Analyzing 33 Million Bike Trips with BigQuery

Explore the power of BigQuery for analyzing massive datasets in this conference talk. Dive into a case study of 33 million NYC bike share trips, learning how to spot trends and create insightful visualizations. Follow along as queries are run live, demonstrating how to import data, use client libraries, and link applications to BigQuery. Discover techniques for creating interactive maps, pie charts, and bar graphs using Data Studio to visualize popular routes and rider demographics. Gain practical knowledge on working with public datasets, including GitHub data and Oslo bicycle information. Leave equipped with the skills to apply these big data analytics techniques to your own projects, whether using C# or exploring other public datasets available through Google Cloud.

Introduction
Story
Questions
City Bike
CSV Data
Subscriber Data
BigQuery Overview
Visualization
Importing data
Using the client library
Importing data via web UI
Linking your app to BigQuery
City bike trips table
Count rows
City bike system
Running the query
Data Studio
Create a new data source
Create a custom query
Connect to Data Studio
Add Data to Report
Bar Chart
Most Popular Stations
Map Data
Interactive Map
Trips by Member Type
Create a pie chart
Create new data source
Create custom query
Sort by user type
Gender and age
Public graphs
Routes by gender
Routes by men
Routes by women
Find the bike
Run the query
Animation
Data Visualization
Public Data Sets
GitHub Data
File Table
Run Query
Other public datasets
Oslo Bicycle
Public Data
Personal Projects
Map
Resources
CSharp
NYC Bike Data
Google Cloud Blog