Verantwortung und Lifcycle werden ebenfalls vorgestellt und der Ablauf von PI (Programm Increment) über Sprints mit Userstories erläutert.
Data Mesh und Domain Driven Design - rücken Analytics und SD nun doch näher z...Fabian Hardt
Lange Zeit wichen Architekturen von Analytics Systemen stark von modernen Software-Architekturen ab. Während die letzten Jahre oft von Domain Driven Design die Rede war und immer mehr Monolithen in Microservices zerschlagen wurden, blieben Analytics Plattformen wie Data Lake und Data Warehouse weiterhin schwerfällig. Doch nun ist auch hier die Rede von Data Mesh und Data Products. Doch inwiefern unterscheiden sich diese Konzepte nun eigentlich noch und was ist der Unterschied zwischen Microservice und Data Product? Werden hier nicht doch ähnliche Frameworks und Architekturpatterns benötigt? Diese Einordnung soll dieser Vortrag bieten und ein Beispiel bringen, wie eine noch engere Verzahnung im Unternehmen erfolgen kann und somit echte Mehrwerte für Ihre IT-Landschaften schaffen kann.
Cloud Computing – erwachsen genug für Unternehmen? by Dr. Michael PaulyMedien Meeting Mannheim
Das Hype-Thema Cloud Computing wird langsam erwachsen. Nach anfänglicher Euphorie drängen nun vermehrt Angebote für Unternehmen auf den Markt, die den Begriff Cloud auch verdienen. Dabei stehen Anbieter von Cloud Computing im geschäftlichen Umfeld vor der Herausforderung die technischen Möglichkeiten und die Anforderungen des Geschäfts zusammenzubringen.
Hierbei sind neben rechtlichen Fragen u.a. auch technische Fragen zu klären wie z.B. eine Integration von Bestandssystemen erfolgt oder welche organisatorischen Voraussetzungen für Cloud Computing in Unternehmen überhaupt notwendig sind. Dabei stellt sich immer mehr heraus, dass im Unternehmensumfeld Cloudprinzipien aufgegeben werden müssen, um den Anforderungen des Geschäftes nach dynamischen und flexiblen ICT-Services zu genügen.
Unternehmen, die vor der Frage eines möglichen Einsatzes von Cloud Computing stehen, haben im Vorfeld eine Vielzahl von Aspekten abzuwägen und Fragen zu klären. Dieser Vortrag betrachtet einige davon und zeigt am Beispiel der Dynamic Services von T- Systems, eine Realisierung auf Providerseite auf.
Denn erst wenn die heute noch offenen Fragen für das jeweilige Unternehmen zufriedenstellend beantwortet sind, stellt Cloud Computing eine echte alternative Sourcingstrategie dar.
Am 8. April 2008 fand in den Räumlichkeiten des Kosaido International Golfclubs in Düsseldorf die zweite Veranstaltung zum Thema modellgetriebene Softwareentwicklung (MDSD) statt. Unter dem Titel "MDSD - Chance und Herausforderung für IT-Organisationen" lag der Schwerpunkt der Vorträge dieses Mal auf den Organisatorischen Rahmenbedingungen, in denen MDSD erfolgreich betreiben
Das aktuelle Schlagwort in der Presse und auf vielen Webseiten lautet Cloud-Computing. Viele Anbieter von Hard- und Software werben sogar mit dem Begriff Cloud-Computing. Aber mal ehrlich, wissen sie was eigentlich Cloud-Computing wirklich bedeutet und was mit der Wolke gemeint ist.
Blockchain value cases in startup sceneTarmo Ploom
This document discusses potential business cases and applications for blockchain technology startups. It begins by discussing the challenges of crossing the chasm from early adopters to mainstream users. It then reviews the current bitcoin ecosystem and some selected business cases, including mining, trading, infrastructure providers and payment processing. It suggests the potential of blockchain lies in reengineering old business models to be less expensive and developing new markets not previously possible. Examples of potential "killer apps" discussed include machine-to-machine payments, resource allocation in shared economies, peer-to-peer social networks and more. It closes by asking for any questions.
Blockchain value cases in startup scene v0.03Tarmo Ploom
This document discusses potential business cases and applications for blockchain technology startups. It begins by discussing the challenges of adopting new technologies and outlines diffusion of innovation models. It then reviews the current bitcoin ecosystem and provides examples of business models including mining, trading, infrastructure providers and payment processing. The document also examines the potential of blockchain and reviews case studies of the Swiss blockchain ecosystem and how Twitter monetized its platform. It closes by considering potential "killer applications" for blockchain like machine-to-machine payments, resource allocation, social networks and identity management. The document suggests sustainable models may involve reengineering existing businesses or creating new markets not previously possible.
This document discusses Credit Suisse's 25 years of experience managing interfaces. It covers the evolution of their interface management approach from paper-based catalogs to a model-driven SOA repository that links interface design to implementation. It describes their taxonomy of interfaces and outlines their governance process involving quality assurance checkpoints. The presentation analyzes different generations of interface repositories and how Credit Suisse overcame issues such as scale, integration, and bridging the design-implementation gap.
The document discusses the inevitability of multicloud environments and the need for collaboration between cloud providers. It notes that enterprises have requirements around security, availability and integration that make using a single cloud vendor infeasible. As organizations adopt cloud services over time, they progress from using basic SaaS to integrating private and public clouds. However, true multicloud integration faces challenges around cross-cloud communication and standardization. The document argues that cloud integration middleware and standardization are needed to reduce vendor lock-in and enable multicloud, though competition may currently limit collaboration between providers.
Digital transformation from value chain to value network possible digital t...Tarmo Ploom
The document discusses possible digital transformation scenarios for the financial industry. It argues that the main challenge for financial institutions is technology, not regulation. Banks should focus on core competencies like customer relationships and optimizing processes for digital business. Banks can replace parts of existing IT landscapes with services from new FinTech entrants. Leveraging financial industry standards can simplify integration between banks and web services. The document presents models of Credit Suisse's current processes and systems and envisions a future state with increased use of standards and FinTech services integrated into bank processes through an approach of managed evolution.
The document discusses a presentation on service oriented business applications (SOBA) as the road towards an agile enterprise. It provides an overview of Credit Suisse, discusses complexity and agility challenges, and defines SOBA. Key aspects of SOBA covered include the conceptual view, development using model-driven architecture, maturity models, and a roadmap to build the necessary platforms and processes to realize SOBA and increase enterprise agility. The goal of SOBA is to enable composing and changing applications dynamically using reusable services, processes, and rules.
Transforming SOA Landscape Towards SOA+BPM LandscapeTarmo Ploom
This document discusses Credit Suisse's vision for transforming its existing SOA landscape into a SOA+BPM landscape. It provides background on Credit Suisse as an international bank, an overview of its current SOA implementation including services, events, and bulk data transfer, and its current state of BPM where many business processes are well documented but only a portion have been automated using process engines. The document outlines Credit Suisse's roadmap and barriers to fully transforming to a SOA+BPM landscape.
Emerging SOA + BPM Standards,Software and PlatformsTarmo Ploom
The document discusses emerging standards around service-oriented architecture (SOA) and business process management (BPM). It covers topics such as the emergence of SOA and how services are described. SOA technology is discussed, with a focus on enterprise service buses. The emergence of BPM is also introduced. The document presents an agenda that will cover additional topics like classifying process types, process definition languages, BPM technology, service-oriented business applications, and development paradigms.
Interface Management System: Concepts and ImplementationTarmo Ploom
Credit Suisse implemented several generations of interface repositories to manage their growing collection of interfaces. The first generation provided basic management of interface metadata but had limitations scaling to thousands of interfaces. The second generation introduced semi-automated governance processes to manage the interface lifecycle. The third generation added an integrated development environment and the fourth generation aims for model-driven service-oriented architecture.
Interface Management System: Concepts and Implementation
MDA & SOA als Mittel zur IT-Komplexitätsreduktion bei Credit Suisse
1. CONFIDENTIAL
IT-
Workshop MDD, SOA und IT-
Management 2007
MDA & SOA als Mittel zur
IT-Komplexitätsreduktion
bei Credit Suisse
Date: 12.04.2007
Produced by: Tarmo Ploom
Produced by: Name Surname
Date: 03.11.2005 Slide 1
2. Agenda
Einführung Credit Suisse
Credit Suisse IT-Landschaft
Credit Suisse SOA Schictenmodell
Credit Suisse SOA Schictenmodell und MDA
Kombination von MDA und SOA bei Credit Suisse
Learnings und Probleme
Fragen
Produced by: Tarmo Ploom
Date: 12.04.2007 Slide 2
3. Einführung Credit Suisse
Credit Suisse ist führende globale Bank mit dem Head Office in
Zurich.
Credit Suisse fokussiert sich auf: Investment Banking, Private
Banking and Asset Management.
Credit Suisse ist bekannt für ausgezeichnete Expertise,
gesamtheitliche Lösungen und innovative Produkte.
Produced by: Tarmo Ploom
Date: 12.04.2007 Slide 3
4. Assets under Management
of selected financial services companies
As per March 31, 2006 (CHF billion)
UBS* 2,652
State Street 2,012
Barclays* 1,997
Allianz Group* 1,991
Fidelity Investments 1,960
AXA* 1,683
Credit Suisse Group 1,554
Deutsche Bank* 1,371
Vanguard Group** 1,255
JP Morgan Chase 1,141
Mellon Financial Corp. 1,056
ING Group* 866
Northern Trust 853
Morgan Stanley** 827
Merrill Lynch 759
Citigroup 758
*as per December 31, 2005
Goldman Sachs** 746
**as per February 28, 2006
Aviva* 719
695
CHF/USD 1.3068
Prudential Financial*
CHF/EUR 1.5814
BNP Paribas* 680
CHF/GBP 2.2668
Produced by: Tarmo Ploom
Date: 12.04.2007 Slide 4
5. Credit Suisse IT-Landschaft, physische Sicht
Dimension Applikationen
ca 900 Applikationen
Dimension Plattformen
Java Plattform, ca 11.5 Millionen SLOC Java
Host Plattform, ca 14.5 Millionen SLOC PL1
DWH Plattform
ERP Platform
Dimension Skalierung
12 Millionen CORBA Transaktionen täglich
15 Millionen MQ Transaktionen täglich
17 000 Produkte
IT-
Wie kann man die Komplexität von IT-Landschaft reduzieren?
Produced by: Tarmo Ploom
Date: 12.04.2007 Slide 5
6. Credit Suisse IT-Landschaft, logische Sicht
Logische Decomposition
1 Landschaft
19 Domänen
90 Komponenten
Services
ca 1000 publik Services
Events
ca 38 000 publik und private Queues
Bulk
25 000 publik und private Files
Wie kann man die Komplexität von SOA reduzieren?
Produced by: Tarmo Ploom
Date: 12.04.2007 Slide 6
7. Credit Suisse SOA Schichtenmodell
SOA
Geschäfts-
ebene
SOA
SOA Abstraktiosebenen
logische Ebene
SOA
physische Ebene
SOA
Implementationsebene
SOA
Betriebsebene
Produced by: Tarmo Ploom
Date: 12.04.2007 Slide 7
8. Credit Suisse SOA Schichtenmodell und MDA
SOA MDA
Geschäfts-
ebene CIM
SOA MDA
logische Ebene
MDA Abstraktiosebenen
SOA Abstraktiosebenen
PIM
SOA MDA
physische Ebene PSM
SOA MDA
Implementationsebene Implementationsebene
SOA MDA
Betriebsebene Betriebsebene
Produced by: Tarmo Ploom
Date: 12.04.2007 Slide 8
9. Kombination von MDA und SOA bei Credit Suisse
Geschäftsebene der Services wird im Text beschrieben (CIM)
Logische Ebene der Services wird modelliert (PIM)
PSM und Code Artefakten werden mehrheitlich generiert
Java PSM Java
Service Model Artefakten
PL1 PSM PL1
Service Artefakten
PIM Service Model
Model
IDL PSM IDL
Service Model Artefakten
WSDL PSM WSDL
Service Model Artefakten
Produced by: Tarmo Ploom
Date: 12.04.2007 Slide 9
10. Kombination von MDA und SOA bei Credit Suisse
ECore Metamodell statt UML 2.0 Metamodell
Model-zu-Model Transformationen mit Java
Model-zu-Code Transformationen mit JET
ECore PIM ECore PSM ECore Artefakt
Metamodell Metamodell Metamodell
Java Java Java JET
Interface PIM PSM Artefakt Artefakt
Repository Model Model Model
Produced by: Tarmo Ploom
Date: 12.04.2007 Slide 10
11. Learnings & Probleme & Zusammenfassung
Learnings
Für Strukturmodellen eignet sich ECore Metamodell besser als UML 2.0
Metamodell
Erstellung von PIM, PSM und Artefakt Metamodellen ist relativ aufwändig.
Die Model-zu-Model Transformationen sind aufwändiger als Model-zu-Model
Transformationen
Probleme
Model-zu-Model Transformationen
Orchestrierung von Transformationsprozess
Zusammenfassung
MDA hilft die Komplexität von SOA basierenden IT-Landschaften zu reduzieren
Die Grundlagen von MDA sind ein bisschen Turbulent aber Stabilisation ist in der
Sicht
Produced by: Tarmo Ploom
Date: 12.04.2007 Slide 11
12. Fragen?
Produced by: Tarmo Ploom
Date: 12.04.2007 Slide 12