Ever since Ralph Kimball, the guru of dimensional modeling, announced his retirement, it has felt like the end of an era. He was among the first data warehousing/business intelligence pioneers some 20 years ago and although there has been much advancement in the field since then, his dimensional modeling principles are still strongly rooted and…
Relational databases introduced data modeling concepts where we represent a data model with tables, fields and relationships. A new generation of NoSQL databases which are more suitable for big data environments no longer rely on relational data models. We have to think about data modeling in a different way, primarily to ensure that data is…
In data warehousing and business intelligence implementations we usually start by choosing between two most popular approaches. One of them is the enterprise normalized data warehouse approach as defined by Bill Inmon, the father of data warehousing. The second approach is a collection of dimensional data marts based on a common bus architecture as popularized…
Loading data into data warehouses, also known as the ETL process, is an established way of taking data from the source systems and bringing it into the data warehouse. The process consists of three steps: Extract data from the source systems, Transform the data so that it conforms to the data warehousing environment and finally Load…
When we talk about Big Data, we nearly always associate Hadoop as the platform for storing huge amounts of data in a distributed environment. We often include MapReduce as the programming model for processing large data sets. Although it has been known for some time that MapReduce has limitations and is thus not universally applicable…
A large number of companies are starting to implement at least a few of their projects using agile approaches. They do so because it is becoming known that agile projects are more successful and their stakeholders more satisfied. It is risky if a company starts implementing agile before they understand exactly what it means in…
The book Data Smart: Using Data Science to Transform Information into Insight by John W. Foreman is one of those fantastic books that upon reading it I kept on asking myself why I haven’t come across it sooner. The author makes everything about data science appear less mysterious and so much clearer. On the one hand,…
Article published in MonitorPro 04/14, p. 39-40. Do small businesses have big data and if so, how can they take advantage of big data analytics? The perception that small businesses have too little data or that there is no time or interest to perform data analytics or that it is too expensive is simply not true….
“Big data” generally refers to enormous volumes of data stored in distributed NoSQL databases on massive servers with parallel processing. Thus many people believe that advanced analytics or data science is the same as buying the required technology. While we do need technology to analyze huge amounts of data, we also have to understand the…
According to many sources, agile data warehousing is the way to go in the future. Recently, Larissa Moss spoke about this topic at the TDWI conference in Munich, Germany in her session Extreme Scoping: Agile approach for enterprise-class EDW/BI. The main point of her session was the importance of the availability of data that is clearly understood…