Parking Ticket Data Exploration

Description of Data

Column Name	Description	DataType
ticket_number	Ticket number	Text
issue_date	Issue date	Floating Timestamp
issue_time	Issue time	Number
meter_id	Meter ID	Text
marked_time	Marked time	Text
rp_state_plate	State plate registered	Text
plate_expiry_date	Plate expiry date	Text
vin	Vehicle Identification Number	Text
make	Vehicle make	Text
body_style	Body style	Text
color	Color	Text
location	Location	Text
route	Route	Text
agency	Agency	Number
violation_code	Violation code	Text
violation_description	Violation description	Text
fine_amount	Fine amount	Number
agency_desc	Agency description	Text
color_desc	Color description	Text
body_style_desc	Body style description	Text
loc_lat	Latitude	Number
loc_long	Longitude	Number
geolocation	Geolocation	Point

Project Overview

This project explores the parking ticket data provided by the City of Los Angeles API, powered by Socrata. The goal is to demonstrate ETL with various DB, how to retrieve, process, and serve this data using Python web frameworks: Flask and FastAPI. This comparison focuses on understanding the performance, ease of use, and feature sets provided by different databases and frameworks.

Technologies Used

• Python 3
• DuckDB
• Flask
• FastAPI
• Requests library
• Jinja2 (for Flask templates)
• Uvicorn (ASGI server for FastAPI)

Using DuckDB with Parking Ticket Data

In this project, DuckDB, an embedded SQL OLAP database, was utilized to efficiently manage and query a dataset of parking ticket data. DuckDB's unique capabilities allowed for both in-memory and on-disk data management, providing flexibility in data processing and analysis.

Key Features

• Storage Modes: Configured for both rapid in-memory operation and durable on-disk storage.
• Schema Implementation: Custom schema applied to accommodate data attributes such as issue_date, fine_amount, and location.
• Efficient Querying: Structured queries enabled detailed analysis and report generation, supported by DuckDB's columnar storage and optimized execution.

Benefits

• Performance: Accelerated data operations ideal for analytical tasks.
• Ease of Use: Simple integration with Python for straightforward project implementation.
• Adaptability: Supports varying scenarios with in-memory and persistent storage options.