The Essentials of Business Intelligence and Data Warehousing

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Title: The Essentials of Business Intelligence and Data Warehousing

Introduction

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 seminar, which has been set up specifically for newcomers, all the typical BI terms, concepts, techniques, architectures, and technologies are explained. It’s 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 receive quite some attention and is 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 en for 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.

To summarize, after this seminar you have a thorough understanding of the world of business intelligence and data warehousing. You will know 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.

Subjects

1. 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

2. 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

3. 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

4. Designing Data Warehouses and Data Marts

Overview of design techniques: normalization, denormalization, star schema, snowflake schema
Facts and dimensions
Hierarchies of dimensions
Modeling 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

5. 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

6. 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

7. 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

8. 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