This is a continuation of the previous part, where there was described a problem statement related to the analysis of IoT data on the example of fitness tracking activities. There were also described the reasoning behind the storage type selection and recommended to have 2 types of storages:
The purpose of the story is to describe the recommended data model for the second type of storage — relational analytics storage. The logical data model for the data warehouse part.
Fortunately, the Data warehousing concept is already on the market for decades so it is well matured and many thought leaders contributed to best practices and patterns creation around solution building and data modeling.
Let’s use Ralph’s Kimball methodology to approach the modeling and a couple of patterns for particular design decisions.
From the methodology standpoint we going to do the following steps:
We gather these details in a **Bus matrix, **which isthe first high-level version of the model described by the business terms and is well suitable and recommended for sharing and discussing with all kinds of stakeholders.
The particular design patterns used to create a logical data model will be:
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IOT Data Analytics: Data Model. Best Practices of DW Modelling applied on IoT data for most flexible and efficient analytics