Phase one (Data Analysis):

• The Exploratory data analysis file could be found at both:
• notebooks/exploratory_data_analysis.ipynb
• Redundancy in case of problems with jupyter notebook:
• notebooks/exploratory_data_analysis.py

Phase two (Data transformation):

• The second file respective to pipeline, could be found thought the following path:
• etls/insert_data_into_bigquery.py
• It's a classic extract, transform and load (ETL) pipeline.
• It takes data from Excel files and transform them with pandas to load out to BigQuery, the database system of my choice.
• There's a shell script to execute the pipeline that could be executed with the following:
• bash scripts/execute_pipeline.sh

Phase three (Data visualization):

• I've used Looker studio as reporting tool, basically because of the integration with Google's BigQuery.

Looker studio report link:

• https://lookerstudio.google.com/u/0/reporting/7fdc99f6-41ed-425c-980f-705113b002f3/page/Dov1D

Phase Four (Database):

• You could find out the both queries respectively at:
• queries/five_worst_days_in_sales.sql
• This query had the goal to identify the worst days in sales, so we could take actions against them.
• queries/top_5_days_with_more_clients.sql
• This query had the goal to identify what days and periods were more effective in terms of marketing, to reuse the same strategies