# ETL Processing on Google Cloud Using Dataflow and BigQuery (Python) - GSP290

## **Overview**

In Google Cloud, you can build data pipelines that execute Python code to ingest and transform data from publicly available datasets into BigQuery using these Google Cloud services:

* **Cloud Storage**
    
* **Dataflow**
    
* **BigQuery**
    

In this lab, you use these services to create your own data pipeline, including the design considerations and implementation details, to ensure that your prototype meets the requirements. Be sure to open the Python files and read the comments when instructed.

### What you'll do

In this lab, you learn how to:

* Build and run Dataflow pipelines (Python) for data ingestion
    
* Build and run Dataflow pipelines (Python) for data transformation and enrichment
    

## **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-c53c39caeba6@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
    bGDAuTj83DU3
    ```
    
    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-03-396804febdfd`. 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-03-396804febdfd
```

`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-c53c39caeba6@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-03-396804febdfd
```

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

## **Task 1. Ensure that the Dataflow API is successfully enabled**

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

**Important:** even if the API is already enabled, follow steps 1-4 below to disable, and then reenable the API, in order to restart the API successfully.

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.

### Test completed task

Click **Check my progress** to verify your performed task.

Disable and re-enable the Dataflow API

**Check my progress**

## **Task 2. Download the starter code**

1. Run the following command in the Cloud Shell to get Dataflow Python Examples from [Google Cloud's professional services GitHub](https://github.com/GoogleCloudPlatform/professional-services/blob/master/examples/dataflow-python-examples/README.md):
    

```apache
gsutil -m cp -R gs://spls/gsp290/dataflow-python-examples .
```

2. Now, in Cloud Shell, set a variable equal to your project id.
    

```apache
export PROJECT=qwiklabs-gcp-03-396804febdfd
```

```apache
gcloud config set project $PROJECT
```

## **Task 3. Create a Cloud Storage bucket**

* Use the make bucket command in the Cloud Shell to create a new regional bucket in the `us-central1` region within your project:
    

```apache
gsutil mb -c regional -l us-central1  gs://$PROJECT
```

### Test completed task

Click **Check my progress** to verify your performed task.

Create a Cloud Storage Bucket

**Check my progress**

## **Task 4. Copy files to your bucket**

* Use the `gsutil` command in the Cloud Shell to copy files into the Cloud Storage bucket you just created:
    

```apache
gsutil cp gs://spls/gsp290/data_files/usa_names.csv gs://$PROJECT/data_files/
gsutil cp gs://spls/gsp290/data_files/head_usa_names.csv gs://$PROJECT/data_files/
```

### Test completed task

Click **Check my progress** to verify your performed task.

Copy Files to Your Bucket

**Check my progress**

## **Task 5. Create a BigQuery dataset**

* In the Cloud Shell, create a dataset in BigQuery Dataset called `lake`. This is where all of your tables will be loaded in BigQuery:
    

```apache
bq mk lake
```

### Test completed task

Click **Check my progress** to verify your performed task.

Create the BigQuery Dataset (name: lake)

**Check my progress**

## **Task 6. Build a Dataflow pipeline**

In this section you will create an append-only Dataflow which will ingest data into the BigQuery table. You can use the built-in code editor which will allow you to view and edit the code in the Google Cloud console.

![Append-only Dataflow pipeline diagram](https://cdn.qwiklabs.com/xu2ZNOCZ0IgdlDNdPrmJN%2FDIpdFiPVPCSstHGOYHeDM%3D align="left")

### Open the Cloud Shell Code Editor

1. Navigate to the source code by clicking on the **Open Editor** icon in the Cloud Shell:
    

![Open Editor icon](https://cdn.qwiklabs.com/jBG2dXK1ZILP2ZKKw0EmMovNWjE5JecLn277gF6wy70%3D align="left")

2. If prompted, click on **Open in a New Window**. It will open the code editor in new window. The Cloud Shell Editor allows you to edit files in the Cloud Shell environment, from the **Editor** you can return to the Cloud Shell by Clicking on **Open Terminal**.
    

## **Task 7. Data ingestion with a Dataflow Pipeline**

You will now build a Dataflow pipeline with a TextIO source and a BigQueryIO destination to ingest data into BigQuery. More specifically, it will:

* Ingest the files from Cloud Storage.
    
* Filter out the header row in the files.
    
* Convert the lines read to dictionary objects.
    
* Output the rows to BigQuery.
    

## **Task 8. Review pipeline Python code**

In the **Code Editor** navigate to `dataflow-python-examples` > `dataflow_python_examples` and open the `data_ingestion.py` file. Read through the comments in the file, which explain what the code is doing. This code will populate the dataset **lake** with a table in BigQuery.

![Code Editor](https://cdn.qwiklabs.com/wRFYQrAqdsaCRDuCheahYj6OJeCti0a1%2BWAwuqYtZRE%3D align="left")

## **Task 9. Run the Apache Beam pipeline**

1. Return to your Cloud Shell session for this step. You will now do a bit of set up for the required python libraries.
    

The Dataflow job in this lab requires `Python3.8`. To ensure you're on the proper version, you will run the Dataflow processes in a Python 3.8 Docker container.

2. Run the following in Cloud Shell to start up a Python Container:
    

```apache
docker run -it -e PROJECT=$PROJECT -v $(pwd)/dataflow-python-examples:/dataflow python:3.8 /bin/bash
```

This command will pull a Docker container with the latest stable version of Python 3.8 and execute a command shell to run the next commands within the container. The `-v` flag provides the source code as a `volume` for the container so that we can edit in Cloud Shell editor and still access it within the running container.

3. Once the container finishes pulling, and starts executing in the Cloud Shell, run the following to install `apache-beam` in that running container:
    

```apache
pip install apache-beam[gcp]==2.24.0
```

4. Next, in the running container in the Cloud Shell, change directories into where you linked the source code:
    

```apache
cd dataflow/
```

### Run the ingestion Dataflow pipeline in the cloud

5. The following will spin up the workers required, and shut them down when complete:
    

```apache
python dataflow_python_examples/data_ingestion.py \
  --project=$PROJECT --region=us-central1 \
  --runner=DataflowRunner \
  --machine_type=e2-standard-2 \
  --staging_location=gs://$PROJECT/test \
  --temp_location gs://$PROJECT/test \
  --input gs://$PROJECT/data_files/head_usa_names.csv \
  --save_main_session
```

6. Return to the Cloud Console and open the **Navigation menu** > **Dataflow** to view the status of your job.
    

![Navigation menu > Dataflow](https://cdn.qwiklabs.com/Hui0DmWpdsANYxcEAlr82Eb8EEfiimitZ%2BZ4A2rVMLQ%3D align="left")

7. Click on the name of your job to watch it's progress. Once your **Job Status** is **Succeeded**, you can move to the next step. This Dataflow pipeline will take approximately five minutes to start, complete the work, and then shutdown.
    
8. Navigate to BigQuery (**Navigation menu** > **BigQuery**) see that your data has been populated.
    

![Navigation menu > BigQuery](https://cdn.qwiklabs.com/cs%2Bbw2zun8yQhfbJM8osgw%2Fzz0R4dIbQ1j1NoJr6NYU%3D align="left")

9. Click on your project name to see the **usa\_names** table under the `lake` dataset.
    

![usa_names table](https://cdn.qwiklabs.com/WLaavXMsSZkeqgq%2B3zHLhFzYjz3Spu5X0tCEkFpjrRo%3D align="left")

10. Click on the table then navigate to the **Preview** tab to see examples of the `usa_names` data.
    

**Note:** If you don't see the `usa_names` table, try refreshing the page or view the tables using the classic BigQuery UI.

### Test completed task

Click **Check my progress** to verify your performed task.

Build a Data Ingestion Dataflow Pipeline

**Check my progress**

## **Task 10. Data transformation**

You will now build a Dataflow pipeline with a TextIO source and a BigQueryIO destination to ingest data into BigQuery. More specifically, you will:

* Ingest the files from Cloud Storage.
    
* Convert the lines read to dictionary objects.
    
* Transform the data which contains the year to a format BigQuery understands as a date.
    
* Output the rows to BigQuery.
    

### Review transformation pipeline python code

In the Code Editor, open `data_transformation.py` file. Read through the comments in the file which explain what the code is doing.

## **Task 11. Run the Dataflow transformation pipeline**

You will run the Dataflow pipeline in the cloud. This will spin up the workers required, and shut them down when complete.

1. Run the following commands to do so:
    

```apache
python dataflow_python_examples/data_transformation.py \
  --project=$PROJECT \
  --region=us-central1 \
  --runner=DataflowRunner \
  --machine_type=e2-standard-2 \
  --staging_location=gs://$PROJECT/test \
  --temp_location gs://$PROJECT/test \
  --input gs://$PROJECT/data_files/head_usa_names.csv \
  --save_main_session
```

2. Navigate to **Navigation menu** > **Dataflow** and click on the name of this job to view the status of your job. This Dataflow pipeline will take approximately five minutes to start, complete the work, and then shutdown.
    
3. When your **Job Status** is **Succeeded** in the Dataflow Job Status screen, navigate to **BigQuery** to check to see that your data has been populated.
    
4. You should see the **usa\_names\_transformed** table under the `lake` dataset.
    
5. Click on the table and navigate to the **Preview** tab to see examples of the `usa_names_transformed` data.
    

**Note:** If you don't see the `usa_names_transformed` table, try refreshing the page or view the tables using the classic BigQuery UI.

### Test completed task

Click **Check my progress** to verify your performed task.

Build a Data Transformation Dataflow Pipeline

**Check my progress**

## **Task 12. Data enrichment**

You will now build a Dataflow pipeline with a TextIO source and a BigQueryIO destination to ingest data into BigQuery. More specifically, you will:

* Ingest the files from Cloud Storage.
    
* Filter out the header row in the files.
    
* Convert the lines read to dictionary objects.
    
* Output the rows to BigQuery.
    

## **Task 13. Review data enrichment pipeline ython code**

1. In the Code Editor, open `data_enrichment.py` file.
    
2. Check out the comments which explain what the code is doing. This code will populate the data in BigQuery.
    

Line 83 currently looks like:

```apache
values = [x.decode('utf8') for x in csv_row]
```

3. Edit it to look like the following:
    

```apache
values = [x for x in csv_row]
```

4. When you have finished editing this line, remember to **Save** this updated file by selecting the **File** pull down in the Editor and clicking on **Save**
    

## **Task 14. Run the Data Enrichment Dataflow pipeline**

Here you'll run the Dataflow pipeline in the cloud.

1. Run the following in the Cloud Shell to spin up the workers required, and shut them down when complete:
    

```apache
python dataflow_python_examples/data_enrichment.py \
  --project=$PROJECT \
  --region=us-central1 \
  --runner=DataflowRunner \
  --machine_type=e2-standard-2 \
  --staging_location=gs://$PROJECT/test \
  --temp_location gs://$PROJECT/test \
  --input gs://$PROJECT/data_files/head_usa_names.csv \
  --save_main_session
```

2. Navigate to **Navigation menu** > **Dataflow** to view the status of your job. This Dataflow pipeline will take approximately five minutes to start, complete the work, and then shutdown.
    
3. Once your **Job Status** is **Succeed** in the Dataflow Job Status screen, navigate to **BigQuery** to check to see that your data has been populated.
    

You should see the **usa\_names\_enriched** table under the `lake` dataset.

4. Click on the table and navigate to the **Preview** tab to see examples of the `usa_names_enriched` data.
    

**Note:** If you don't see the `usa_names_enriched` table, try refreshing the page or view the tables using the classic BigQuery UI.

### Test the completed data enrichment task

Click **Check my progress** to verify your performed task.

Build a Data Enrichment Dataflow Pipeline

**Check my progress**

## **Task 15. Data lake to Mart and Review pipeline python code**

Now build a Dataflow pipeline that reads data from two BigQuery data sources, and then joins the data sources. Specifically, you:

* Ingest files from two BigQuery sources.
    
* Join the two data sources.
    
* Filter out the header row in the files.
    
* Convert the lines read to dictionary objects.
    
* Output the rows to BigQuery.
    

In the **Code Editor**, open `data_lake_to_mart.py` file. Read through the comments in the file which explain what the code is doing. This code will join two tables and populate the resulting data in BigQuery.

## **Task 16. Run the Apache Beam Pipeline to perform the Data Join and create the resulting table in BigQuery**

Now run the Dataflow pipeline in the cloud.

1. Run the following code block, in the Cloud Shell, to spin up the workers required, and shut them down when complete:
    

```apache
python dataflow_python_examples/data_lake_to_mart.py \
  --worker_disk_type="compute.googleapis.com/projects//zones//diskTypes/pd-ssd" \
  --max_num_workers=4 \
  --project=$PROJECT \
  --runner=DataflowRunner \
  --machine_type=e2-standard-2 \
  --staging_location=gs://$PROJECT/test \
  --temp_location gs://$PROJECT/test \
  --save_main_session \
  --region=us-central1
```

2. Navigate to **Navigation menu** > **Dataflow** and click on the name of this new job to view the status. This Dataflow pipeline will take approximately five minutes to start, complete the work, and then shutdown.
    
3. Once your **Job Status** is **Succeeded** in the Dataflow Job Status screen, navigate to **BigQuery** to check to see that your data has been populated.
    

You should see the **orders\_denormalized\_sideinput** table under the `lake` dataset.

4. Click on the table and navigate to the **Preview** section to see examples of `orders_denormalized_sideinput` data.
    

**Note:** If you don't see the `orders_denormalized_sideinput` table, try refreshing the page or view the tables using the classic BigQuery UI.

### Test completed JOIN task

Click **Check my progress** to verify your performed task.

Build a Data lake to Mart Dataflow Pipeline

**Check my progress**

## **Test your understanding**

Below are multiple choice questions to reinforce your understanding of this lab's concepts. Answer them to the best of your abilities.

**ETL stands for \_\_\_\_.**

* Extract, Transform and Load
    
* Electronic Transferable Ledger
    

---

## Solution of Lab

%[https://www.youtube.com/watch?v=8MmNDBGa-3A] 

```apache
export REGION=
```

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1724224016074/a8b24927-705a-4d49-91d6-2b7da6cc78d8.png align="center")

```apache
curl -LO raw.githubusercontent.com/quiccklabs/Labs_solutions/master/ETL%20Processing%20on%20Google%20Cloud%20Using%20Dataflow%20and%20BigQuery%20Python/quicklabgsp290.sh
sudo chmod +x quicklabgsp290.sh
./quicklabgsp290.sh
```