udemy-complete-dbt-bootcamp/notes/sections1-7.md

## Data Maturity Model

Maslow hierarchy of needs, data version: 

![img.png](../images/pyramid_:of_data_needs.png)

### Data collection

You get data from places.

You place it as is in some staging area.


### Data wrangling

Cleaning stuff up, deduplicating, yadi yada.


### Data integration

Place everything together nicely in the same place in a usable model.

## ETL

ETL made sense before. Storage was expensive and you only wanted to load the strictly necessary data into the target DWH.

But this comes with problems:
- You have a lot of statefulness.
- Debugging and testing pipelines is a pain in the ass.
- You need to do transformations outside of your target database.
- Schema changes were nightmares.

ELT is the new shiny toy:
- We read raw data from source system and load it into our DWH/Data Lake.
- We do our transformations in the target system.
- Schema changes become much more manageable.


## Datawarehouses and data lakes

### Datawarehouse

DWH -> Any database that:
- We use to ground our BI and reporting on.
- Optimized for reads.
- Typically structured in facts and dimensions.

### Data Lake

Decouple storage from compute. Use S3/Blob Storage/Hadoop HDFS for storage. Everything gets stored as files. Use a separate query engine, like Athena, Trino, Spark.

### Data Lakehouse

Just use both. A data lake with some DWH layer on top. Pretty much, a swamp of files with some governance, modelling tool sitting on top of it to control access and ease queries.


## The modern data stack

Cheaper storage -> We don't mind duplicating data more.
Faster networking -> We can spread work across more machines and decouple things like storage and processing. We can distribute workloads with distributed storage and compute.

![img.png](../images/example_of_modern_datastack.png)

dbt makes sense nowadays because the modern data stack makes transformations within the datawarehouse.

## Slowly Changing Dimensions

- The issue comes when a dimension changes in a way that would break referential integrity.
- Sometimes, old data can be thrown away. Sometimes, not.
- There are 4 SCD types.
  - SCD 0 - Retain original
    - Do not update data in the DWH. Source data and DWH gets out of sync.
    - You do this when you don't care about the dimension truly.
    - Example: Fax numbers when fax is not used anymore.
  - SCD 1 - Overwrite
    - Overwrite new values in DWH. Old values go away.
    - We only care about the new state. We don't need the history. 
  - SCD 2 - Add new row
    - Add new raw with `start_date` and `end_date` fields to indicate which values should be looked at depending on time.
    - Used when full historical view is important.
    - Increases amount of data stored.
  - SCD 3 - Add new attribute
    - Keep current attribute value and previous value
    - It only keeps the previous type at most
    - Intermediate approach between SCD2 and SCD1


## dbt overview

- dbt takes care of the T in ETL/ELT.
- dbt works within the datawarehouse and with SQL.
- Why not use raw SQL and that's it?  Because dbt brings good software practices like modularity, version control, reusability, testing, documentation and such to SQL swamps.

## Case

- ELT in Airbnb.
- Data from insideairbnb.com/berlin/
- The project will use snowflake as a DWH and preset (managed superset) as a BI tool.


## dbt project structure


- `dbt_project.yml`: header of the project, with stuff like versioning, the default profile for the project, the paths to different folders, etc.

This is a pic of the data flow we are going to build: ![img.png](../images/dataflow_overview.png)