2-Day Seminar

The Logical Data Warehouse -
Design, Architecture, and Technology

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

PDF File IRM UK Public Courses 2017

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, such as self-service BI and data science, and can new technology be used optimally for data analysis and storage, such as Hadoop and NoSQL?

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 be hooked up to the data warehouse more quickly
  • self-service BI can be supported correctly
  • streaming analytics is easy to implement
  • the adoption of new technology, such as Hadoop and NoSQL, is easy
  • 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 is based on a decoupling of data consumers (reporting, analysis, data science) on the one hand, and data sources on the other hand.

The technology to create a logical data warehouse is available in the form data virtualization servers, 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 use cases and products will be discussed. It discusses how organizations can migrate their existing architecture to this new one. Tips, best practices, and design guidelines are given to help make this migration as efficient as possible.

Learning Objectives
In this course, Rick van der Lans answers the following questions:

  • 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?
  • What are 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 Architecture

  • 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 Architecture

  • The essence : decoupling of data consumers and data producers
  • From batch-integration to on-demand integration of data
  • The impact on flexibility and productivity – an improved time-to-market for reports
  • Why the LDWA is better suited for new forms of BI, including self-service BI, investigative analytics, and data science
  • Is a physical data warehouse still needed in a LDWA?

Implementing a Logical Data Warehouse Architecture with Data Virtualization Servers

  • Why data virtualization?
  • Working with virtual tables
  • Market overview: Cirro Data Hub, Cisco Information Server, Data Virtuality UltraWrap, Denodo Platform, 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, and data from cloud applications
  • The importance of an integrated business glossary and centralization of metadata specifications

Features for Improving Performance

  • Query optimization techniques, including pushdown, query injection, and ship join
  • Caching of virtual tables
  • Refreshing styles for cached virtual tables
  • Query optimization across a network in the cloud; push query processing to the place where the data is produced

Data Virtualization Modules for Design, Modelling and Implementation

  • The importance of impact and lineage analysis for raising trust in report results
  • Speeding up the process of hooking up new data sources through data model discovery
  • Embedded data profiling features lead to closer collaboration with users
  • How data preparation can be integrated
  • The business glossary
  • Google-like search to help users on their way

General Guidelines and Tips for Designing a Logical Data Warehouse Architecture

  • The importance of three layers with virtual tables
  • Treating historic data in a LDWA
  • Where and how is data being cleansed?
  • Design principles for defining data quality rules in a LDWA
  • Using master data in a LDWA
  • Defining a LDWA on a datavault-based data warehouse

Big Data and the Logical Data Warehouse Architecture

  • New data storage technologies for big data, including Hadoop, MongoDB, Cassandra
  • The polyglot persistent environment in which each application uses its own optimal database technology
  • Making big data easy to use for analysis and reporting
  • Big data is too “big” to copy
  • Offloading cold data with a LDWA

Developing Data Lakes with Data Virtualization Technology

  • What exactly is a data lake and what is its purpose?
  • The relationship between the data lake and data science
  • The practical restrictions of a physical data lake: data security, size, data protection and privacy, internal politics, and network bandwidth
  • The alternative is the logical or virtual data lake
  • Integrating the logical data lake and the LDWA

Implementing Operational BI and Streaming with a Logical Data Warehouse Architecture

  • Examples of operational reporting and operational analytics
  • What do we mean with streaming analytics and what is the relationship with the Internet-of-Things?
  • Extending a LDWA with operational data for streaming analytics
  • “Streaming” data in a LDWA
  • Pushing streaming analytics to the point where the streaming data is produced

Self-Service BI and the Logical Data Warehouse Architecture

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

Migrating to a Logical Data Warehouse

  • An A to Z roadmap
  • Guidelines for the development of a LDWA
  • 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

Summary and Conclusions

This course is intended for everyone who needs to be aware of developments in the field of business intelligence and data warehousing, such as:

  • BI Architects
  • Business Analysts
  • Data Warehouse Managers
  • System Analysts
  • Consultants
  • Technology Planners
  • Project Managers
  • Database Designers & Database Experts

Some knowledge of the classical data warehouse architecture is required.

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 helped  many 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

Group Booking Discount

  • 2-3 Delegates - 10%
  • 4-5 Delegates - 20%
  • 6+ Delegates - 25%

Multiple Seminar Booking Discount

Attend more than one of our seminars and you will be entitled to the following discounts:

  • 2nd course 10%
  • 3rd course 15%
  • 4th course 20%
  • 5th+ course 25%

Please note, only one discount can be applied at any one time.

15-16 June 2017
VENUE: etc.venues Marble Arch  
Garfield House,
86 Edgware Rd,
London W2 2EA
Phone: +44 (0) 20 7793 4200

London Accommodation: IRM UK in association with JP Events Ltd has arranged special discounted rates at all venues and at other hotels nearby the venue. Please visit the JP Events website for further information.

Email: jane@jpetem.com Tel +44 (0)84 5680 1138 Fax +44 (0)84 5680 1139.

In-House Training
If you require a quote for running an in-house course, please contact us with the following details:

  • Subject matter and/or speaker required
  • Estimated number of delegates
  • Location (town, country)
  • Number of days required (if different from the public course)
  • Preferred date

Please contact:
Jeanette Hall
E-mail: jeanette.hall@irmuk.co.uk
Telephone: +44 (0)20 8866 8366
Fax: +44 (0) 2036 277202

Speaker: Rick F. van der Lans
Rick F. van der Lans

Enterprise Data Management Series
Ten Steps to Data Quality
Incorporating Big Data, Hadoop and NoSQL in BI Systems and Data Warehouses
Managing Your Information Asset
Predictive & Advanced Analytics
Building an Enterprise Data Lake & Data Refinery for Enterprise Data as a Service
Business-Oriented Data Modelling
Advanced Data Modelling: Communication, Consistency, and Complexity
The Logical Data Warehouse - Design, Architecture, and Technology
Information Management Fundamentals
Data Modelling Fundamentals
Data Modelling Masterclass

Multiple Seminar Booking Discount
Attend more than one of our seminars and you will be entitled to the following discounts:

  • 2nd course 10%
  • 3rd course 15%
  • 4th course 20%
  • 5th+ course 25%

Group Booking Discount

  • 2-3 Delegates - 10%
  • 4-5 Delegates - 20%
  • 6+ Delegates - 25%

We regret that this offer cannot be used in conjunction with the Multiple Seminar Discount or any other discount.

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