Data Modeling with Postgres

Table creation script runs without errors.

The script, create_tables.py, runs in the terminal without errors. The script successfully connects to the Sparkify database, drops any tables if they exist, and creates the tables.

Fact and dimensional tables for a star schema are properly defined.

CREATE statements in sql_queries.py specify all columns for each of the five tables with the right data types and conditions.

ETL script runs without errors.

The script, etl.py, runs in the terminal without errors. The script connects to the Sparkify database, extracts and processes the log_data and song_data, and loads data into the five tables.

Since this is a subset of the much larger dataset, the solution dataset will only have 1 row with values for value containing ID for both songid and artistid in the fact table. Those are the only 2 values that the query in the sql_queries.py will return that are not-NONE. The rest of the rows will have NONE values for those two variables.

ETL script properly processes transformations in Python.

INSERT statements are correctly written for each table, and handle existing records where appropriate. songs and artists tables are used to retrieve the correct information for the songplays INSERT.

The project shows proper use of documentation.

The README file includes a summary of the project, how to run the Python scripts, and an explanation of the files in the repository. Comments are used effectively and each function has a docstring.

The project code is clean and modular.

Scripts have an intuitive, easy-to-follow structure with code separated into logical functions. Naming for variables and functions follows the PEP8 style guidelines.