In dimensional modeling there are various types of hierarchy that range from simplistic to complex hierarchies with multiple level starting points. These complex hierarchies are often referred to as ragged hierarchies. Ragged hierarchies can occur because some of your source data systems may capture data at a very granular level whilst another system may capture or output data at summary levels.
You may also be using third party data and wish to combine it with your in house data. When the data is consolidated it may not be possible to populate all levels of the hierarchy with the same coverage if at all.
In these situations it can be useful to undertake a mapping exercise with an outcome similar to that shown in the diagram. This should occur before modelling the hierarchies in dimension tables and the ETL build.
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I architect data solutions and superintend the solutions that I architect. I build teams that are usually a mix of full-time employees and sub-contractors. I ensure mentoring and knowledge transfer. The emergence of big data has opened the door to a rise in interest in predictive analytics aka machine learning. The technology changes have provided the opportunity to architect data solutions which combine enterprise data warehousing experience with data lake concepts and to apply knowledge of statistics to that data to deliver predictive analytics. To that end I’m putting effort into understanding how the evolving technologies hook in to each other. I have a focused interest in big data technologies especially on the Google Cloud Platform. Technologies such as Hadoop, Spark, Apache Beam, BigQuery and Tensorflow (machine learning) and their integration/virtualization with the enterprise data warehouse.
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