📈Data Model Sync

As your organization's shared data model is constantly evolving, Euno helps ensure that these changes are reflected in your BI tool. The data model sync automation is designed to ensure that your BI application reflects the most up-to-date shared data model of your organization.

How the Data Model Sync Works

Euno supports the automated synchronization of data model revisions, facilitating seamless updates from the dbt manifest to LookML files. Here’s how it works:

  1. New revision: When a change is detected in the dbt manifest, Euno generates LookML files based on the updated manifest/catalog. Euno can either sync all models or be configured for a selective sync, in which models are synced based on a tag or or an input list. The sync process is triggered by changes in the dbt manifest. If no changes are detected, no sync is triggered.

  2. Branch checkout: Euno checks out the selected LookML repository and branch configured in the data model sync configuration

  3. PR/Commit: Euno generates a PR/commit, depending on the configurations

What Fields are Supported in the Data Model Sync?

Columns

Euno supports syncing all dbt columns in the model to dimensions in LookML. The dimension data type will be dependent on the dialect used in your DW (see Dialect Conversion Charts). For time dimensions, Euno automatically syncs these columns as a dimension_group of type; time, in LookML.

dimension_group: dimension_group_name {
  type: time
  sql: SQL expression ;;  
  timeframes: [raw, time, hour_of_day, date, day_of_the_week, week,  
              month, quarter, year]
  datatype: # field type conversion - dependent on dialect
  convert_tz: yes
}

Metrics

Euno supports automatically syncing dbt metrics to Looker, if the metric is built from a single semantic model. Meaning; the measures or metrics used to create the metric is from the same semantic model, and are of type:

  • Simple

  • Ratio

  • Derived

If the metric includes a filter, Euno will sync an additional dimension specifically for the filter that will hold the filter's logic within the 'sql' parameter of that dimension. The dimension will be of type 'yesno', with the parameter 'hidden' set to 'yes', tagged with 'euno-generated-filter-dimension', and named '<measure_name>_euno_generated_filter_dim' , which can be overridden by placing the macro key; under the meta section of the metric. Inside the measure, under the 'filter' parameter, it will reference this field with the dimension as the key and 'yes' as the value. Essentially, this means that if the data matches the logic in the filter, the metric will be applied to the data.

Note: These filters will only be generated for simple metrics and will specifically apply to dbt filter templates utilizing entities, dimensions, or time dimensions within the identical semantic model. Metrics filtered by other metrics are not currently supported.

Below is an example of how the Data Model Sync syncs a metric with a filter (simple_metric) in LookML.

dbt Metric with Filter
dbt Metric Synced to LookML with Filter
semantic_models:
  - name: users
    model: ref('users')
    entities:
      - type: primary
        name: pe
        description: This is a delphi auto-generated primary entity (Change according to desired primary entity)
    defaults:
      agg_time_dimension: time_dim
    dimensions:
      - name: time_dim
        type: time
        type_params:
          time_granularity: day
  measures:
      - name: simple_measure
        label: simple_measure
        agg: sum
        expr: 1
metrics:
  - name: simple_metric
    label: simple_metric
    type: simple
    type_params:
      measure: simple_measure
    filter: |
      {{ TimeDimension('pe__time_dim', 'day') }} IS NULL

  dimension: simple_metric_euno_generated_filter_dim {
    type: yesno
    hidden: yes
    sql: DATE_TRUNC('day', ${TABLE}.time_dim) IS NULL ;;
    tags: ["euno-generated-filter-dimension"]
  }

  measure: simple_metric {
    type: sum
    sql: 1 ;;
    filters: [simple_metric_euno_generated_filter_dim: "TRUE"]
  }

    type: yesno
    sql: metric_time__day IS NOT NULL ;;
  }

Euno also supports syncing filters using our built-in macro 'getdelphi.io/lookml_filters'. As described below, the above macro key should be placed in the metric's config, with the value being a list containing the 'filter_field' as the key and the 'filter_value' as the value inside the list. If the metric includes both a filter, and the above macro, the macro will override the filter within the metric. 

getdelphi.io/lookml_filters: '[["filter_field", "filter_value"]]'

Limitations of the Data Model Sync

Euno does not support syncing the following dbt metrics:

  • Cross model metric, a metric that is built from metrics across multiple semantic models

  • Cumulative metrics

  • Conversion metrics

  • Metrics which include an offset window

Formatting Fields in the Data Model Sync

Users can utilize Euno’s built-in macros using field meta to sync specific formatting like labels and value_format to LookML. See Customizing Generated LookML Files.

Configuring the Data Model Sync

The Data Model Sync section explains how to set up, configure, and manage the synchronization of data models between dbt and Looker using Euno's automation framework. This guide covers running the sync locally, automating the sync process, and pushing the sync to Looker repositories.

Overview of the Data Model Sync Interface

The Data Model Sync interface is organized into two main tabs:

  1. Data Sync Jobs Tab

    • This tab is where you create, configure, edit, and manage Data Sync Jobs.

    • Jobs define how, when, and where data model synchronization occurs. Each job is displayed as a tile with key details, including target repositories, sync triggers, and configuration options.

    • Use this tab to manage all your jobs and initiate manual syncs.

  2. Data Sync Runs Tab

    • This tab tracks the execution history of Data Sync Jobs.

    • Each run corresponds to a job execution and displays detailed information about its lifecycle, including status (Pending, Running, Completed, Failed, or Canceled) and artifacts (e.g., LookML packages).

    • Use this tab to monitor the status of your syncs and download generated artifacts.

Both tabs work together to provide a complete view of your Data Model Sync operations: the Jobs tab focuses on setup and configuration, while the Runs tab provides visibility into execution and outcomes.

Setting Up the Data Model Sync

1. Creating a Data Sync Job

A Data Sync Job defines the configuration for synchronizing your data models to your BI tool.

Steps:

  1. Navigate to Automations > Data Model Sync and open the Data Sync Jobs Tab.

    • If no jobs exist, click the Create New Job button.

  2. Fill in the Configuration Details:

    • General Settings:

      • Job Name: Assign a name for easy identification.

      • SQL Dialect: Select the appropriate SQL dialect for your DW that Looker is connected to. For details on how we convert certain data types to Looker data types, click here:

        • Snowflake

        • Databricks

        • Trino

        • Presto

        • BigQuery (Standard SQL or Legacy SQL)

    • Sync Trigger: Choose whether the sync should be triggered automatically.

      • By default, the automatic trigger is not configured. To automate the sync, toggle on the automatic sync option.

      • If the automatic sync is enabled, select the Euno project that should trigger the sync. The connected job or dbt artifacts defined in the selected Euno project will trigger the sync whenever a change is detected in the dbt manifest.

    • Sync Target: The sync target is where Euno will write the LookML artifacts from the sync.

      • Select the target repository

        • If a sync target has been enabled, a target repository must be selected, or the configuration cannot be saved.

      • Select the target branch

        • If a sync target has been enabled, a target branch must be selected, or the configuration cannot be saved.

      • Select the target directory

        • The default behavior is for Euno to push to a separate directory named /dbt_sync. This can be configured to any directory you prefer.

        • This directory should be a separate directory used exclusively for Euno sync purposes. The data model sync works by replacing all files in the directory. Therefore, this directory should only be managed by Euno, as any manual changes will be overridden during the next sync.

      • Choose a merge strategy:

        • Create Pull Request (default)

        • Commit Directly to Branch

    • What To Sync: Select the sync contents.

      • Select dbt project: Select the dbt project which is being synced.

      • dbt Models:

        • Sync All Models: Includes all models from the selected dbt project.

        • Selective Sync: Specify the desired models by typing the model name and pressing Enter to add it.

        • Models with Specific Tags: Only models with specified tags will sync.

      • Metrics:

        • Include All Metrics: Sync all supported metrics associated with the selected models.

        • No Metrics: Exclude metrics from the sync.

  3. Save or Cancel the Job:

    • Click Save to apply your configuration. Confirm your action in the dialog box.

    • Use Cancel to discard changes.

Saved jobs appear as tiles under the Data Sync Jobs Tab. Each tile includes job details and options to edit, delete, or manually trigger the job.

2. Running the Data Model Sync Locally

To validate data model sync outputs prior to automating, you can run the sync manually without pushing changes to the Looker repository. To run the job locally, either create a new job and leave the Target Information option disabled, or edit an existing job (see image below), toggle off Target Information, and save the job. If no target information is configured, the job will run locally, and the artifacts can be downloaded to your machine.

3. Automating the Data Sync

Automated syncs ensure data models remain up to date without manual intervention.

Steps:

  1. Enable Automatic Sync During Job Configuration.

  2. Specify the Euno project that triggers the sync. The Euno project is connected to either a dbt Cloud job, a webhook, or dbt artifacts, and will trigger the sync to run automatically whenever a change is detected in the dbt manifest.

    • Please note: The sync is triggered once the latest dbt artifacts are published ().

  3. Save the job. The sync will automatically execute based on updates to the project.

4. Configuring Sync to Push to Looker

For environments integrated with Looker:

  1. Set the Target Repository, Branch, and Directory during job configuration.

  2. Choose a merge strategy:

    • Create Pull Request (default): Push changes via a PR for review.

    • Commit Directly: Automatically merge changes into the branch.

  3. Save the job. Changes sync directly to the specified Looker repository.

Managing Data Sync Jobs and Runs

Data Sync Jobs Tab

This tab displays all configured sync jobs as tiles, with the following details:

  • Header: Job name, status of last run, and an ellipsis menu for options:

    • Edit job: Opens the job configuration form.

    • View jobs runs: Opens the Data Sync Runs tab with the table filtered by the specific job.

    • Delete job: Confirm deletion via dialog box.

  • Body: Includes target project, automatic sync status, and target branch.

  • Footer: Buttons for manual sync or canceling a running sync, and the timestamp of the last time the job ran.

Data Sync Runs Tab

This tab displays the execution history of syncs in a table, with each row representing a different run. The table is sorted by timestamp and, by default, includes all runs from every job. You can filter the table by a specific job using the filter located in the top-right corner of the page.

Columns in the Table:

  • Timestamp: The timestamp of when the job was triggered.

  • Job: The name of the job.

  • Trigger: How the job was triggered—either automatic or manual.

  • Triggered By: What triggered the job (e.g. user or euno project)

  • Run Status:

  • Action Buttons

    • Rerun Job Button:

    • Cancel Job Button:

    • Download Run Artifacts:

  • Open Report:

Use the Runs Tab to monitor ongoing syncs, investigate failures, or retrieve generated artifacts.

Sync Data Model Now

To manually trigger a sync:

  1. Navigate to the Data Sync Jobs Tab and select a job tile.

  2. Click Run now to start the job.

    • Progress and results will appear in the Data Sync Runs Tab.

Customizing LookML Structure for Auto-code Generation

Euno uses Jinja2 templates to generate the LookML files from a given dbt manifest and catalog.

Out-of-the-box, every model that is selected by the tags (or all models, if no tags are specified), will automatically generate a single view file in the target directory. Users can customize the behavior by either specifying meta fields in the model or the column, or by overriding the default Euno Jinja2 macros.

Deploying LookML Changes Made in GitHub to Looker

Euno syncs to LookML any changes introduced to dbt, by utilizing the GitHub integration to commit changes directly to the LookML repository in GitHub. However, Looker does not automatically pull these changes from GitHub and deploy them to the production environment.

You have the option to deploy changes to the project's production environment in Looker either manually for every commit or by setting up automatic pushes from the master branch.

Note: This step is crucial to ensure a successful sync with Looker.

Manually

To manually deploy the changes from a branch to the Looker’s production environment, follow the following steps from within Looker:

  • Enter “development mode”

  • Go to “Develop”, and pick the Looker project

  • Go to “Git Actions”

  • Click “Pull from…” and pick “Pull from remote”

  • Once the operation is done, click “Deploy to Production”

Automatically

Users can take advantage of Looker’s built-in Advanced deploy mode capability so that changes would automatically be deployed using either a webhook, API endpoint or manually using the deployment manager.

For Looker configurations involving GitHub, we can recommend configuring a webhook so that commits into the master branch would be automatically applied in Looker.

For more detailed instructions see Google’s instructions.

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