Event Details

The world of business intelligence (BI) and data warehousing uses a unique terminology and deploys its own set of technologies, design techniques, and products. For newcomers all this can be overwhelming. What do all these new terms exactly mean, such as star schema, data mart, ETL, self-service BI, data science, big data, staging area, and BI in the cloud? In this new course, which has been designed specifically for newcomers to BI and Data Warehousing,  all the typical BI terms, concepts, techniques, architectures and technologies are explained. It is a complete and critical introduction.

The well-known data warehouse architectures of Bill Inmon and Ralph Kimball are explained in detail, including all the database components that they are made up of: staging area, operational data store, enterprise data warehouse and data mart. Their respective use cases, benefits, and drawbacks are discussed. Additionally, new upcoming architectures are clarified, such as datawarehouse in the cloud and the logical data warehouse architecture.

For designing all these databases special techniques exist, such as star schemas, snowflake schemas, and datavault. Here, a specific terminology is used as well, such as fact table, dimensional table, hierarchy, hub table, and slowly-changing dimensions.  Working with ETL products will be discussed in relationship to topics such as data quality, data profiling and master data management.

The wide range of available technologies for data storage with their pros and cons are systematically discussed; from the traditional SQL databases to the new Hadoop technology, from analytical SQL database servers to SQL-on-Hadoop, and from on-premise data storage to storage in the cloud. And with all this new technology, what’s the role of good old SQL?

For reporting and delivering data to business users a wide range of products is available, from simple reporting tools to the most advanced data science tools. Products from IBM, Information Builders, Microsoft, Oracle, Qlik, SAP, SAS, Tableau, and many others are discussed and compared. The impact of new technologies for analytics, such as Spark and Hadoop, are explained. The capabilities for analyzing unstructured data, such as text, audio and video are discussed, together with the new world of streaming analytics.

At the end of this course, you will have a thorough understanding of  the world of business intelligence and data warehousing.  You will learn about the techniques, the technologies  and the numerous products that are being applied. It is a complete and practical introduction to business intelligence and data warehousing and will help you on your way in BI projects.

Course Outline

The Importance of Business Intelligence for Organizations

  • Data as competitive business asset
  • The history of business intelligence
  • Why reporting and analytics directly on the production systems is not recommended?
  • From reporting via self-service BI to data science and statistics
  • The data sources for business intelligence: transactional systems and open data

Overview of Database Technology

  • Working with standard SQL database servers
  • The importance of processing database queries as close to the stored data as possible
  • Pros and cos of in-memory database technology
  • What’s the added value of analytical SQL products, such as Exasol, IBM Netezza, and Teradata?
  • Making use of multi-dimensional cubes to speed up data access
  • Keeping data in memory with BI tools and Apache Spark

Traditional Data Warehouse Architectures

  • Every traditional data warehouse architecture consist of a chain of databases
  • Well-known  databases in the chain: staging area, operational data store, data warehouse, and data marts
  • Why do we need a  staging area and what is the relationship with change data capture and  data replication?
  • The enterprise data warehouse as centralized data store for all the data needed for reporting and analytics
  • Using data marts to speed up query performance
  • The influence of big data

Designing Data Warehouses and Data Marts

  • Overview of design techniques: normalization, denormalization, star schema, snowflake schema
  • Facts and dimensions
  • Hierarchies of dimensions
  • Modelling time with slowly-changing dimensions
  • Storing derived and aggregated data
  • The special tables date and time
  • The influence of datavault on the architecture and usage

Using ETL to Pump Data Through the Chain

  • What is the functionality of an ETL product?
  • ETL is about data integration, filtering, cleansing, aggregation, and transformation
  • What do they mean exactly with pushdown?
  • Improving data quality
  • What is the difference between data cleansing and data profiling?
  • Overview of ETL products
  • The role of master data management when integrating data

Overview of the Tools for Reporting and Analytics

  • Identifying different types of users
  • The importance of a central metadata repository
  • Features and overview of tools for reporting and dashboards,
  • Tools for self-service BI and self-service data preparation, including PowerBI, QlikSense, Tableau, Paxata, SnapLogic, and Trifacta
  • Tools for statistics and data science
  • The role of an integrated BI platform

Modern Data Warehouse Architectures

  • The logical data warehouse architecture
  • Data integration with data virtualization tools
  • The data lake for the data scientists
  • Fast data, streaming data, and the Internet-of-Things

The Organization of the Data Warehouse

  • The relationship between business intelligence and data governance
  • The difference between data owners, data stewards, and data custodians
  • The importance of a data strategy and the alignment with the business strategy
  • The data lifecycle: creation, distribution, use maintenance, preservation, and disposal
  • The waterfall or the agile approach

Special Features of the Course

  • Presented by an international and independent authority on business intelligence
  • A non-traditional approach of explaining the BI concepts that includes all the new trends and technologies, including agile development, data science, big data, and logical data warehouses
  • An interactive style that allows attendees to discuss their own BI situation
  • Learn to speak the business intelligence lingo
  • Understand when to use which technique and which technology
  • Learn how other organizations are developing data warehouse environments
  • Understand the differences between tool categories
  • Understand how all the pieces make one big BI puzzle
Who's it for?
  • New managers of BI or Data Warehouse departments
  • Developers starting in BI projects
  • Those who want to understand how the data warehouse and business intelligence operates
  • BI Developers who want to improve their knowledge of BI developments
  • Business representatives  involved in BI projects and need to understand the terminology
  • Managers and IT Architects who need to understand the value of data warehousing and business intelligence
Rick van der Lans
Rick van der Lans
Founder of R20/Consultancy BV, Ambassador of Axians Business Analytics Laren
R20/Consultancy BV
Rick van der Lans is a highly-respected independent analyst, consultant, author, and internationally acclaimed lecturer specializing in data architectures, data warehousing, business intelligence, big data, data virtualization, and database technology. He works for R20/Consultancy, which he founded in 1987. In 2018 he was selected the sixth most influential BI analyst worldwide by onalytica.com. He has presented countless seminars, webinars, and keynotes at industry-leading conferences. For many years, he served as the chairman of the annual European Enterprise Data and Business Intelligence Conference in London and the annual Data Warehousing and Business Intelligence Summit in The Netherlands. He presents seminars, keynotes, and inhouse sessions on the following topics: Modern data architectures Big data and analytics Data virtualization The logical data warehouse Data warehousing and business intelligence Rick helps clients worldwide to design their data warehouse, big data, business intelligence, and streaming architectures and solutions and assists them with selecting the right products. He is ambassador of Axians Business Analytics Laren (formerly Kadenza), an international consultancy company specializing in business intelligence, data management, big data, data warehousing, data virtualization, and analytics. He is the author of several books on computing. Some of these books are available in different languages. Books such as the popular "Introduction to SQL" is available in English, Dutch, Italian, Chinese, and German and is sold worldwide. He was the author of the first available book on SQL, entitled including Introduction to SQL, which has been translated into several languages with more than 100,000 copies sold. More recently, he published two books on Data Virtualization: Selected Writings and Data Virtualization for Business Intelligence Systems.