Create a Cloud SQL instance
Whitelist the Cloud Shell instance to access your SQL instance
Create a bts database and flights table using the create_table.sql file
Loading Data into Google Cloud SQL
In this lab, you import data from CSV text files into Cloud SQL and then carry out some basic data analysis using simple queries.
The dataset used in this lab comes from the US Bureau of Transport Statistics and contains historical information about internal flights in the United States. This dataset can be used to demonstrate a wide range of data science concepts and techniques and is used in all of the labs in the Data Science on Google Cloud Platform Quest.
- Create Cloud SQL instance
- Create a Cloud SQL database
- Import text data into Cloud SQL
- Build an initial data model using queries
Setup and requirements
Before you click the Start Lab button
Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources will be made available to you.
This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab.
To complete this lab, you need:
- Access to a standard internet browser (Chrome browser recommended).
- Time to complete the lab---remember, once you start, you cannot pause a lab.
How to start your lab and sign in to the Google Cloud Console
Click the Start Lab button. If you need to pay for the lab, a pop-up opens for you to select your payment method. On the left is the Lab Details panel with the following:
- The Open Google Console button
- Time remaining
- The temporary credentials that you must use for this lab
- Other information, if needed, to step through this lab
Click Open Google Console. The lab spins up resources, and then opens another tab that shows the Sign in page.
Tip: Arrange the tabs in separate windows, side-by-side.
Note: If you see the Choose an account dialog, click Use Another Account.
If necessary, copy the Username from the Lab Details panel and paste it into the Sign in dialog. Click Next.
Copy the Password from the Lab Details panel and paste it into the Welcome dialog. Click Next.
Important: You must use the credentials from the left panel. Do not use your Google Cloud Skills Boost credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.
Click through the subsequent pages:
- Accept the terms and conditions.
- Do not add recovery options or two-factor authentication (because this is a temporary account).
- Do not sign up for free trials.
After a few moments, the Cloud Console opens in this tab.
Activate Cloud Shell
Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.
- In the Cloud Console, in the top right toolbar, click the Activate Cloud Shell button.
- Click Continue.
It takes a few moments to provision and connect to the environment. When you are connected, you are already authenticated, and the project is set to your PROJECT_ID. The output contains a line that declares the PROJECT_ID for this session:
gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.
(Optional) You can list the active account name with this command:
(Optional) You can list the project ID with this command:
gcloud, in Google Cloud, Cloud SDK documentation, see the gcloud command-line tool overview.
Task 1. Prepare your environment
This lab uses a set of code samples and scripts developed for the Data Science on the Google Cloud Platform, 2nd Edition book from O'Reilly Media, Inc. It covers the configuration of Google Cloud SQL and importing data tasks covered in the first part of Chapter 3, "Creating Compelling Dashboards". You clone the sample repository used in Chapter 2 from Github to the Cloud Shell and carry out all of the lab tasks from there.
Clone the Data Science on Google Cloud repository
In Cloud Shell enter the following commands to clone the repository:
Change to the repository directory:
Create the environment variables used later in the lab for your project ID and the storage bucket that contains your data:
Enter following command to stage the file into Cloud Storage bucket:
Task 2. Create a Cloud SQL instance
Enter the following commands to create a Cloud SQL instance:
This takes a few minutes to complete.
Test completed task
Click Check my progress to verify your performed task. If you have successfully created a Cloud SQL instance, you will see an assessment score.
- Create an environment variable with the Cloud Shell IP address:
Allowlist the Cloud Shell instance for management access to your SQL instance:
When prompted press Y to accept the change.
Test completed task
Click Check my progress to verify your performed task. If you have successfully allowlisted Cloud Shell to access the SQL instance, you will see an assessment score.
Create database and table
To import data into a Postgres table, you first create an empty database and a table with the correct schema.
In the Cloud Console, on the Navigation menu (), click SQL.
To open the Overview page of an instance, click the instance name
Select Databases from the SQL navigation menu on the left.
Click Create database.
In the New database dialog, name the database
To open the Overview page of an instance, select Overview from the SQL navigation menu.
Click IMPORT on the top.
In the Cloud Storage file field, click Browse.
In the Buckets section, click the arrow opposite your bucket name.
Select the file
In the File format section, select SQL.
Specify the Database
btsin your Cloud SQL instance.
Click Import start the import.
A few seconds later, the empty table will be created.
Test completed task
Click Check my progress to verify your performed task. If you have successfully created a bts database and flights table using the create_table.sql file, you will see an assessment score.
Task 3. Add data to a Cloud SQL instance
You created the empty database and table, now load the CSV files into this table. Loading the January data by browsing to 201501.csv in your bucket and specifying CSV as the format, bts as the database, and flights as the table.
In your Cloud SQL instance page, click IMPORT.
In the Cloud Storage file field, click Browse, and then click the arrow opposite your bucket name, and then click
Select CSV as File format.
btsdatabase and type in
flightsas your table.
Task 4. Interact with the database
Connect to the Cloud SQL instance from Cloud Shell using:
When prompted for a password enter
Passw0rd. You may not see the letters as you type.
In the prompt that comes up, connect to the bts database:
When prompted for a password enter
Then, run a query to obtain the 5 busiest airports:
While this query is performant because the dataset is relatively small (only January!), the database will slow as you add more months.
Relational databases are suited to smallish datasets on which you perform ad hoc queries that return a small a subset of the data. For larger datasets, you tune the performance of a relational database by indexing the columns of interest. Further, because relational databases typically support transactions and guarantee strong consistency, they are an excellent choice for data that will be updated often.
However, a relational database is a poor choice if:
- Your data is primarily read-only
- If your dataset sizes go into the terabyte range
- You have a need to scan the full table (such as to compute the maximum value of a column) or if your data streams in at high rates.
This describes the flight delay use case. For this case you would switch from a relational database to an analytics data warehouse – BigQuery. The analytics data warehouse will allow us to use SQL and is much more capable of dealing with large datasets and ad hoc queries (i.e. doesn’t need the columns to be indexed).
Now you know how to create tables and import text data that has been stored on Cloud Storage into Google Cloud SQL.
Finish your Quest
This self-paced lab is part of the Data Science on Google Cloud and Cloud SQL Quests. A Quest is a series of related labs that form a learning path. Completing this Quest earns you the badge above, 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 Data Science on Google Cloud or Cloud SQL and get immediate completion credit if you've taken this lab. See other available Quests.
Take your next lab
Continue your Quest with Visualizing Data with Google Data Studio, or check out these suggestions:
Next steps / learn more
Here are some follow-up steps:
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Manual Last Updated April 1, 2022
Lab Last Tested March 7, 2022
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