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Study mode:
on
1
Introduction
2
Story
3
Questions
4
City Bike
5
CSV Data
6
Subscriber Data
7
BigQuery Overview
8
Visualization
9
Importing data
10
Using the client library
11
Importing data via web UI
12
Linking your app to BigQuery
13
City bike trips table
14
Count rows
15
City bike system
16
Running the query
17
Data Studio
18
Create a new data source
19
Create a custom query
20
Connect to Data Studio
21
Add Data to Report
22
Bar Chart
23
Most Popular Stations
24
Map Data
25
Interactive Map
26
Trips by Member Type
27
Create a pie chart
28
Create new data source
29
Create custom query
30
Sort by user type
31
Gender and age
32
Public graphs
33
Routes by gender
34
Routes by men
35
Routes by women
36
Find the bike
37
Run the query
38
Animation
39
Data Visualization
40
Public Data Sets
41
GitHub Data
42
File Table
43
Run Query
44
Other public datasets
45
Oslo Bicycle
46
Public Data
47
Personal Projects
48
Map
49
Resources
50
CSharp
51
NYC Bike Data
52
Google Cloud Blog
Description:
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.

Analyzing 33 Million Bike Trips with BigQuery

NDC Conferences
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