This article was originally published on Medium (Google Cloud Community).

1. Explanation of the use case presented in this article

In a previous article, I demonstrated how to build an ELT pipeline using dbt on Google Cloud. In this article, we'll explore the same use case — this time using Dataform, Google Cloud's native solution for managing SQL-based data workflows.

One of the key advantages of Dataform on Google Cloud is that it's a fully managed, serverless service, natively integrated with BigQuery.

As with the dbt-focused article, I'll use a football-related, real-world ELT pipeline that includes both staging and mart layers. I'll walk through how to create a Dataform repository manually, link it with a GitHub repository, and then automate the entire setup using Terraform. This approach enables a GitOps workflow, where the GitHub repository becomes the single source of truth for managing your Dataform resources.

Here is the diagram of this use case:

I also created a video on this topic on my GCP YouTube channel. Feel free to subscribe to the channel to support my work for the Google Cloud community.

English version:

French version:

🔧 CI/CD layer

• The CI/CD pipeline is implemented using Cloud Build and Infrastructure as Code (IaC) with Terraform.
• It automates the creation of the Dataform repository and establishes a link with a GitHub repository, enabling a GitOps-driven workflow.

📦 Applicative ELT pipeline

• Data ingestion: raw files are loaded into BigQuery using bq load.
• Staging layer: reads raw data and applies initial data cleaning and standardization.
• Mart layer: transforms cleaned data into business-ready domain models, applying domain-specific logic.
• Dynamic views generation: creates views for player statistics, dynamically generated from the domain data.
• Dynamic tables generation: builds country-specific tables to expose player statistics by country.

2. Usual structure of a Dataform project

Dataform is fully managed and deeply integrated with BigQuery, making it seamless to use within the Google Cloud ecosystem. When getting started, the Dataform console makes it easy to generate boilerplate code and a project structure, helping you ramp up quickly.

On the Dataform main page, the first step is to click the "Create repository" button to start a new project.

In the repository creation menu, you can assign any name, select the europe-west1 region, and specify a service account with the necessary permissions — typically Dataform Editor and BigQuery Data Editor to get started. For production setups, use more restricted roles aligned with the principle of least privilege.

Then click on "Go to repositories":

Click on "Create development workspace":

Then open the created workspace:

Click on "Initialize workspace":

After this step, Dataform generates the example project structure. Next, open the workflow_settings.yaml file and click on "Install Packages" to set up the required dependencies.

The boilerplate project provides code that's ready to run out of the box. It includes:

• a workflow_settings.yaml configuration file
• a definitions/ folder containing sample models
• an includes/ folder for reusable JavaScript functions

You can execute the pipeline directly by clicking "Start execution" → "Execute actions" in the UI.

In the "Selection of actions" panel, choose all actions for this example, then click on "Start execution" to run the pipeline.

Click on "Details":

The execution is successful:

By clicking on "View details", you can access the compiled SQL queries that Dataform generates from your models.

You can also view the compiled dependency graph, which visualizes the relationships between models and their execution order.

In the next section, we'll walk through our use case and explore a more realistic, real-world scenario.

3. Structure of the project presented in this use case

3.1 Dataform configuration

The workflow_settings.yaml file, located at the root of the GitHub repo and of the Dataform project, defines key configuration parameters:

• the GCP project ID
• the location (region) where the Dataform workflow runs
• the BigQuery dataset used to store the Dataform-generated tables
• the BigQuery dataset used to store assertion results
• the Dataform core version used for execution

defaultProject: gb-poc-373711
defaultLocation: europe-west1
defaultDataset: qatar_fifa_world_cup_dataform
defaultAssertionDataset: qatar_fifa_world_cup_dataform_assertions
dataformCoreVersion: 3.0.42

3.2 Definitions and models

The definitions folder contains all the Dataform models, organized into staging and mart layers.

Dataform models are defined using either SQLX or JavaScript files.

3.3 Staging layer

In the staging layer, the SQLX file cleans the raw data and applies light transformations, typically producing BigQuery views as output.

The team_players_stat_raw_cleaned.sqlx file:

config {
    type: "view",
    columns: {
        goalsScored: "Goal scored for the player.",
        assistsProvided: "Assists provided by the player.",
        appearances: "Appearances for the player."
    }
}

SELECT
    nationality,
    SAFE_CAST(goalsScored AS INT64) AS goalsScored,
    SAFE_CAST(assistsProvided AS INT64) AS assistsProvided,
    SAFE_CAST(dribblesPerNinety AS FLOAT64) AS dribblesPerNinety,
    SAFE_CAST(appearances AS INT64) AS appearances,
    SAFE_CAST(totalDuelsWonPerNinety AS FLOAT64) AS totalDuelsWonPerNinety,
    SAFE_CAST(interceptionsPerNinety AS FLOAT64) AS interceptionsPerNinety,
    SAFE_CAST(tacklesPerNinety AS FLOAT64) AS tacklesPerNinety,
    brandSponsorAndUsed,
    club,
    savePercentage,
    IF(savePercentage <> "-", TRUE, FALSE) AS isGoalKeeperStatsExist,
    fifaRanking,
    position,
    playerName,
    cleanSheets,
    nationalTeamKitSponsor,
    nationalTeamJerseyNumber,
    playerDob
FROM `qatar_fifa_world_cup_dataform.team_players_stat_raw`

Similar to dbt, the config block in a Dataform model lets you define settings specific to that model. For example:

• type: how the model is materialized (e.g. a view).
• columns: documentation and descriptions for specific columns.

The rest of the model is a standard BigQuery SQL query that defines the transformation logic.

3.4 Mart layer

The team_players_stat.sqlx file:

config {
  type: "table",
  description: "Description of the table.",
  columns: team_player_stat_columns_descriptions.columns_descriptions,
  bigquery: {
    partitionBy: {
      field: "ingestionDate",
      dataType: "timestamp",
      granularity: "day"
    },
    clusterBy: ["teamName"]
  }
}

WITH

team_players_stat_raw AS (
    SELECT * FROM ${ref("team_players_stat_raw_cleaned")}
),

goalKeepersStats AS (
    SELECT
        nationality,
        STRUCT(
            playerName,
            appearances,
            savePercentage,
            cleanSheets
        ) AS goalKeeperStatsStruct
    FROM team_players_stat_raw
    WHERE isGoalKeeperStatsExist IS TRUE
),

goalKeeperStatsPerTeam AS (
    SELECT
        nationality,
        ARRAY_AGG(goalKeeperStatsStruct ORDER BY goalKeeperStatsStruct.savePercentage DESC LIMIT 1)[OFFSET(0)] AS stats
    FROM goalKeepersStats
    GROUP BY
        nationality
)

SELECT
    statRaw.nationality AS teamName,
    nationalTeamKitSponsor,
    fifaRanking,
    SUM(goalsScored) AS teamTotalGoals,
    CURRENT_TIMESTAMP() AS ingestionDate,
    goalKeeperStatsPerTeam.stats AS goalKeeper,
    ${team_players_stat_functions.build_player_stats(
      "goalsScored",
      "appearances",
      "brandSponsorAndUsed",
      "club",
      "position",
      "playerDob",
      "playerName"
    )}
    AS topScorers,
    ${team_players_stat_functions.build_player_stats(
      "assistsProvided",
      "appearances",
      "brandSponsorAndUsed",
      "club",
      "position",
      "playerDob",
      "playerName"
    )}
    AS bestPassers,
    ${team_players_stat_functions.build_player_stats(
      "dribblesPerNinety",
      "appearances",
      "brandSponsorAndUsed",
      "club",
      "position",
      "playerDob",
      "playerName"
    )}
    AS bestDribblers,
    ${team_players_stat_functions.build_player_stats(
      "appearances",
      "appearances",
      "brandSponsorAndUsed",
      "club",
      "position",
      "playerDob",
      "playerName"
    )}
    AS playersMostAppearances,
    ${team_players_stat_functions.build_player_stats(
      "totalDuelsWonPerNinety",
      "appearances",
      "brandSponsorAndUsed",
      "club",
      "position",
      "playerDob",
      "playerName"
    )}
    AS playersMostDuelsWon,
    ${team_players_stat_functions.build_player_stats(
      "interceptionsPerNinety",
      "appearances",
      "brandSponsorAndUsed",
      "club",
      "position",
      "playerDob",
      "playerName"
    )}
    AS playersMostInterception,
    ${team_players_stat_functions.build_player_stats(
      "tacklesPerNinety",
      "appearances",
      "brandSponsorAndUsed",
      "club",
      "position",
      "playerDob",
      "playerName"
    )}
    AS playersMostSuccessfulTackles
FROM team_players_stat_raw statRaw
JOIN goalKeeperStatsPerTeam ON statRaw.nationality = goalKeeperStatsPerTeam.nationality
GROUP BY
    statRaw.nationality,
    nationalTeamKitSponsor,
    fifaRanking,
    goalKeeperStatsPerTeam.stats

For the mart step, the config block declares a table as the model type:

config {
  type: "table",
  description: "Description of the table.",
  columns: team_player_stat_columns_descriptions.columns_descriptions,
  bigquery: {
    partitionBy: {
      field: "ingestionDate",
      dataType: "timestamp",
      granularity: "day"
    },
    clusterBy: ["teamName"]
  }
}

We also added clustering and partitioning options in the bigquery block to improve performance and reduce cost.

3.5 Mart step: column descriptions

Instead of embedding the column descriptions directly in the SQLX model, we extracted them into a separate JavaScript file. This is particularly useful when descriptions are long, as it keeps the model clean and readable.

You have two options for column descriptions: embed them in the SQLX model, or move them to a JS file. When descriptions are verbose, I recommend the JS file for clarity, maintainability and readability.

In the config block, the columns parameter directly references the JavaScript constant that holds the descriptions:

columns: team_player_stat_columns_descriptions.columns_descriptions,

This constant is declared in the team_player_stat_columns_descriptions.js file, in the includes folder. The nested objects (top_scorers_columns, best_passers_columns, …) are declared in the same file and describe the fields of each STRUCT:

const top_scorers_columns = {
    description: "An object containing the top scorers fields.",
    columns: {
        goals: "Total number of goals scored by the top scorers.",
        players: {
            description: "List of top-scoring players",
            columns: {
                playerName: "Name of the top-scoring player.",
                playerDob: "Date of birth of the player.",
                position: "Playing position of the player.",
                club: "Club that the player is affiliated with.",
                brandSponsorAndUsed: "Brand sponsor of the player's gear.",
                appearances: "Number of matches the player has played in."
            }
        }
    }
};

// ... same pattern for the other statistics

const columns_descriptions = {
    teamName: "Name of the national football team.",
    teamTotalGoals: "Total number of goals scored by the team in the tournament.",
    fifaRanking: "Current FIFA ranking of the national team.",
    nationalTeamKitSponsor: "Official sponsor providing kits for the national team.",
    topScorers: top_scorers_columns,
    bestPassers: best_passers_columns,
    bestDribblers: best_dribblers_columns,
    goalKeeper: goal_keeper_columns,
    playersMostAppearances: players_most_appearances_columns,
    playersMostDuelsWon: players_most_duels_won_columns,
    playersMostInterception: players_most_interceptions_columns,
    playersMostSuccessfulTackles: players_most_successful_tackles_columns
};

module.exports = {
    columns_descriptions
};

3.6 Mart step: reusable function to compute player statistics

Most player statistics — such as top scorers and best passers — are computed the same way. To avoid duplicating code, I created a reusable function that encapsulates this logic.

In Dataform, one way to define reusable logic is to create a JavaScript file in the includes folder. Here, the team_players_stat_functions.js file holds this function:

function build_player_stats(statIndicator, appearances, brandSponsorAndUsed, club, position, playerDob, playerName) {
    return `
    STRUCT(
      MAX(${statIndicator}) AS ${statIndicator},
      ARRAY_AGG(
        IF(
          ${statIndicator} = 0 OR ${statIndicator} = 0.00,
          NULL,
          STRUCT(
            ${appearances},
            ${brandSponsorAndUsed},
            ${club},
            ${position},
            ${playerDob},
            ${playerName}
          )
        )
        ORDER BY ${statIndicator} DESC LIMIT 1
      )[OFFSET(0)] AS players
    )
  `;
}

module.exports = {build_player_stats};

The build_player_stats function takes the column names as parameters and injects them into the generated SQL. To make it available in SQLX or JavaScript models, it must be exported with module.exports.

I gave more details on this use case and its data modeling in the article I previously wrote on dbt, which covers the same context.

3.7 Mart step: dynamic tables and views

One great feature of Dataform is the ability to write models in JavaScript instead of SQLX when needed. This is particularly useful when you need to generate logic or structure dynamically.

That's exactly what we demonstrate here: after computing the domain data, we dynamically generate one view per statistic and one table per country. JavaScript models are well suited for this kind of logic, letting us create models programmatically with more flexibility.

The stat_dynamic_tables_and_views.js file:

const statViews =
    [
        {
            columnToSelect: "goalKeeper",
            viewName: "goal_keeper"
        },
        {
            columnToSelect: "topScorers",
            viewName: "top_scorers"
        },
        {
            columnToSelect: "bestPassers",
            viewName: "best_passers"
        }
    ];

const statPerCountryTables = [
    {
        countryName: "France",
        tableName: "france_players"
    },
    {
        countryName: "Argentina",
        tableName: "argentina_players"
    }
]

statViews.forEach((view) => {
    publish(view.viewName + "_stat").query(
        ctx => `
            SELECT ${view.columnToSelect}
            FROM ${ctx.ref("team_players_stat")}
        `
    );
});

statPerCountryTables.forEach((table) => {
    publish(table.tableName + "_stat",
        {
            type: "table"
        })
        .query(
            ctx => `
                SELECT *
                FROM ${ctx.ref("team_players_stat")}
                WHERE teamName = "${table.countryName}"
            `
        );
});

We declare two const arrays — one for the views and one for the tables — and loop over each with forEach. The publish function then defines each view and table dynamically.

Below is the diagram of the ELT pipeline built with Dataform, with the data lineage showing how each component connects across the workflow.

4. Connect the GitHub repo to the Dataform repo from the console

In real-world projects, Dataform code is typically managed in GitHub repositories. Dataform can synchronize a repository over either HTTPS or SSH.

I prefer SSH, as it's both more secure and more convenient with GitHub. In this example, I had already added an SSH key to my GitHub account, which lets me reuse it across multiple repositories. You could also add a key at the repository level (deploy key), but managing it at the account level is more practical in my case.

To retrieve the GitHub SSH public host key in the format expected by a known_hosts file, run:

ssh-keyscan -t rsa github.com

It displays GitHub's public host key:

# github.com:22 SSH-2.0-4c545346
github.com ssh-rsa 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

Make sure to copy the entire value, starting from ssh-rsa (or ssh-ed25519) all the way to the end of the line, without the github.com hostname.

Next, you need the private key of your SSH key pair, which Dataform uses to authenticate with your GitHub repository.

Use one of the following commands, depending on the type of key you generated:

cat ~/.ssh/id_rsa
# or
cat ~/.ssh/id_ed25519

⚠️ Be careful — never share this private key. It must be kept secure at all times.

Store the private key as a secret in Secret Manager. Dataform reads it from there to establish the SSH connection with your GitHub repository.

Open the Dataform repository you created earlier and go to the Settings tab. From there, click on "Connect with Git" to link your repository to a Git provider such as GitHub.

Then fill in the Git connection form:

• the SSH URL of the remote GitHub repository
• the default branch (e.g. main)
• the secret version containing your private SSH key (from Secret Manager)
• the SSH public host key, in known_hosts format (from ssh-keyscan)

To let Dataform read the private key from Secret Manager, grant the Secret Manager Secret Accessor role (roles/secretmanager.secretAccessor) to the Dataform service agent:

service-{PROJECT_NUMBER}@gcp-sa-dataform.iam.gserviceaccount.com

The connection is successful:

When you create a new Dataform workspace, it automatically pulls the files and project structure from the linked GitHub repository, using the configured default branch (typically main).

5. Connect the GitHub repo to the Dataform repo with Terraform

In this section, we automate the GitHub repository link with Terraform instead of configuring it manually. This aligns with GitOps best practices, where Git is the single source of truth for infrastructure and configuration.

The Terraform code structure:

The infra folder contains all the Terraform configuration files.

The backend.tf file defines the remote state backend, which uses Google Cloud Storage (GCS) to persist the Terraform state:

terraform {
  backend "gcs" {
  }
}

The versions.tf file pins the Terraform and Google Cloud provider versions:

terraform {
  required_version = ">= 1.9.8"

  required_providers {
    google      = "= 6.14.0"
    google-beta = "= 6.14.0"
  }
}

Input variables are declared in the variables.tf file to keep the configuration flexible:

variable "project_id" {
  type = string
}

variable "region" {
  description = "Location for load balancer and Cloud Run resources"
  default     = "europe-west1"
}

variable "dataform_repo_name" {
  description = "Dataform repo name."
  type        = string
}

variable "service_account_email" {
  description = "Service Account email for the creation of the Dataform repo."
  type        = string
}

The locals.tf file declares constants such as the SSH public host key and the Secret Manager secret version of the private SSH key. These locals centralize values used across the Terraform configuration.

locals {
  github_account_host_public_ssh_key_value      = "ssh-rsa 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"
  github_account_private_ssh_key_secret_version = "projects/975119474255/secrets/github_account_mazlum_tosun_private_key/versions/latest"
}

The main.tf file creates the Dataform repository and configures the connection to the GitHub repository over SSH:

resource "google_dataform_repository" "world_cup_elt_dataform_repo" {
  provider = google-beta

  project = var.project_id
  name    = var.dataform_repo_name
  region  = var.region

  service_account = var.service_account_email

  workspace_compilation_overrides {
    default_database = var.project_id
  }
  git_remote_settings {
    url            = "ssh://[email protected]/tosun-si/${var.dataform_repo_name}.git"
    default_branch = "main"
    ssh_authentication_config {
      user_private_key_secret_version = local.github_account_private_ssh_key_secret_version
      host_public_key                 = local.github_account_host_public_ssh_key_value
    }
  }
}

Conclusion

This article presented a real-world ELT pipeline built with Dataform, covering key concepts such as staging and mart layers, concrete data transformations, column documentation, and JavaScript functions for dynamic logic.

It also included a DevOps and IaC part, so you can reproduce a complete, hands-on setup that follows modern best practices.

Dataform is a powerful choice for teams working with BigQuery, thanks to its fully managed experience and tight integration with GCP. Its support for a GitOps workflow — where GitHub repositories are the single source of truth — makes it even more appealing for collaborative, production-grade data projects.

Personally, I find using a programming language like JavaScript for dynamic logic far more effective than Jinja templating. JavaScript may not be the default choice for data engineers, but it offers better readability and more power when implementing complex or repetitive logic across your models.

All the code presented in this article is available in this GitHub repository:

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