Exploring London's Travel Network using Snowflake SQL

Analyzing Transport for London data over 12 years with Snowflake SQL to uncover usage patterns and insights.

Introduction

In this hands-on Snowflake SQL Data Analysis Project, I had used Snowflake SQL to analyze a database and performed exploratory analysis on Transport for London data (by transport type) over 12 years using Snowflake SQL. Data was stored in a Snowflake database (TFL) with a single JOURNEYS table and analyzed to uncover key insights. Their remit covers the London Underground, Overground, Docklands Light Railway (DLR), buses, trams, river services (clipper and Emirates Airline cable car), roads, and even taxis.

Note: that in Snowflake all databases, tables, and columns are upper case by default.

📊 Insights Required

The aim of this project is to analyze transport data and uncover the following key insights:

• Identify the most popular transport types by total number of journeys.
• Determine the most popular months and years for Emirates Airline.
• Find the least popular years for Underground & DLR journey types.

🔎 Query Execution & Findings

• MOST POPULAR TRANSPORT TYPES BY TOTAL NUMBER OF JOURNEYS

  

• MOST POPULAR MONTHS AND YEARS FOR EMIRATES AIRLINE

  

• LEAST POPULAR YEARS OF UNDERGROUND & DLR JOURNEY TYPE