Green_Taxi_ETL: Mage.ai Pipeline for Green Taxi Data

This project implements a data ETL pipeline using Mage.ai to process green taxi trip data for the final quarter of 2020 (October, November, December).

Getting Started:

1. Ensure you have Mage.ai installed and configured. Refer to the official documentation for installation instructions: https://docs.mage.ai/introduction/overview
2. Create a configuration file (io_config.yaml) containing your specific database connection details (not included in this repository for security reasons).
3. Execute the pipeline using Mage.ai commands. Refer to the Mage.ai documentation for available commands.

The pipeline consists of three key blocks:
Data loader, Transformer, Exporter

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1. Data Loader Block

• Leverages Pandas to read compressed CSV files (.csv.gz) from a specified URL for each month.
• Iterates through a list of target months (October, November, December) to download and load data.
• Parses the lpep_pickup_datetime column as datetime format during data loading.
• Concatenates the dataframes from each month into a single dataframe.

2. Transformer Block

• Filters out records with zero passengers or zero trip distance.
• Creates a new column lpep_pickup_date by extracting the date from lpep_pickup_datetime.
  -Renames columns from snake_case to lowercase with underscores removed (e.g., vendorID becomes vendor_id).
• Includes unit tests (using @test decorator) to verify data integrity after transformation.
  • Ensures there are valid vendor IDs present.
  • Asserts that minimum passenger count and trip distance are greater than zero.

3. Expoter Block

• Utilizes Mage.ai's Postgres exporter to write the transformed data to a PostgreSQL database.
• Configuration for the database connection and table details is expected to be defined in a separate io_config.yaml file.
• Exports the data to a specified schema (ny_taxi) and table (green_cab_data).
• Handles existing table scenarios by replacing the data (if_exists='replace').

Key Features:

• Modular and reusable code blocks with clear functionalities.
• Leverages decorators for data loading, transformation, and exporting.
• Employs unit tests to ensure data quality after transformations.
• Utilizes configuration files for database connection details (separate file).

Benefits:

• Streamlines data processing for green taxi trip data, enabling efficient analysis.
• Provides a clean and maintainable codebase using Mage.ai features.
• Improves data quality through filtering and assertions.
• Enables flexible configuration for database connection and table details.