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IRM UK 2-Day Seminar | The Logical Data Warehouse - Design, Architecture, and Technology
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The technology to create a logical data warehouse is available, and many organizations have already successfully completed the migration; a migration that is based on a step-by-step process and not on full rip-and-replace approach. In this practical course, the architecture is explained and products will be discussed. It discusses how organizations can migrate their existing architecture to this new one. Tips and design guidelines are given to help make this migration as efficient as possible.

 Export to Your Calendar 6/15/2017 to 6/16/2017
When: June 15 - 16, 2017
Thursday and Friday
Where: Map this event »
etc.venues Marble Arch
Garfield House,
86 Edgware Rd,
London W2 2EA
United Kingdom
Presenter: Rick F. van der Lans
Contact:

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  The Logical Data Warehouse -
Design, Architecture, and Technology
 


Use code AEA10 to receive 10% AEA member discount when registering!!

Register On-line:
15-16 June 2017, London

Overview

Classic data warehouse architectures are made up of a chain of databases. This chain consists of numerous databases, such as the staging area, the central data warehouse and several datamarts, and countless ETL programs needed to pump data through the chain. This architecture has served many organizations well. But is it still adequate for all the new user requirements and can new technology be used optimally for data analysis and storage?

Integrating self-service BI products with this architecture is not easy and certainly not if users want to access the source systems. Delivering 100% up-to-date data to support operational BI is difficult to implement. And how do we embed new storage technologies, such as Hadoop and NoSQL, into the architecture?

It is time to migrate gradually to a more flexible architecture in which new data sources can hooked up to the data warehouse more quickly, in which self-service BI can be supported correctly, in which OBI is easy to implement, in which the adoption of new technology, such as Hadoop and NoSQL, is easy, and in which the processing of big data is not a technological revolution, but an evolution.  

The architecture that fulfills all these needs is called the logical data warehouse architecture. This architecture, introduced by Gartner, is based on a decoupling of reporting and analyses on the one hand, and data sources on the other hand.

The technology to create a logical data warehouse is available, and many organizations have already successfully completed the migration; a migration that is based on a step-by-step process and not on full rip-and-replace approach.

In this practical course, the architecture is explained and products will be discussed. It discusses how organizations can migrate their existing architecture to this new one. Tips and design guidelines are given to help make this migration as efficient as possible.

Learning Objectives

In this course, you will learn:

  • What are the practical benefits of the logical data warehouse architecture and what are the differences with the classical architecture.
  • How can organizations step-by-step and successfully migrate to this flexible logical data warehouse architecture
  • The possibilities and limitations of the various available products.
  • How do data virtualization products work
  • How can big data be added transparently to the existing BI environment
  • How can self-service BI be integrated with the classical forms of BI
  • How can users be granted access to 100% up-to-date data without disrupting the operational systems
  • What are the real-life experiences of organizations that have already implemented a logical data warehouse

Course Outline

Challenges for the Classic Data Warehouse

  • Integrating big data with existing data and making it available for reporting and analytics
  • Supporting self-service BI and self-service data preparation
  • Polyglot persistency – processing data stored in Hadoop and NoSQL systems
  • Operational Business Intelligence, or analyzing of 100% up-to-date data

The Logical Data Warehouse

  • The essence : decoupling of reporting and data sources
  • From batch-integration to on-demand integration of data
  • The impact on flexibility and productivity – an improved time-to-market for reports
  • Examples of organizations

Implementing a Logical Data Warehouse with Data Virtualization Servers

  • Why data virtualization?
  • Market overview: AtScale, Cirro Data Hub, Cisco Information Server, Denodo Platform, Informatica Data Services, RedHat JBoss Data Virtualization, Rocket, and Stone Bond Enterprise Enabler
  • Importing non-relational data, such as XML and JSON documents, web services, NoSQL, and Hadoop data
  • The importance of an integrated business glossary and centralization of metadata specifications
  • Performance improving facilities: Caching of virtual tables, refreshing, and optimization techniques

Migrating to a Logical Data Warehouse

  • An A to Z roadmap
  • Guidelines for the development of a logical data warehouse
  • Three different methods for modeling: outside-in, inside-out, and middle-out
  • The value of a canonical data model
  • Considerations for security aspects
  • Step by step dismantling of the existing architecture
  • The focus on sharing of metadata specifications for integration, transformation, and cleansing

Self-Service BI and the Logical Data Warehouse

  • Why self-service BI can lead to “report chaos”
  • Centralizing and reusing metadata specifications with a logical data warehouse
  • Upgrading self-service BI into managed self-service BI
  • Implementing Gartner’s BI-modal environment

Big Data and the Logical Data Warehouse

  • New data storage technologies for big data, including Hadoop, MongoDB, Cassandra
  • The appearance of the polyglot persistent environment; or each application its own optimal database technology
  • Design rules to integrate big data and the data warehouse seamlessly
  • Big data is too “big” to copy
  • Offloading cold data with a logical data warehouse

Implementing Operational BI with a Logical Data Warehouse

  • Examples of operational reporting and operational analytics
  • Extending a logical data warehouse with operational data for real-time analytics
  • “Streaming” data in a logical data warehouse
  • The coupling of data replication and data virtualization

The Logical Data Warehouse and the Environment

  • Design principles to define data quality rules in a logical data warehouse
  • How data preparation can be integrated with a logical data warehouse
  • Shifting of tasks in the BICC  
  • Which new development and design skills are important?
  • The impact on the entire design and development process

Summary and Conclusions

Audience

  • Business Intelligence Specialists
  • Data Warehouse Designers
  • Business Analysts
  • Technology Planners
  • Technical Architects
  • Enterprise Architects
  • IT Consultants
  • IT Strategists
  • Systems Analysts
  • Database Developers
  • Database Administrators
  • Solutions Architects
  • Data Architects
  • IT Managers

Speaker Biography

Rick F. van der Lans

Rick F. van der Lans is an independent analyst, consultant, author, and lecturer specialising in data warehousing, business intelligence, big data, and database technology. He is Managing Director of R20/Consultancy. He has helpedmany large companies worldwide in defining their business intelligence and big data architectures. Mr. van der Lans is an internationally acclaimed lecturer. He is chairman of the European Business Intelligence Conference. His popular IT books have been translated into many languages and have sold over 100,000 copies. His latest book is entitled "Data Virtualization for Business Intelligence Systems". Rick writes blogs for well-known websites, such as TechTarget.com and BeyeNetwork.com, and he has written numerous sucessful whitepapers.

Seminar Fee
£1,245 + VAT (£249) = £1,494

Register On-line:
15-16 June 2017, London

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