# Dataflow: Qwik Start - Python - GSP207

## **Overview**

The Apache Beam SDK is an open source programming model for data pipelines. In Google Cloud, you can define a pipeline with an Apache Beam program and then use Dataflow to run your pipeline.

In this lab, you set up your Python development environment for Dataflow (using the Apache Beam SDK for Python) and run an example Dataflow pipeline.

### What you'll do

In this lab, you learn how to:

*   Create a Cloud Storage bucket to store results of a Dataflow pipeline
    
*   Install the Apache Beam SDK for Python
    
*   Run a Dataflow pipeline remotely
    

## **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).
    

**Note:** Use an Incognito or private browser window to run this lab. This prevents any 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:** If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your 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 pop-up opens for you to select your payment method. On the left is the **Lab Details** panel with the following:
    
    *   The **Open Google Cloud console** button
        
    *   Time remaining
        
    *   The temporary credentials that you must use for this lab
        
    *   Other information, if needed, to step through this lab
        
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.
    
    ```apache
    student-04-cbc404c8e0af@qwiklabs.net
    ```
    
    You can also find the **Username** in the **Lab Details** panel.
    
4.  Click **Next**.
    
5.  Copy the **Password** below and paste it into the **Welcome** dialog.
    
    ```apache
    wSNwVO5LWBWa
    ```
    
    You can also find the **Password** in the **Lab Details** 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 view a menu with a list of Google Cloud products and services, click the **Navigation menu** at the top-left.

![Navigation menu icon](https://cdn.qwiklabs.com/nUxFb6oRFr435O3t6V7WYJAjeDFcrFb16G9wHWp5BzU%3D align="left")

### 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**
    
    ![Activate Cloud Shell icon](https://cdn.qwiklabs.com/ep8HmqYGdD%2FkUncAAYpV47OYoHwC8%2Bg0WK%2F8sidHquE%3D align="left")
    
    at the top of the Google Cloud console.
    

When you are connected, you are already authenticated, and the project is set to your **Project\_ID**, `qwiklabs-gcp-04-b22b47259281`. The output contains a line that declares the **Project\_ID** for this session:

```apache
Your Cloud Platform project in this session is set to qwiklabs-gcp-04-b22b47259281
```

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

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

```apache
gcloud auth list
```

3.  Click **Authorize**.
    

**Output:**

```apache
ACTIVE: *
ACCOUNT: student-04-cbc404c8e0af@qwiklabs.net

To set the active account, run:
    $ gcloud config set account `ACCOUNT`
```

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

```apache
gcloud config list project
```

**Output:**

```apache
[core]
project = qwiklabs-gcp-04-b22b47259281
```

**Note:** For full documentation of `gcloud`, in Google Cloud, refer to [the gcloud CLI overview guide](https://cloud.google.com/sdk/gcloud).

### Set the region

*   In Cloud Shell, run the following command to set the project region for this lab:
    

```apache
gcloud config set compute/region us-east1
```

### Ensure that the Dataflow API is successfully enabled

To ensure access to the necessary API, restart the connection to the Dataflow API.

1.  In the Cloud Console, enter "Dataflow API" in the top search bar. Click on the result for **Dataflow API**.
    
2.  Click **Manage**.
    
3.  Click **Disable API**.
    

If asked to confirm, click **Disable**.

4.  Click **Enable**.
    

When the API has been enabled again, the page will show the option to disable.

## **Task 1. Create a Cloud Storage bucket**

When you run a pipeline using Dataflow, your results are stored in a Cloud Storage bucket. In this task, you create a Cloud Storage bucket for the results of the pipeline that you run in a later task.

1.  On the **Navigation menu** (
    
    ![Navigation menu icon](https://cdn.qwiklabs.com/tkgw1TDgj4Q%2BYKQUW4jUFd0O5OEKlUMBRYbhlCrF0WY%3D align="left")
    
    ), click **Cloud Storage** > **Buckets**.
    
2.  Click **Create bucket**.
    
3.  In the **Create bucket** dialog, specify the following attributes:
    

*   ***Name***: To ensure a unique bucket name, use the following name: `qwiklabs-gcp-04-b22b47259281`\-bucket. Note that this name does not include sensitive information in the bucket name, as the bucket namespace is global and publicly visible.
    
*   ***Location type***: Multi-region
    
*   ***Location***: `us`
    
*   A location where bucket data will be stored.
    

4.  Click **Create**.
    
5.  If Prompted Public access will be prevented, click **Confirm**.
    

**Test completed task**

Click **Check my progress** to verify your performed task. If you have completed the task successfully you will be granted an assessment score.

Create a Cloud Storage bucket.

Check my progress

## **Task 2. Install the Apache Beam SDK for Python**

1.  To ensure that you use a supported Python version, begin by running the `Python3.9` Docker Image:
    

```apache
docker run -it -e DEVSHELL_PROJECT_ID=$DEVSHELL_PROJECT_ID python:3.9 /bin/bash
```

This command pulls a Docker container with the latest stable version of Python 3.9 and then opens up a command shell for you to run the following commands inside your container.

2.  After the container is running, install the latest version of the Apache Beam SDK for Python by running the following command from a virtual environment:
    

```apache
pip install 'apache-beam[gcp]'==2.42.0
```

You will see some warnings returned that are related to dependencies. It is safe to ignore them for this lab.

3.  Run the `wordcount.py` example locally by running the following command:
    

```apache
python -m apache_beam.examples.wordcount --output OUTPUT_FILE
```

You may see a message similar to the following:

```apache
INFO:root:Missing pipeline option (runner). Executing pipeline using the default runner: DirectRunner.
INFO:oauth2client.client:Attempting refresh to obtain initial access_token
```

This message can be ignored.

4.  You can now list the files that are on your local cloud environment to get the name of the `OUTPUT_FILE`:
    

```apache
ls
```

5.  Copy the name of the `OUTPUT_FILE` and `cat` into it:
    

```apache
cat <file name>
```

Your results show each word in the file and how many times it appears.

## **Task 3. Run an example Dataflow pipeline remotely**

1.  Set the BUCKET environment variable to the bucket you created earlier:
    

```apache
BUCKET=gs://<bucket name provided earlier>
```

2.  Now you'll run the `wordcount.py` example remotely:
    

```apache
python -m apache_beam.examples.wordcount --project $DEVSHELL_PROJECT_ID \
  --runner DataflowRunner \
  --staging_location $BUCKET/staging \
  --temp_location $BUCKET/temp \
  --output $BUCKET/results/output \
  --region us-east1
```

In your output, wait until you see the message:

```apache
JOB_MESSAGE_DETAILED: Workers have started successfully.
```

Then continue with the lab.

## **Task 4. Check that your Dataflow job succeeded**

1.  Open the Navigation menu and click **Dataflow** from the list of services.
    

You should see your **wordcount** job with a **status** of **Running** at first.

2.  Click on the name to watch the process. When all the boxes are checked off, you can continue watching the logs in Cloud Shell.
    

The process is complete when the status is **Succeeded**.

**Test completed task**

Click **Check my progress** to verify your performed task. If you have completed the task successfully you will be granted with an assessment score.

Run an Example Pipeline Remotely.

Check my progress

3.  Click **Navigation menu** > **Cloud Storage** in the Cloud Console.
    
4.  Click on the name of your bucket. In your bucket, you should see the **results** and **staging** directories.
    
5.  Click on the **results** folder and you should see the output files that your job created:
    
6.  Click on a file to see the word counts it contains.
    

## **Task 5. Test your understanding**

Below is a multiple choice question to reinforce your understanding of this lab's concepts. Answer it to the best of your abilities.

**Dataflow temp\_location must be a valid Cloud Storage URL.**

*   <mark class="bg-yellow-200 dark:bg-yellow-500/30">True</mark>
    
*   False
    

* * *

## Solution of Lab

### New solution

```apache
curl -LO raw.githubusercontent.com/ePlus-DEV/storage/refs/heads/main/labs/GSP207/lab.sh
source lab.sh
```

![](https://cdn.hashnode.com/uploads/covers/5f802df9bbabf10ec84d9fe8/dee5b925-b9c8-4ec0-9263-42a636dab571.png align="center")

* * *

### Old solution

<iframe type="youtube" src="https://youtu.be/Xa8OrPc6IQ8" data-node-type="hn-embed"></iframe>

```apache
export REGION=
```

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1730975385772/facf8865-d688-4826-85cb-c4242099297d.png align="center")

```apache
curl -LO raw.githubusercontent.com/QUICK-GCP-LAB/2-Minutes-Labs-Solutions/main/Dataflow%20Qwik%20Start%20-%20Python/gsp207.sh
sudo chmod +x gsp207.sh
./gsp207.sh
```