GSP283

Overview

Imagine you have datasets that live in different locations around the globe, and your data is in Google Cloud Storage buckets or in BigQuery tables. How can you organize that data so that it gets consolidated and analyzed to provide insights on your business?

Managed Service for Apache Airflow helps you build, schedule, and monitor workflows to move and process data across different regions and storage systems, using an intuitive graphical interface. It provides a flexible framework with operators and integrations that enable reliable data transfer between services such as BigQuery and Cloud Storage.

In this lab, you create and run an Apache Airflow workflow using Managed Service for Apache Airflow that completes the following tasks:

• Reads from a config file that provides a list of tables to copy
• Exports the list of tables from a BigQuery dataset located in US to Cloud Storage
• Copies the exported tables from the US to EU Cloud Storage bucket
• Imports the list of tables into the target BigQuery dataset in EU

What you'll do

In this lab, you learn how to perform the following tasks:

• Create a Managed Airflow environment.
• Create Cloud Storage buckets.
• Create BigQuery datasets.
• Create and run Apache Airflow workflows in Managed Airflow to move data between Cloud Storage and BigQuery.

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 are made available to you.

This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials 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).

Note: Use an Incognito (recommended) or private browser window to run this lab. This prevents conflicts between your personal account and the student account, which may cause extra charges incurred to your personal account.

• Time to complete the lab—remember, once you start, you cannot pause a lab.

Note: Use only the student account for this lab. If you use a different Google Cloud account, you may incur charges to that account.

How to start your lab and sign in to the Google Cloud console

1. Click the Start Lab button. If you need to pay for the lab, a dialog opens for you to select your payment method. On the right is the Lab setup and access panel with the following:

   • The Open Google Cloud console button
   • The temporary credentials (username and password) that you must use for this lab
   • Other information, if needed, to step through this lab

   Note that the lab timer is located near the top of the page, showing the remaining time.

2. Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).

   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.

3. If necessary, copy the Username below and paste it into the Sign in dialog.

   {{{user_0.username | "Username"}}}

   You can also find the Username in the Lab setup and access panel.

4. Click Next.

5. Copy the Password below and paste it into the Welcome dialog.

   {{{user_0.password | "Password"}}}

   You can also find the Password in the Lab setup and access panel.

6. Click Next.

   Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

7. 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 Google Cloud console opens in this tab.

Note: To access Google Cloud products and services, click the Navigation menu or type the service or product name in the Search field.

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.

1. Click Activate Cloud Shell at the top of the Google Cloud console.

2. Click through the following windows:

   • Continue through the Cloud Shell information window.
   • Authorize Cloud Shell to use your credentials to make Google Cloud API calls.

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:

Your Cloud Platform project in this session is set to {{{project_0.project_id | "PROJECT_ID"}}}

gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.

3. (Optional) You can list the active account name with this command:

gcloud auth list

4. Click Authorize.

Output:

ACTIVE: * ACCOUNT: {{{user_0.username | "ACCOUNT"}}} To set the active account, run: $ gcloud config set account `ACCOUNT`

5. (Optional) You can list the project ID with this command:

gcloud config list project

Output:

[core] project = {{{project_0.project_id | "PROJECT_ID"}}} Note: For full documentation of gcloud, in Google Cloud, refer to the gcloud CLI overview guide.

Task 1. Create a Managed Airflow environment

1. On the Google Cloud console title bar, type Managed Airflow in the Search field, then click Managed Airflow in the Products & Pages section to create a Managed Airflow environment.

2. Click Create environment > Managed Airflow Gen 3.

3. Set the following parameters for your environment and leave all other settings as default:

   • Name: airflow-advanced-lab

   • Location:

   • Image Version: composer-3-airflow-2.n.n-build.n

   • Service account: Compute Engine default service account

   • Resilience mode: Select Standard resilience, and for Airflow database zone, select

   • Under Environment resources, select Small.

   • For Web server network access control, select Allow access from all IP addresses.

4. Click Create.

The environment creation process is completed when the green checkmark displays to the left of the environment name on the Environments page in the Google Cloud console.

Note: It can take up to 20 minutes for the environment to complete the setup process. Move on to the next section Create Cloud Storage buckets and BigQuery destination dataset.

Click Check my progress to verify the objective.

Create a Managed Airflow environment.

Task 2. Create Cloud Storage buckets

In this task you will record your environment's DAGs bucket name and create two Cloud Storage multi-regional buckets. These buckets will be used to copy the exported tables across locations (from the US to the EU).

Record DAGs bucket name

When you created your Managed Airflow environment in Task 1, Google Cloud automatically provisioned a Cloud Storage bucket to store your environment's DAGs (workflows) and plugins. Locate and record this bucket name to use later in this lab.

1. Navigate to Cloud Storage > Buckets.
2. On the Buckets page, locate the bucket automatically created for your Managed Airflow environment. Its name follows the format -airflow-advance-xxxxxxxx-bucket (for example, us-central1-airflow-advance-73b9-bucket).
3. Copy and record this bucket name in a text editor for use later in the lab.

Create a bucket in US

Next, create a source Cloud Storage bucket in the US multi-region. The Airflow DAG will export your source BigQuery tables from the US dataset into this bucket before transferring them to Europe.

1. Navigate to Cloud Storage > Buckets and click Create.
2. Name the bucket -us.
3. For Location type, select us (multiple regions in United States).
4. Leave the other values as default and click Create.
5. In the Public access will be prevented dialog, select Enforce public access prevention on this bucket and click Confirm.

Create a bucket in EU

Next, create a destination Cloud Storage bucket in the EU multi-region. The Airflow DAG will copy the exported BigQuery table files from the US bucket into this EU bucket before loading them into your destination BigQuery dataset in Europe.

1. On the Buckets page, click Create bucket.
2. Name the bucket -eu and click Continue.
3. For Location type, select eu (multiple regions in European Union).
4. Leave the other values as default and click Create.
5. In the Public access will be prevented dialog, select Enforce public access prevention on this bucket and click Confirm.

Click Check my progress to verify the objective.

Create two Cloud Storage buckets.

Task 3. Create the BigQuery destination dataset

Create the destination BigQuery dataset in the EU from the Google Cloud console.

1. In the Google Cloud console, in the Navigation menu click BigQuery.

The Welcome to BigQuery in the Cloud Console message box opens. This message box provides a link to the quickstart guide and lists UI updates.

2. Click Done.

3. Click the three dots next to your Qwiklabs project ID and select Create dataset.

4. Set Dataset ID to nyc_tlc_EU and Data location type to EU (multiple regions in European Union).

5. Be sure Link to an external dataset is not selected.

6. Click Create dataset.

Click Check my progress to verify the objective.

Create a BigQuery dataset.

Task 4. Review Airflow core concepts

• While your environment is building, read about the sample file you'll be using in this lab.

Airflow is a platform to programmatically author, schedule and monitor workflows.

Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies.

Core concepts

DAG - A Directed Acyclic Graph is a collection of tasks, organized to reflect their relationships and dependencies.

Operator - The description of a single task; it is usually atomic. For example, the BashOperator is used to execute a Bash command.

Task - A parameterized instance of an Operator; a node in the DAG.

Task Instance - A specific run of a task; characterized as: a DAG, a Task, and a point in time. It has an indicative state: running, success, failed, skipped, ...

Learn more about Airflow concepts from the Concepts documentation.

Task 5. Define the workflow

Workflows in Apache Airflow are composed of DAGs (Directed Acyclic Graphs). The code in bq_copy_across_locations.py represents the workflow, also known as a DAG. Open the file to understand how the DAG is constructed. The next section provides a detailed look at its key components.

To orchestrate all the workflow tasks, the DAG imports the following operators:

1. DummyOperator: Creates Start and End dummy tasks for better visual representation of the DAG.
2. BigQueryToCloudStorageOperator: Exports BigQuery tables to Cloud Storage buckets using Avro format.
3. GoogleCloudStorageToGoogleCloudStorageOperator: Copies files across Cloud Storage buckets.
4. GoogleCloudStorageToBigQueryOperator: Imports tables from Avro files in Cloud Storage bucket.

• In this example, the function read_table_list() is defined to read the config file and build the list of tables to copy:

# -------------------------------------------------------------------------------- # Functions # -------------------------------------------------------------------------------- def read_table_list(table_list_file): """ Reads the master CSV file that will help in creating Airflow tasks in the DAG dynamically. :param table_list_file: (String) The file location of the master file, e.g. '/home/airflow/framework/master.csv' :return master_record_all: (List) List of Python dictionaries containing the information for a single row in master CSV file. """ master_record_all = [] logger.info('Reading table_list_file from : %s' % str(table_list_file)) try: with open(table_list_file, 'rb') as csv_file: csv_reader = csv.reader(csv_file) next(csv_reader) # skip the headers for row in csv_reader: logger.info(row) master_record = { 'table_source': row[0], 'table_dest': row[1] } master_record_all.append(master_record) return master_record_all except IOError as e: logger.error('Error opening table_list_file %s: ' % str( table_list_file), e)

• The name of the DAG is bq_copy_us_to_eu_01, and the DAG is not scheduled by default so needs to be triggered manually.

default_args = { 'owner': 'airflow', 'start_date': datetime.today(), 'depends_on_past': False, 'email': [''], 'email_on_failure': False, 'email_on_retry': False, 'retries': 1, 'retry_delay': timedelta(minutes=5), } # DAG object. with models.DAG('bq_copy_us_to_eu_01', default_args=default_args, schedule_interval=None) as dag:

• To define the Cloud Storage plugin, the class GCSPlugin(AirflowPlugin) is defined, mapping the hook and operator downloaded from the Airflow 1.10-stable branch.

# Import operator from plugins from gcs_plugin.operators import gcs_to_gcs

Task 6. View environment information

1. Go back to Managed Airflow to check on the status of your environment.

2. Once your environment has been created, click the name of the environment to see its details.

The Environment details page provides information, such as the Airflow web UI URL, Google Kubernetes Engine cluster ID, and name of the Cloud Storage bucket connected to the DAGs folder.

Note: Managed Apache Airflow services use Cloud Storage to store DAGs, also known as workflows. Each environment has an associated Cloud Storage bucket, and only the DAGs present in that bucket are scheduled by Airflow.

Complete the next steps in Cloud Shell.

Create a virtual environment

Python virtual environments are used to isolate package installation from the system.

1. Install the virtualenv environment:

sudo apt-get install -y virtualenv

2. Build the virtual environment:

python3 -m venv venv

3. Activate the virtual environment:

source venv/bin/activate

Task 7. Create a variable for the DAGs Cloud Storage bucket

• In Cloud Shell, replace [YOURDAGSBUCKET] in the command below with the DAGs bucket name you recorded in Task 2 and run the command:

Note: Make sure to replace your DAGs bucket name in the following command. Navigate to Navigation menu > Cloud Storage > Buckets, it will be similar to -airflow-advance-YOURDAGSBUCKET-bucket. DAGS_BUCKET=[YOURDAGSBUCKET]

You will be using this variable a few times during the lab.

Task 8. Set Airflow variables

Airflow variables are an Airflow-specific concept that is distinct from environment variables. In this step, you'll set the following three Airflow variables used by the DAG you will deploy: table_list_file_path, gcs_source_bucket, and gcs_dest_bucket.

Key	Value	Details
table_list_file_path	/home/airflow/gcs/dags/bq_copy_eu_to_us_sample.csv	CSV file listing source and target tables, including dataset
gcs_source_bucket	-us	Cloud Storage bucket to use for exporting BigQuery tables from source
gcs_dest_bucket	-eu	Cloud Storage bucket to use for importing BigQuery tables at destination

The next gcloud composer command executes the Airflow CLI sub-command variables. The sub-command passes the arguments to the gcloud command line tool.

To set the three variables, you will run the gcloud composer command once for each row from the above table. The form of the command is this:

gcloud composer environments run ENVIRONMENT_NAME \ --location LOCATION variables -- \ set KEY VALUE You can safely ignore this gcloud error: (ERROR: gcloud crashed (TypeError): 'NoneType' object is not callable). This is a known issue with using gcloud composer environments run with the 410.0.0 version of gcloud. Your variables will still be set accordingly despite the error message.

• ENVIRONMENT_NAME is the name of the environment.
• LOCATION is the Compute Engine region where the environment is located. The gcloud composer command requires including the --location flag or setting the default location before running the gcloud command.
• KEY and VALUE specify the variable and its value to set. Include a space two dashes space ( -- ) between the left-side gcloud command with gcloud-related arguments and the right-side Airflow sub-command-related arguments. Also include a space between the KEY and VALUE arguments.

1. Run these commands in Cloud Shell to set the Airflow variables:

gcloud composer environments run airflow-advanced-lab \ --location {{{ project_0.default_region | "REGION" }}} variables -- \ set table_list_file_path /home/airflow/gcs/dags/bq_copy_eu_to_us_sample.csv gcloud composer environments run airflow-advanced-lab \ --location {{{ project_0.default_region | "REGION" }}} variables -- \ set gcs_source_bucket {{{project_0.project_id | "Project ID"}}}-us gcloud composer environments run airflow-advanced-lab \ --location {{{ project_0.default_region | "REGION" }}} variables -- \ set gcs_dest_bucket {{{project_0.project_id | "Project ID"}}}-eu Note: If you receive an error stating that the subcommand cannot execute while the environment is in the CREATING state, wait until the airflow-advanced-lab environment has finished creating (indicated by a green checkmark and the RUNNING state) before running these commands.

To see the value of a variable, run the Airflow CLI sub-command variables with the get argument or use the Airflow UI.

For example, run the following:

gcloud composer environments run airflow-advanced-lab \ --location {{{ project_0.default_region | "REGION" }}} variables -- \ get gcs_source_bucket Note: Make sure to set all three Airflow variables used by the DAG.

Task 9. Upload the DAG and dependencies to Cloud Storage

1. Copy the Google Cloud Python docs samples files into Cloud Shell:

cd ~ gcloud storage cp -r gs://spls/gsp283/python-docs-samples .

2. Upload a copy of the third-party hook and operator to the plugins folder of your Airflow DAGs Cloud Storage bucket:

gcloud storage cp -r python-docs-samples/third_party/apache-airflow/plugins/* gs://$DAGS_BUCKET/plugins

3. Next, upload the DAG and config file to the DAGs Cloud Storage bucket of your environment:

gcloud storage cp python-docs-samples/composer/workflows/bq_copy_across_locations.py gs://$DAGS_BUCKET/dags gcloud storage cp python-docs-samples/composer/workflows/bq_copy_eu_to_us_sample.csv gs://$DAGS_BUCKET/dags

Managed Apache Airflow services automatically detect and register DAGs in the Airflow environment, with updates typically reflected within a few minutes. You can monitor task status through the Airflow web interface and verify that the DAG scheduling behavior matches its defined configuration.

Task 10. Explore the Airflow UI

To access the Airflow web interface using the Google Cloud console:

1. Return to the Environments page in Managed Airflow.
2. In the Airflow webserver column for the environment, click the Airflow link.

3. Click on your lab credentials.
4. The Airflow web UI opens in a new browser window. Data will still be loading when you get here. You can continue with the lab while this is happening.

View variables

The variables you set earlier are persisted in your environment.

• View the variables by selecting Admin > Variables from the Airflow menu bar.

Trigger the DAG to run manually

1. Click on the DAGs tab and wait for the links to finish loading.

2. To trigger the DAG manually, click the play button for composer_sample_bq_copy_across_locations:

3. Click Trigger DAG to confirm this action.

Click Check my progress to verify the objective.

Uploading the DAG and dependencies to Cloud Storage

Explore DAG runs

When you upload your DAG file to the DAGs folder (configured storage location), Apache Airflow parses the file. If no errors are found, the workflow appears in the DAG list, and it is queued to run based on its scheduling configuration. If the schedule is set to None, the workflow will not run automatically and must be triggered manually.

The Runs status turns green (indicating active or completed runs) once the play button is pressed:

1. Click the name of the DAG to open the DAG details page. This page includes a graphical representation of workflow tasks and dependencies.

2. Now, in the toolbar, click Graph, then mouseover the graphic for each task to see its status. Note that the border around each task also indicates the status (green border = running; red = failed, etc.).

To run the workflow again from the Graph view:

1. In the Airflow UI Graph View, click the start graphic.
2. Click Clear > Clear existing task to reset all the tasks and then click Clear DAG run to confirm.

Refresh your browser while the process is running to see the most recent information.

Task 11. Validate the results

Now check the status and results of the workflow by going to these Google Cloud console pages:

• The exported tables were copied from the US bucket to the EU Cloud Storage bucket. Click on Cloud Storage to see the intermediate Avro files in the source (US) and destination (EU) buckets.
• The list of tables was imported into the target BigQuery dataset. Click on BigQuery, then click on your project name and the nyc_tlc_EU dataset to validate that the tables are accessible from the dataset you created.

Delete the Managed Airflow environment

1. Return to the Environments page in Managed Airflow.

2. Select your Managed Airflow environment.

3. Click Delete.

4. In the dialog that opens, click Delete to confirm you want to continue deleting the Managed Airflow environment.

Congratulations!

You copied tables programmatically from US to EU! This lab is based on this blog post by David Sabater Dinter.

Next steps

• Learn more about using Airflow at the Airflow website or the Airflow GitHub project.
• There are lots of other resources available for Airflow, including a discussion group.
• Sign up for an Apache JIRA account and re-open any issues that you care about in the Apache Airflow JIRA project.
• For information about the Airflow UI, see Accessing the web interface.

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Manual Last Updated September 10, 2026

Lab Last Tested September 10, 2026

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