Build ELT pipeline for marketing analytics data

This tutorial shows you how to set up an ELT workflow that extracts, loads, and transforms marketing analytics data in BigQuery.

A typical ELT workflow periodically extracts new customer data from your data source and loads it into BigQuery. The unstructured data is then processed into meaningful metrics. In this tutorial, you create an ELT workflow by setting up a marketing analytics data transfer by using the BigQuery Data Transfer Service. Then, you schedule Dataform to run periodic transformations on the data.

In this tutorial, you use Google Ads as your data source, but you can use any of the data sources supported by the BigQuery Data Transfer Service.

Before you begin

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
Create a project: To create a project, you need the Project Creator (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
Create a project: To create a project, you need the Project Creator (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Required roles

To get the permissions that you need to complete this tutorial, ask your administrator to grant you the following IAM roles on the project:

BigQuery Admin (roles/bigquery.admin)
Dataform Admin (roles/dataform.admin)

For more information about granting roles, see Manage access to projects, folders, and organizations.

You might also be able to get the required permissions through custom roles or other predefined roles.

Schedule recurring data transfers

To keep BigQuery up to date with the latest marketing data from your data source, set up recurring data transfers using the BigQuery Data Transfer Service to extract and load data on a schedule.

In this tutorial, you use Google Ads as the example data source. For a full list of data sources supported by the BigQuery Data Transfer Service, see Supported data sources.

Go to the Data transfers page in the Google Cloud console.

Go to Data transfers
Click Create transfer.
In the Source type section, for Source, choose Google Ads.
In the Data source details section:
1. For Customer ID, enter your Google Ads customer ID.
2. For Report type, select Standard. The standard report includes the standard set of reports and fields as detailed in Google Ads report transformation.
  - For Refresh window, enter 5.
In the Destination settings section, for Dataset, select the dataset that you created to store your data.
In the Transfer config name section, for Display name, enter Marketing tutorial.
In the Schedule options section:
- For Repeat frequency, select Days.
- For At, enter 08:00.
Click Save.

After you save the configuration, the BigQuery Data Transfer Service begins the data transfer. Based on the settings in the transfer configuration, the data transfer runs once every day at 8:00 AM UTC and extracts data from Google Ads from the past five days.

You can monitor ongoing transfer jobs to check the status of each data transfer.

Query table data

When your data is transferred to BigQuery, the data is written to ingestion-time partitioned tables. For more information, see Introduction to partitioned tables.

If you query your tables directly instead of using the auto-generated views, you must use the _PARTITIONTIME pseudocolumn in your query. For more information, see Querying partitioned tables.

The following sections show sample queries that you can use to examine your transferred data.

Campaign performance

The following sample query analyzes Google Ads campaign performance for the past 30 days.

Console

SELECT
  c.customer_id,
  c.campaign_name,
  c.campaign_status,
  SUM(cs.metrics_impressions) AS Impressions,
  SUM(cs.metrics_interactions) AS Interactions,
  (SUM(cs.metrics_cost_micros) / 1000000) AS Cost
FROM
  `DATASET.ads_Campaign_CUSTOMER_ID` c
LEFT JOIN
  `DATASET.ads_CampaignBasicStats_CUSTOMER_ID` cs
ON
  (c.campaign_id = cs.campaign_id
  AND cs._DATA_DATE BETWEEN
  DATE_ADD(CURRENT_DATE(), INTERVAL -31 DAY) AND DATE_ADD(CURRENT_DATE(), INTERVAL -1 DAY))
WHERE
  c._DATA_DATE = c._LATEST_DATE
GROUP BY
  1, 2, 3
ORDER BY
  Impressions DESC

bq

  bq query --use_legacy_sql=false '
  SELECT
    c.customer_id,
    c.campaign_name,
    c.campaign_status,
    SUM(cs.metrics_impressions) AS Impressions,
    SUM(cs.metrics_interactions) AS Interactions,
    (SUM(cs.metrics_cost_micros) / 1000000) AS Cost
  FROM
    `DATASET.ads_Campaign_CUSTOMER_ID` c
  LEFT JOIN
    `DATASET.ads_CampaignBasicStats_CUSTOMER_ID` cs
  ON
    (c.campaign_id = cs.campaign_id
    AND cs._DATA_DATE BETWEEN
    DATE_ADD(CURRENT_DATE(), INTERVAL -31 DAY) AND DATE_ADD(CURRENT_DATE(), INTERVAL -1 DAY))
  WHERE
    c._DATA_DATE = c._LATEST_DATE
  GROUP BY
    1, 2, 3
  ORDER BY
    Impressions DESC'

Replace the following:

DATASET: the name of the dataset that you created to store the transferred table
CUSTOMER_ID: your Google Ads Customer ID.

Count of keywords

The following sample query analyzes keywords by campaign, ad group, and keyword status. This query uses the KeywordMatchType function. Keyword match types help control which searches can trigger your ad. For more information about keyword matching options, see About keyword matching options.

Console

  SELECT
    c.campaign_status AS CampaignStatus,
    a.ad_group_status AS AdGroupStatus,
    k.ad_group_criterion_status AS KeywordStatus,
    k.ad_group_criterion_keyword_match_type AS KeywordMatchType,
    COUNT(*) AS count
  FROM
    `DATASET.ads_Keyword_CUSTOMER_ID` k
    JOIN
    `DATASET.ads_Campaign_CUSTOMER_ID` c
  ON
    (k.campaign_id = c.campaign_id AND k._DATA_DATE = c._DATA_DATE)
  JOIN
    `DATASET.ads_AdGroup_CUSTOMER_ID` a
  ON
    (k.ad_group_id = a.ad_group_id AND k._DATA_DATE = a._DATA_DATE)
  WHERE
    k._DATA_DATE = k._LATEST_DATE
  GROUP BY
    1, 2, 3, 4

bq

  bq query --use_legacy_sql=false '
  SELECT
    c.campaign_status AS CampaignStatus,
    a.ad_group_status AS AdGroupStatus,
    k.ad_group_criterion_status AS KeywordStatus,
    k.ad_group_criterion_keyword_match_type AS KeywordMatchType,
    COUNT(*) AS count
  FROM
    `DATASET.ads_Keyword_CUSTOMER_ID` k
  JOIN
    `DATASET.ads_Campaign_CUSTOMER_ID` c
  ON
    (k.campaign_id = c.campaign_id AND k._DATA_DATE = c._DATA_DATE)
  JOIN
    `DATASET.ads_AdGroup_CUSTOMER_ID` a
  ON
    (k.ad_group_id = a.ad_group_id AND k._DATA_DATE = a._DATA_DATE)
  WHERE
    k._DATA_DATE = k._LATEST_DATE
  GROUP BY
    1, 2, 3, 4'

Replace the following:

DATASET: the name of the dataset that you created to store the transferred table
CUSTOMER_ID: your Google Ads Customer ID.

Create a Dataform repository

After you create the data transfer configuration to transfer the latest data from Google Ads, set up Dataform to regularly transform your marketing analytics data. Dataform lets you schedule regular data transformations, and it lets you define these transformations with SQL while collaborating with other data analysts.

Create a Dataform repository to store the SQLX queries that make up your transformation code.

In the Google Cloud console, go to the Dataform page.

Go to Dataform
Click Create repository.
On the Create repository page, do the following:
1. In the Repository ID field, enter marketing-tutorial-repository.
2. In the Region list, select a region.
3. Click Create.

The marketing-tutorial-repository repository now appears in your Dataform repositories list.

For more information about Dataform repositories, see About Dataform repositories.

Create and initialize a Dataform development workspace

Create a Dataform development workspace so that you can work on the transformation code within your repository before you commit and push your changes to your repository.

In the Google Cloud console, go to the Dataform page.

Go to Dataform
Click marketing-tutorial-repository.
Click Create development workspace.
In the Create development workspace window, do the following:
1. In the Workspace ID field, enter marketing-tutorial-workspace.
2. Click Create.
The development workspace page appears.
Click Initialize workspace.

The marketing-tutorial-workspace development workspace now appears in your marketing-tutorial-repository repository under the Development Workspaces tab, along with two example files in the definitions directory called *first_view.sqlx and *second_view.sqlx.

For more information about Dataform development workspaces, see Overview of development workspaces.

Declare your Google Ads table as table source

Connect your newly-transferred Google Ads table to Dataform by declaring it as a data source by following these following steps:

Create a SQLX file for data source declaration

In Dataform, you declare a data source destination by creating a SQLX file in the definitions/ directory:

In the Google Cloud console, go to the Dataform page.

Go to the Dataform page
Select marketing-tutorial-repository.
Select marketing-tutorial-workspace.
In the Files pane, next to definitions/, click the More menu.
Click Create file.
In the Create new file pane, do the following:
1. In the Add a file path field, after definitions/, enter the name definitions/googleads-declaration.sqlx.
2. Click Create file.

Declare a data source

Edit the definitions/googleads-declaration.sqlx to declare a transferred Google Ads table as a data source. This example declares the ads_Campaign table as a data source:

In your development workspace, in the Files pane, click your SQLX file for data source declaration.

In the file, enter the following code snippet:

    config {
        type: "declaration",
        database: "PROJECT_ID",
        schema: "DATASET",
        name: "ads_Campaign_CUSTOMER_ID",
    }

Define your transformation

Define your data transformations by create a SQLX file in the definitions/ directory. In this tutorial, you create a daily transformation that aggregates metrics like clicks, impressions, costs, and conversions using a file named daily_performance.sqlx.

Create the transformation SQLX file

In the Files pane, next to definitions/, click the More menu, and then select Create file.
In the Add a file path field, enter definitions/daily_performance.sqlx.
Click Create file.

Define the transformation SQLX file

In the Files pane, expand the definitions/ directory.

Select daily_performance.sqlx, then enter the following query:

    config {
        type: "table",
        schema: "reporting",
        tags: ["daily", "google_ads"]
    }
    SELECT
        date,
        campaign_id,
        campaign_name,
    SUM(clicks) AS total_clicks
    FROM
        `ads_Campaign_CUSTOMER_ID`
    GROUP BY
        date,
        campaign_id,
        campaign_name
        ORDER BY
        date DESC

Commit and push your changes

After you have made your changes in your development workspace, you can commit and push these changes to your repository by following these steps:

In the marketing-tutorial-workspace workspace, click Commit 1 change.
In the New commit pane, enter a commit description in the Add a commit message field.
Click Commit all changes.
In the marketing-tutorial-workspace workspace, click Push to default branch.

After your changes are successfully pushed to your repository, the Workspace is up to date message appears.

Schedule your data transformation

After you have defined your data transformation file, schedule the data transformations.

Create a production release

A production release in Dataform ensures that your environment is consistently updated with the results of your data transformations. The following steps show you how to specify the main branch of the marketing-tutorial-repository repository to store your data transformations:

In the Google Cloud console, go to the Dataform page.

Go to the Dataform page
Select marketing-tutorial-repository.
Click the Releases & scheduling tab.
Click Create production release.
In the Create release configuration pane, configure the following settings:
1. In the Release ID field, enter transformations.
2. In the Git commitish field, leave the default value main .
3. In the Schedule frequency section, select On-demand.
Click Create.

Create a workflow configuration

Once you have created a production release, you can then create a workflow configuration that runs your data transformations on a specified schedule in your repository. The following steps show you how to schedule daily transformations from the transformations file:

In the Google Cloud console, go to the Dataform page.

Go to the Dataform page
Select marketing-tutorial-repository.
Click the Releases & scheduling tab.
In the Workflow configurations section, click Create.
In the Create workflow configuration pane, in the Configuration ID field, enter transformations.
In the Release configuration menu, select transformations.
Under Authentication, select Execute with user credentials

In the Schedule frequency section, do the following:

1. Select **Repeat**.
1. For **Repeats**, select `Daily`.
1. For **At time**, enter `10:00 AM`.
1. For **Timezone**, select `Coordinated Universal Time (UTC)`.

Click Selection of tags.
In the Select tags to execute field, select Daily.
Click Create.

The workflow configuration that you have created runs the entire latest compilation result created by the transformations release configuration.

Clean up

To avoid incurring charges to your Google Cloud account for the resources used on this page, follow these steps.

Delete the dataset created in BigQuery

To avoid incurring charges for BigQuery assets, delete the dataset called dataform.

In the Google Cloud console, go to the BigQuery page.

Go to BigQuery
In the Explorer panel, expand your project and select dataform.
Click the Actions menu, and then select Delete.
In the Delete dataset dialog, enter delete into the field, and then click Delete.

Delete the Dataform development workspace and configurations

Dataform development workspace creation incurs no costs, but to delete the development workspace you can follow these steps:

In the Google Cloud console, go to the Dataform page.

Go to Dataform
Click quickstart-repository.
Click the Release & scheduling tab.
Under the Release configurations section, click the More menu next to the production configuration, and then click Delete.
Under the Workflow configurations section, click the More menu next to the transformations configuration, and then click Delete.
In the Development workspaces tab, click the More menu by quickstart-workspace, and then select Delete.
To confirm, click Delete.

Delete the Dataform repository

Dataform repository creation incurs no costs, but to delete the repository you can follow these steps:

In the Google Cloud console, go to the Dataform page.

Go to Dataform
By quickstart-repository, click the More menu, and then select Delete.
In the Delete repository window, enter the name of the repository to confirm deletion.
To confirm, click Delete.