GSP848

Overview

Information access uses multiple formats, and BigQuery makes working with multiple data sources simple. In this lab you will get started with sports data science by importing external sports data sources into BigQuery tables. This will give you the basis for building more sophisticated analytics in subsequent labs.

The data used in this lab originates from the following sources:

• Pappalardo et al., (2019) A public data set of spatio-temporal match events in soccer competitions, Nature Scientific Data 6:236, https://www.nature.com/articles/s41597-019-0247-7
• Pappalardo et al. (2019) PlayerRank: Data-driven Performance Evaluation and Player Ranking in Soccer via a Machine Learning Approach. ACM Transactions on Intelligent Systems and Technologies (TIST) 10, 5, Article 59 (September 2019), 27 pages. DOI: https://doi.org/10.1145/3343172

In this lab, you will learn how to:

• Upload files from Google Cloud Storage (GCS) into BigQuery tables using the Cloud Console.

• Use the Cloud Console to access information derived from BigQuery tables.

• Understand how to write queries on the uploaded tables.

Task 1. Open BigQuery

The BigQuery console provides an interface to query tables, including public datasets offered by BigQuery.

1. In the Cloud Console, from the Navigation menu select BigQuery:

2. The Welcome to BigQuery in the Cloud Console message box opens. This message box provides a link to the quickstart guide and the release notes.
3. Click Done.
4. The BigQuery console opens.

In this section the BigQuery interface was used to access the console. The console provides a convenient way to add information to a dataset. BigQuery uses tables to represent data in a structured way.

In the next section learn more about BigQuery and how to create custom tables.

Task 2. Create custom tables

In this section, you will create a dataset. The dataset is used to add data to the project. Datasets utilize tables and views to help control access to data within a project.

1. In the BigQuery console, observe the Explorer section.
2. Click on the View actions icon next to your project ID and select Create dataset.

Note: A dataset is contained within a specific project. Datasets are top-level containers that are used to organize and control access to your tables and views. A table or view must belong to a dataset, so you need to create at least one dataset before loading data into BigQuery. Reference: BigQuery datasets introduction.

3. On the Create dataset page fill in the following:

Field	Value
Dataset ID	soccer
Data location	us (multiple regions in United States)
Default table expiration	Default

4. The BigQuery Create dataset screen will display information similar to below:

Note: Currently, the public datasets are stored in the US multi-region data locations. For simplicity, place your dataset in the same location.

5. Click Create dataset at the bottom of the panel.

Click Check my progress to verify the objective

Check a new dataset is created

In this section a new dataset was created using BigQuery. During this process, BigQuery needs to know where to store the information to be created. It also provides the option to include customer managed encryption, if required.

In the next section learn how to populate the created dataset with JavaScript Object Notation (JSON) a common data format.

Task 3. Load JSON Data

Now you will load the tables created previously with soccer data into the dataset.
BigQuery provides support for a number of import formats. In this lab use JSON with the dataset created in the previous section.

1. Create a table by clicking on the View actions icon next to your soccer dataset in the Explorer section.

2. Select Create table.

In the following section use the default values for all settings unless otherwise indicated. The data is stored in a public Google Cloud Storage (GCS) bucket.

3. On the Create table page add the following information:

Field	Value
Source	Google Cloud Storage
Select file from GCS bucket	spls/bq-soccer-analytics/competitions.json
File format	JSONL (Newline delimited JSON)
Table name	competitions
Schema	Check the box marked Schema Auto detect

Note: When using Cloud Storage buckets with BigQuery, it does not require the prefix of gs:// to be applied.

4. The BigQuery Create table screen will display information similar to below:

5. Click Create table.
6. Wait for BigQuery to create the table and load the data.
7. A pop up notification message saying "competitions" created is displayed.
8. The table will show up after the data is loaded.
9. Repeat the steps above for the other JSON data to be ingested.

GCS bucket file	Table name
spls/bq-soccer-analytics/matches.json	matches
spls/bq-soccer-analytics/teams.json	teams
spls/bq-soccer-analytics/players.json	players
spls/bq-soccer-analytics/events.json	events

Note: Use the exact Cloud Storage bucket files and table names shown.

10. Once the tables are created the display will be similar to below:

Click Check my progress to verify the objective Check a competitions table is created

In this section new tables were created using BigQuery. During this process, BigQuery used Cloud Storage as the source for the JSON files. Cloud Storage provides a good intermediate storage option for object files.

In the next section learn how to populate the created dataset with a comma-separated values (CSV) file, that is another common data format.

Task 4. Load CSV data

In this section, load another table of soccer data into the dataset. The load process will this time be sourced from a comma-separated values (CSV) file stored in Cloud Storage.

1. Create a table by clicking on the View actions icon next to your soccer dataset in the Explorer section, and select Create table.

Use the default values for all settings unless otherwise indicated.

2. On the Create table page add the following information:

Field	Value
Source	Google Cloud Storage
Select file from GCS bucket	spls/bq-soccer-analytics/tags2name.csv
File format	CSV
Table name	tags2name
Schema	Check the box marked Auto detect

3. The BigQuery Create table screen will display information similar to below:

4. Click Create table (at the bottom of the window).
5. Wait for BigQuery to create the table and load the data.

A pop up message will appear saying "tags2name" created.

6. The table will show up after the data is loaded.

Click Check my progress to verify the objective

Check the tags2name table is created

In this section a new table was created using BigQuery. During this process, BigQuery used Cloud Storage as the source for the CSV file. Cloud Storage provides a good intermediate storage option for object files.

Task 5. Preview tables

1. In the left pane, select soccer > competitions in the navigation panel.
2. In the Details panel, click the Preview tab.

3. Click through the other uploaded tables from the navigation panel.
4. Check the Schema, Details, and Preview tabs to learn more about the data in each table.

BigQuery provides a convenient way to store data previously held in a variety of formats. To learn more about data ingestion techniques for BigQuery read Choosing a data ingestion method.

In the next couple of sections learn how to query the datasets created in BigQuery.

Task 6. Query Player data

Now that you've loaded data into your tables, you can run queries against it. Next, create a query that retrieves the top 10 tallest defenders (for whom height is available) in the players table.

1. In the query Editor, click "+" (Compose new query) icon.

2. Copy and paste the following query into the query Editor:

SELECT (firstName || ' ' || lastName) AS player, birthArea.name AS birthArea, height FROM `soccer.players` WHERE role.name = 'Defender' ORDER BY height DESC LIMIT 5 Note: In the above query, use BigQuery to retrieve information relating to soccer players. Specifically query for a specific player role to understand the general characteristics of a defender.

3. Click Run. The results are displayed below the query window.

Click Check my progress to verify the objective

Check the query has been run

Understanding how to perform queries in BigQuery is essential. Running queries in BigQuery provides a simple interface to extract powerful data insights.

Task 7. Query events data

Create a query to retrieve counts of all event types that are found in the events table.

1. Copy and paste the following query into the query Editor:

SELECT eventId, eventName, COUNT(id) AS numEvents FROM `soccer.events` GROUP BY eventId, eventName ORDER BY numEvents DESC

2. Click Run. The results are displayed below the query window.

Note: In the above query, use BigQuery to retrieve information relating to events occurring within a soccer match. Specifically query for the frequency of events like passes and shots.

Click Check my progress to verify the objective

Check the query has been run

Being able to capitalize on stored data to establish trends and patterns presents an opportunity to deliver real benefit to end users. In the next section test your understanding of what you have learned in this introduction to BigQuery.

Task 8. Pop quiz

Test your understanding of BigQuery by completing the short quiz on the topics covered in this lab.

Congratulations!

You have successfully uploaded files stored in Cloud Storage into tables in BigQuery and learned how to compose queries to extract data from the tables.

Finish your quest

This self-paced lab is part of the Predict Soccer Match Outcomes with BigQuery ML quest. A quest is a series of related labs that form a learning path. Completing this quest earns you a badge to recognize your achievement. You can make your badge or badges public and link to them in your online resume or social media account. Enroll in this quest or any quest that contains this lab and get immediate completion credit. See the Google Cloud Skills Boost catalog to see all available quests.

Take your next lab

Continue your Quest with BigQuery Soccer Data Analysis.

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Manual Last Updated December 05, 2022

Lab Last Tested December 05, 2022

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