在基于postgres数据库的数据仓库中实现继承的最佳方法

Question

I am developing a multi-step data pipeline that should optimize the following process:

1) Extract data from a NoSQL database ( MongoDB ).

2) Transform and load the data into a relational ( PostgreSQL ) database.

3) Build a data warehouse using the Postgres database

I have manually coded a script to handle steps 1) and 2), which is an intermediate ETL pipeline. Now my goal is to build the data warehouse using the Postgres database, but I came across with a few doubts regarding the DW design. Below is the dimensional model for the relational database:

There are 2 main tables, Occurrence and Canonical , from which inherit a set of others (drawn in red and blue, respectively). Note that there are 2 child data types, ObserverNodeOccurrence and CanonicalObserverNode , that have an extra many-to-many relationship with another table.

I made some research regarding how inheritance should be implemented in a data warehouse and figured the best practice would be to merge together the family data types (super and child tables) into a single table . Doing this would imply adding extra attributes and a lot of null values. My new dimensional model would look like the following:

Question 1: Do you think this is the best approach to address this problem? If not, what would be?

Question 2: Any software recommendations for on-premise data warehouses? (on-premise is a must since it contains sensitive data)

Answer 1

Usually having fewer tables to join and denormalizing data will improve query performance for data warehouse queries, so they are often considered a good thing.

This would suggest your second table design. NULL values don't occupy any space in a PostgreSQL table, so you need not worry about that.

Answer 2

As described here there are three options to implement inheritance in a relational database.

IMO the only practicable way to be used in data warehouse is the Table-Per-Hierarchy option, which merges all entities in one table.

The reason is not only the performance gain by saving the joins . In data warehouse often the historical view of the data is important. Think, how would you model a change in a subtype in some entity?

An important thing is to define a discriminator column which uniquely defines the source entity.