Google Cloud is seen as a leader in areas including data analytics, machine learning and open source. And digital transformation through the cloud allowed companies to deliver personalised, high quality experiences.
During this Lockdown time in New Zealand, working from home means taking less time on the traffic and a time to learn more advanced techniques.
So stay positive and stay safe!
Thanks to GCP fundamentals, it is a perfect opportunity for those who wants to learn Google Cloud Platform.
If you are interested in or have any problems with Business Intelligence, feel free to contact me.
First of all, a concept of Data Warehouse is supposed to be clear. Data Warehouse is not a copy of source database with a name prefixed with ‘DW’.
DW is we can store data from multiple data sources to be used for historical or analysis report. Some data sources are .csv format, some are google docs, etc. So we need to aggregate them into one data source.
This is called Data Warehouse.
How to design it? The procedure is Dimensional Modelling.
What is the difference between relational and dimensional?
It is same as the difference like normalised VS denormalised.
Minimal data redundancy
Optimised for fast read and fast write
Current data only
Redundancy data storage for performance
Fast read only
Current and historical data
The next part is about fact table and dimension table in dimensional design.
Data that can be measured
Contains surrogate key, linking the associated measures or facts
Some types of dimensional models:
Accumalating snapshot table
Factless Fact table
If you are interested in or have any problems with Dimensional Modelling, feel free to contact me.