Using Joins and Group Bys the right way for data warehousing
Joins represent the denormalization of data to create dimensions and add dimension attributes to fact tables for reporting. Group bys enable the creation of metrics that determine how your data is used to make decisions.
If you have wondered
Why do your joins often end up in a dense block that's hard to pick apart.
Why are people running nearly identical queries with only different join combinations.
How to avoid losing data when performing joins
How group bys have been used to mask underlying data modeling issues
Why a group by all is considered a red flag
Then this post is for you. In this post, we will cover how to use joins and group bys, as well as what to look out for when using them.
By the end of this post, you will know what to watch out for when you perform joins or group bys.
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When discussing the normalisation process, it is always the First Normal Form that causes the most grief and confusion. Anith Sen takes up the challenge to explain, in simple terms, exactly what the First Normal Form really is, and why it is so important for Database Design. Along the way, he dispels some of the myths that have grown up around 1NF.
I've tried to look on Google about a decent explanation of weak and strong entity type, but I haven't fully understood them.
Could someone give me an example of a strong and weak entity type?
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northwind-date-warehouse/README.md at main · JokoCyber/northwind-date-warehouse
Designing a star schema data warehouse based on the Northwind database and implementing the ETL process using the SSIS tool. - JokoCyber/northwind-date-warehouse
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