Difference between Stagining Area and ODS in Datawarehouse Modeling

I'm working on a BI project and I have a big confusion.

My data source is split into two categories:

• An SQL Script containing some tables
• Some Excel files

These datas should be in a single datawarehouse

According to the BI Data Modeling Pipeling , I should pass by:

Data Source => Staging Area => ODS => Datawarehouse

For my case , I didn't use ODS and I did as following:

• For the tables in the SQL script , I created a new empty database connection called "SA" in which I then
  executed the SQL script in the Oracle SQL developer which generated the source tables in my staging area.
  Of course , I didn't do any transformation or anything.

Then I did one job in Talend , to load each dimension of the datawarehouse , I put the Staging Area tables
as my sources , did some transformations , created derived columns ect... and loaded the transformed data
in my dimensions.

• For the Excel files , I did two Talend jobs.
  • First One , to load Excel File into ==> Staging Area (without any transfomration)
  • Then , The Stagining I loaded will be the source in my second job where I'll be performing some transformation and cleaning

I was wondering if I'm doing everything right and following best practices because I feel like I never understodd about the specific transformation that should be done in ODS & Staging Area.
And if ODS is mandatory or not , like when do I exactly use it?

In the BI Data Modeling Pipeline, the typical flow is indeed Data Source -> Staging Area -> Operational Data Store (ODS) -> Data Warehouse. However, the specific implementation of this pipeline can vary depending on the requirements and complexity of your project.

It seems like you have taken a simplified approach by combining the Staging Area and ODS into a single staging area. This is not uncommon, especially in smaller projects or when the data does not require extensive transformations in the ODS layer. By directly loading the SQL script tables into the staging area and performing transformations there, you have effectively bypassed the ODS layer.

Regarding your approach for the Excel files, it is generally a good practice to separate the loading of data into the staging area from the transformation process. This allows for better control and reusability of the data loading step. By first loading the Excel files into the staging area and then performing transformations in a separate job, you have followed a logical flow.

While the ODS layer is not mandatory, it serves as an intermediate layer between the staging area and the data warehouse. Its purpose is to integrate and refine data from different sources before loading it into the data warehouse. The ODS layer can perform additional data cleansing, data validation, consolidation, and aggregation as required. It acts as a staging area for refined data before it is further transformed and loaded into the data warehouse.

In your case, since you have directly transformed the data from the staging area and loaded it into your dimensions, you have skipped the ODS layer. This approach can be acceptable if your transformations and data refinement processes are minimal and can be efficiently performed within the staging area itself.

However, in more complex scenarios or when dealing with multiple data sources, incorporating an ODS layer can provide additional benefits as allowing more comprehensive data integration, consolidation, and validation, enabling you to ensure data quality and consistency before loading it into the data warehouse.