Connect to dbt Core

Note

This article covers dbt Core, a version of dbt for your local development machine that interacts with Databricks SQL warehouses and Databricks clusters within your Databricks workspaces. To use the hosted version of dbt (called dbt Cloud) instead, or to use Partner Connect to quickly create a SQL warehouse within your workspace and then connect it to dbt Cloud, see Connect to dbt Cloud.

dbt (data build tool) is a development environment that enables data analysts and data engineers to transform data by simply writing select statements. dbt handles turning these select statements into tables and views. dbt compiles your code into raw SQL and then runs that code on the specified database in Databricks. dbt supports collaborative coding patterns and best practices such as version control, documentation, modularity, and more. For more information, see What, exactly, is dbt? and Analytics Engineering for Everyone: Databricks in dbt Cloud on the dbt website.

dbt does not extract or load data. dbt focuses on the transformation step only, using a “transform after load” architecture. dbt assumes that you already have a copy of your data in your database.

This article focuses on using dbt Core. dbt Core enables you to write dbt code in the text editor or IDE of your choice on your local development machine and then run dbt from the command line. dbt Core includes the dbt Command Line Interface (CLI). The dbt CLI is free to use and open source.

A hosted version of dbt called dbt Cloud is also available. dbt Cloud comes equipped with turnkey support for scheduling jobs, CI/CD, serving documentation, monitoring and alerting, and an integrated development environment (IDE). For more information, see Connect to dbt Cloud. The dbt Cloud Developer plan provides one free developer seat; Team and Enterprise paid plans are also available. For more information, see dbt Pricing on the dbt website.

Because dbt Core and dbt Cloud can use hosted git repositories (for example, on GitHub, GitLab or BitBucket), you can use dbt Core to create a dbt project and then make it available to your dbt Cloud users. For more information, see Creating a dbt project and Using an existing project on the dbt website.

For a general overview of dbt, watch the following YouTube video (26 minutes).

Requirements

Before you install dbt Core, you must install the following on your local development machine:

  • Python 3.7 or higher

  • A utility for creating Python virtual environments (such as pipenv)

Step 1: Create and activate a Python virtual environment

In this step, you use pipenv to create a Python virtual environment. We recommend using a Python virtual environment as it isolates package versions and code dependencies to that specific environment, regardless of the package versions and code dependencies within other environments. This helps reduce unexpected package version mismatches and code dependency collisions.

  1. From your terminal, switch to an empty directory, creating that directory first if necessary. This procedure creates an empty directory named dbt_demo in the root of your user home directory.

    mkdir ~/dbt_demo
    cd ~/dbt_demo
    
    mkdir %USERPROFILE%\dbt_demo
    cd %USERPROFILE%\dbt_demo
    
  2. In this empty directory, create a file named Pipfile with the following content. This Pipfile instructs pipenv to use Python version 3.8.6. If you use a different version, replace 3.8.6 with your version number.

    [[source]]
    url = "https://pypi.org/simple"
    verify_ssl = true
    name = "pypi"
    
    [packages]
    dbt-databricks = "*"
    
    [requires]
    python_version = "3.8.6"
    

    Note

    The preceding line dbt-databricks = "*" instructs pipenv to use the latest version of the dbt-databricks package. In production scenarios, you should replace * with the specific version of the package that you want to use. See dbt-databricks Release history on the Python Package Index (PyPI) website.

  3. Create a Python virtual environment in this directory by running pipenv and specifying the Python version to use. This command specifies Python version 3.8.6. If you use a different version, replace 3.8.6 with your version number:

    pipenv --python 3.8.6
    
  4. Install the dbt Databricks adapter by running pipenv with the install option. This installs the packages in your Pipfile, which includes the dbt Databricks adapter package, dbt-databricks, from PyPI. The dbt Databricks adapter package automatically installs dbt Core and other dependencies.

    Important

    If your local development machine uses any of the following operating systems, you must complete additional steps first: CentOS, MacOS, Ubuntu, Debian, and Windows. See the “Does my operating system have prerequisites” section of Use pip to install dbt on the dbt Labs website.

    pipenv install
    
  5. Activate this virtual environment by running pipenv shell. To confirm the activation, the terminal displays (dbt_demo) before the prompt. The virtual environment begins using the specified version of Python and isolates all package versions and code dependencies within this new environment.

    pipenv shell
    

    Note

    To deactivate this virtual environment, run exit. (dbt_demo) disappears from before the prompt. If you run python --version or pip list with this virtual environment deactivated, you might see a different version of Python, a different list of available packages or package versions, or both.

  6. Confirm that your virtual environment is running the expected version of Python by running python with the --version option.

    python --version
    

    If an unexpected version of Python displays, make sure you have activated your virtual environment by running pipenv shell.

  7. Confirm that your virtual environment is running the expected versions of dbt and the dbt Databricks adapter by running dbt with the --version option.

    dbt --version
    

    If an unexpected version of dbt or the dbt Databricks adapter displays, make sure you have activated your virtual environment by running pipenv shell. If an unexpected version still displays, try installing dbt or the dbt Databricks adapter again after you activate your virtual environment.

Step 2: Create a dbt project and specify and test connection settings

In this step, you create a dbt project, which is a collection of related directories and files that are required to use dbt. You then configure your connection profiles, which contain connection settings to a Databricks cluster, a SQL warehouse, or both. To increase security, dbt projects and profiles are stored in separate locations by default.

Tip

You can connect to an existing cluster or SQL warehouse, or you can create a new one.

  • An existing cluster or SQL warehouse can be efficient for multiple dbt projects, for using dbt in a team, or for development use cases.

  • A new cluster or SQL warehouse allows you to run a single dbt project in isolation for production use cases, as well as leverage automatic termination to save costs when that dbt project is not running.

Use Databricks to create a new cluster or SQL warehouse, and then reference the newly-created or existing cluster or SQL warehouse from your dbt profile.

  1. With the virtual environment still activated, run the dbt init command with a name for your project. This procedure creates a project named my_dbt_demo.

    dbt init my_dbt_demo
    
  2. When you are prompted whether to use a databricks or spark database, enter the number that corresponds to databricks.

  3. When prompted for a host value:

    • For a cluster, enter the Server Hostname value from the Advanced Options, JDBC/ODBC tab for your Databricks cluster.

    • For a SQL warehouse, enter the Server Hostname value from the Connection Details tab for your SQL warehouse.

  4. When prompted for an http_path value:

  5. When prompted for a token, enter the value of your Databricks personal access token.

    Note

    As a security best practice, when authenticating with automated tools, systems, scripts, and apps, Databricks recommends you use access tokens belonging to service principals instead of workspace users. To create access tokens for service principals, see Manage access tokens for a service principal.

  6. When prompted for the desired Unity Catalog option value, enter the number that corresponds with use Unity Catalog or not use Unity Catalog.

  7. If you chose to use Unity Catalog, enter the desired value for catalog when prompted.

  8. Enter the desired values for schema and threads when prompted.

  9. dbt writes your entries to a profiles.yml file. The location of this file is listed in the output of the dbt init command. You can also list this location later by running the dbt debug --config-dir command. You can open this file now to examine and to verify its contents.

  10. Confirm that the connection details are correct by running the dbt debug command.

    Important

    Make sure that your cluster or SQL warehouse is running first.

    You should see output similar to the following:

    dbt debug
    
    ...
    Configuration:
      profiles.yml file [OK found and valid]
      dbt_project.yml file [OK found and valid]
    
    Required dependencies:
     - git [OK found]
    
    Connection:
      ...
      Connection test: OK connection ok
    

Step 3: Create and run models

In this step, you use your favorite text editor to create models, which are select statements that create either a new view (the default) or a new table in a database, based on existing data in that same database. This procedure creates a model based on the sample diamonds table from the Sample datasets, as described in the Create a table section of Tutorial: Query data with notebooks. This procedure assumes this table has already been created in your workspace’s default database.

  1. In the project’s models directory, create a file named diamonds_four_cs.sql with the following SQL statement. This statement selects only the carat, cut, color, and clarity details for each diamond from the diamonds table. The config block instructs dbt to create a table in the database based on this statement.

    {{ config(
      materialized='table',
      file_format='delta'
    ) }}
    
    select carat, cut, color, clarity
    from diamonds
    

    Tip

    For additional config options such as using the Delta file format and the merge incremental strategy, see Apache Spark configurations on the dbt website and the “Model Configuration” and “Incremental Models” sections of the Usage Notes in the dbt-labs/dbt-spark repository in GitHub.

  2. In the project’s models directory, create a second file named diamonds_list_colors.sql with the following SQL statement. This statement selects unique values from the colors column in the diamonds_four_cs table, sorting the results in alphabetical order first to last. Because there is no config block, this model instructs dbt to create a view in the database based on this statement.

    select distinct color
    from {{ ref('diamonds_four_cs') }}
    sort by color asc
    
  3. In the project’s models directory, create a third file named diamonds_prices.sql with the following SQL statement. This statement averages diamond prices by color, sorting the results by average price from highest to lowest. This model instructs dbt to create a view in the database based on this statement.

    select color, avg(price) as price
    from diamonds
    group by color
    order by price desc
    
  4. With the virtual environment activated, run the dbt run command with the paths to the three preceding files. In the default database (as specified in the profiles.yml file), dbt creates one table named diamonds_four_cs and two views named diamonds_list_colors and diamonds_prices. dbt gets these view and table names from their related .sql file names.

    dbt run --model models/diamonds_four_cs.sql models/diamonds_list_colors.sql models/diamonds_prices.sql
    
    ...
    ... | 1 of 3 START table model default.diamonds_four_cs.................... [RUN]
    ... | 1 of 3 OK created table model default.diamonds_four_cs............... [OK ...]
    ... | 2 of 3 START view model default.diamonds_list_colors................. [RUN]
    ... | 2 of 3 OK created view model default.diamonds_list_colors............ [OK ...]
    ... | 3 of 3 START view model default.diamonds_prices...................... [RUN]
    ... | 3 of 3 OK created view model default.diamonds_prices................. [OK ...]
    ... |
    ... | Finished running 1 table model, 2 view models ...
    
    Completed successfully
    
    Done. PASS=3 WARN=0 ERROR=0 SKIP=0 TOTAL=3
    
  5. Run the following SQL code to list information about the new views and to select all rows from the table and views.

    If you are connecting to a cluster, you can run this SQL code from a notebook that is connected to the cluster, specifying SQL as the default language for the notebook. If you are connecting to a SQL warehouse, you can run this SQL code from a query.

    SHOW views IN default;
    
    +-----------+----------------------+-------------+
    | namespace | viewName             | isTemporary |
    +===========+======================+=============+
    | default   | diamonds_list_colors | false       |
    +-----------+----------------------+-------------+
    | default   | diamonds_prices      | false       |
    +-----------+----------------------+-------------+
    
    SELECT * FROM diamonds_four_cs;
    
    +-------+---------+-------+---------+
    | carat | cut     | color | clarity |
    +=======+=========+=======+=========+
    | 0.23  | Ideal   | E     | SI2     |
    +-------+---------+-------+---------+
    | 0.21  | Premium | E     | SI1     |
    +-------+---------+-------+---------+
    ...
    
    SELECT * FROM diamonds_list_colors;
    
    +-------+
    | color |
    +=======+
    | D     |
    +-------+
    | E     |
    +-------+
    ...
    
    SELECT * FROM diamonds_prices;
    
    +-------+---------+
    | color | price   |
    +=======+=========+
    | J     | 5323.82 |
    +-------+---------+
    | I     | 5091.87 |
    +-------+---------+
    ...
    

Step 4: Create and run more complex models

In this step, you create more complex models for a set of related data tables. These data tables contain information about a fictional sports league of three teams playing a season of six games. This procedure creates the data tables, creates the models, and runs the models.

  1. Run the following SQL code to create the necessary data tables.

    If you are connecting to a cluster, you can run this SQL code from a notebook that is connected to the cluster, specifying SQL as the default language for the notebook. If you are connecting to a SQL warehouse, you can run this SQL code from a query.

    The tables and views in this step start with zzz_ to help identify them as part of this example. You do not need to follow this pattern for your own tables and views.

    DROP TABLE IF EXISTS zzz_game_opponents;
    DROP TABLE IF EXISTS zzz_game_scores;
    DROP TABLE IF EXISTS zzz_games;
    DROP TABLE IF EXISTS zzz_teams;
    
    CREATE TABLE zzz_game_opponents (
    game_id INT,
    home_team_id INT,
    visitor_team_id INT
    ) USING DELTA;
    
    INSERT INTO zzz_game_opponents VALUES (1, 1, 2);
    INSERT INTO zzz_game_opponents VALUES (2, 1, 3);
    INSERT INTO zzz_game_opponents VALUES (3, 2, 1);
    INSERT INTO zzz_game_opponents VALUES (4, 2, 3);
    INSERT INTO zzz_game_opponents VALUES (5, 3, 1);
    INSERT INTO zzz_game_opponents VALUES (6, 3, 2);
    
    /*
    +---------+--------------+-----------------+
    | game_id | home_team_id | visitor_team_id |
    +=========+==============+=================+
    | 1       | 1            | 2               |
    +---------+--------------+-----------------+
    | 2       | 1            | 3               |
    +---------+--------------+-----------------+
    | 3       | 2            | 1               |
    +---------+--------------+-----------------+
    | 4       | 2            | 3               |
    +---------+--------------+-----------------+
    | 5       | 3            | 1               |
    +---------+--------------+-----------------+
    | 6       | 3            | 2               |
    +---------+--------------+-----------------+
    */
    
    CREATE TABLE zzz_game_scores (
    game_id INT,
    home_team_score INT,
    visitor_team_score INT
    ) USING DELTA;
    
    INSERT INTO zzz_game_scores VALUES (1, 4, 2);
    INSERT INTO zzz_game_scores VALUES (2, 0, 1);
    INSERT INTO zzz_game_scores VALUES (3, 1, 2);
    INSERT INTO zzz_game_scores VALUES (4, 3, 2);
    INSERT INTO zzz_game_scores VALUES (5, 3, 0);
    INSERT INTO zzz_game_scores VALUES (6, 3, 1);
    
    /*
    +---------+-----------------+--------------------+
    | game_id | home_team_score | visitor_team_score |
    +=========+=================+====================+
    | 1       | 4               | 2                  |
    +---------+-----------------+--------------------+
    | 2       | 0               | 1                  |
    +---------+-----------------+--------------------+
    | 3       | 1               | 2                  |
    +---------+-----------------+--------------------+
    | 4       | 3               | 2                  |
    +---------+-----------------+--------------------+
    | 5       | 3               | 0                  |
    +---------+-----------------+--------------------+
    | 6       | 3               | 1                  |
    +---------+-----------------+--------------------+
    */
    
    CREATE TABLE zzz_games (
    game_id INT,
    game_date DATE
    ) USING DELTA;
    
    INSERT INTO zzz_games VALUES (1, '2020-12-12');
    INSERT INTO zzz_games VALUES (2, '2021-01-09');
    INSERT INTO zzz_games VALUES (3, '2020-12-19');
    INSERT INTO zzz_games VALUES (4, '2021-01-16');
    INSERT INTO zzz_games VALUES (5, '2021-01-23');
    INSERT INTO zzz_games VALUES (6, '2021-02-06');
    
    /*
    +---------+------------+
    | game_id | game_date  |
    +=========+============+
    | 1       | 2020-12-12 |
    +---------+------------+
    | 2       | 2021-01-09 |
    +---------+------------+
    | 3       | 2020-12-19 |
    +---------+------------+
    | 4       | 2021-01-16 |
    +---------+------------+
    | 5       | 2021-01-23 |
    +---------+------------+
    | 6       | 2021-02-06 |
    +---------+------------+
    */
    
    CREATE TABLE zzz_teams (
    team_id INT,
    team_city VARCHAR(15)
    ) USING DELTA;
    
    INSERT INTO zzz_teams VALUES (1, "San Francisco");
    INSERT INTO zzz_teams VALUES (2, "Seattle");
    INSERT INTO zzz_teams VALUES (3, "Amsterdam");
    
    /*
    +---------+---------------+
    | team_id | team_city     |
    +=========+===============+
    | 1       | San Francisco |
    +---------+---------------+
    | 2       | Seattle       |
    +---------+---------------+
    | 3       | Amsterdam     |
    +---------+---------------+
    */
    
  2. In the project’s models directory, create a file named zzz_game_details.sql with the following SQL statement. This statement creates a table that provides the details of each game, such as team names and scores. The config block instructs dbt to create a table in the database based on this statement.

    -- Create a table that provides full details for each game, including
    -- the game ID, the home and visiting teams' city names and scores,
    -- the game winner's city name, and the game date.
    
    {{ config(
      materialized='table',
      file_format='delta'
    ) }}
    
    -- Step 4 of 4: Replace the visitor team IDs with their city names.
    select
      game_id,
      home,
      t.team_city as visitor,
      home_score,
      visitor_score,
      -- Step 3 of 4: Display the city name for each game's winner.
      case
        when
          home_score > visitor_score
            then
              home
        when
          visitor_score > home_score
            then
              t.team_city
      end as winner,
      game_date as date
    from (
      -- Step 2 of 4: Replace the home team IDs with their actual city names.
      select
        game_id,
        t.team_city as home,
        home_score,
        visitor_team_id,
        visitor_score,
        game_date
      from (
        -- Step 1 of 4: Combine data from various tables (for example, game and team IDs, scores, dates).
        select
          g.game_id,
          go.home_team_id,
          gs.home_team_score as home_score,
          go.visitor_team_id,
          gs.visitor_team_score as visitor_score,
          g.game_date
        from
          zzz_games as g,
          zzz_game_opponents as go,
          zzz_game_scores as gs
        where
          g.game_id = go.game_id and
          g.game_id = gs.game_id
      ) as all_ids,
        zzz_teams as t
      where
        all_ids.home_team_id = t.team_id
    ) as visitor_ids,
      zzz_teams as t
    where
      visitor_ids.visitor_team_id = t.team_id
    order by game_date desc
    
  3. In the project’s models directory, create a file named zzz_win_loss_records.sql with the following SQL statement. This statement creates a view that lists team win-loss records for the season.

    -- Create a view that summarizes the season's win and loss records by team.
    
    -- Step 2 of 2: Calculate the number of wins and losses for each team.
    select
      winner as team,
      count(winner) as wins,
      -- Each team played in 4 games.
      (4 - count(winner)) as losses
    from (
      -- Step 1 of 2: Determine the winner and loser for each game.
      select
        game_id,
        winner,
        case
          when
            home = winner
              then
                visitor
          else
            home
        end as loser
      from {{ ref('zzz_game_details') }}
    )
    group by winner
    order by wins desc
    
  4. With the virtual environment activated, run the dbt run command with the paths to the two preceding files. In the default database (as specified in the profiles.yml file), dbt creates one table named zzz_game_details and one view named zzz_win_loss_records. dbt gets these view and table names from their related .sql file names.

    dbt run --model models/zzz_game_details.sql models/zzz_win_loss_records.sql
    
    ...
    ... | 1 of 2 START table model default.zzz_game_details.................... [RUN]
    ... | 1 of 2 OK created table model default.zzz_game_details............... [OK ...]
    ... | 2 of 2 START view model default.zzz_win_loss_records................. [RUN]
    ... | 2 of 2 OK created view model default.zzz_win_loss_records............ [OK ...]
    ... |
    ... | Finished running 1 table model, 1 view model ...
    
    Completed successfully
    
    Done. PASS=2 WARN=0 ERROR=0 SKIP=0 TOTAL=2
    
  5. Run the following SQL code to list information about the new view and to select all rows from the table and view.

    If you are connecting to a cluster, you can run this SQL code from a notebook that is connected to the cluster, specifying SQL as the default language for the notebook. If you are connecting to a SQL warehouse, you can run this SQL code from a query.

    SHOW VIEWS FROM default LIKE 'zzz_win_loss_records';
    
    +-----------+----------------------+-------------+
    | namespace | viewName             | isTemporary |
    +===========+======================+=============+
    | default   | zzz_win_loss_records | false       |
    +-----------+----------------------+-------------+
    
    SELECT * FROM zzz_game_details;
    
    +---------+---------------+---------------+------------+---------------+---------------+------------+
    | game_id | home          | visitor       | home_score | visitor_score | winner        | date       |
    +=========+===============+===============+============+===============+===============+============+
    | 1       | San Francisco | Seattle       | 4          | 2             | San Francisco | 2020-12-12 |
    +---------+---------------+---------------+------------+---------------+---------------+------------+
    | 2       | San Francisco | Amsterdam     | 0          | 1             | Amsterdam     | 2021-01-09 |
    +---------+---------------+---------------+------------+---------------+---------------+------------+
    | 3       | Seattle       | San Francisco | 1          | 2             | San Francisco | 2020-12-19 |
    +---------+---------------+---------------+------------+---------------+---------------+------------+
    | 4       | Seattle       | Amsterdam     | 3          | 2             | Seattle       | 2021-01-16 |
    +---------+---------------+---------------+------------+---------------+---------------+------------+
    | 5       | Amsterdam     | San Francisco | 3          | 0             | Amsterdam     | 2021-01-23 |
    +---------+---------------+---------------+------------+---------------+---------------+------------+
    | 6       | Amsterdam     | Seattle       | 3          | 1             | Amsterdam     | 2021-02-06 |
    +---------+---------------+---------------+------------+---------------+---------------+------------+
    
    SELECT * FROM zzz_win_loss_records;
    
    +---------------+------+--------+
    | team          | wins | losses |
    +===============+======+========+
    | Amsterdam     | 3    | 1      |
    +---------------+------+--------+
    | San Francisco | 2    | 2      |
    +---------------+------+--------+
    | Seattle       | 1    | 3      |
    +---------------+------+--------+
    

Step 5: Create and run tests

In this step, you create tests, which are assertions you make about your models. When you run these tests, dbt tells you if each test in your project passes or fails.

There are two type of tests. Schema tests, applied in YAML, return the number of records that do not pass an assertion. When this number is zero, all records pass, therefore the tests pass. Data tests are specific queries that must return zero records to pass.

  1. In the project’s models directory, create a file named schema.yml with the following content. This file includes schema tests that determine whether the specified columns have unique values, are not null, have only the specified values, or a combination.

    version: 2
    
    models:
      - name: zzz_game_details
        columns:
          - name: game_id
            tests:
              - unique
              - not_null
          - name: home
            tests:
              - not_null
              - accepted_values:
                  values: ['Amsterdam', 'San Francisco', 'Seattle']
          - name: visitor
            tests:
              - not_null
              - accepted_values:
                  values: ['Amsterdam', 'San Francisco', 'Seattle']
          - name: home_score
            tests:
              - not_null
          - name: visitor_score
            tests:
              - not_null
          - name: winner
            tests:
              - not_null
              - accepted_values:
                  values: ['Amsterdam', 'San Francisco', 'Seattle']
          - name: date
            tests:
              - not_null
      - name: zzz_win_loss_records
        columns:
          - name: team
            tests:
              - unique
              - not_null
              - relationships:
                  to: ref('zzz_game_details')
                  field: home
          - name: wins
            tests:
              - not_null
          - name: losses
            tests:
              - not_null
    
  2. In the project’s tests directory, create a file named zzz_game_details_check_dates.sql with the following SQL statement. This file includes a data test to determine whether any games happened outside of the regular season.

    -- This season's games happened between 2020-12-12 and 2021-02-06.
    -- For this test to pass, this query must return no results.
    
    select date
    from {{ ref('zzz_game_details') }}
    where date < '2020-12-12'
    or date > '2021-02-06'
    
  3. In the project’s tests directory, create a file named zzz_game_details_check_scores.sql with the following SQL statement. This file includes a data test to determine whether any scores were negative or any games were tied.

    -- This sport allows no negative scores or tie games.
    -- For this test to pass, this query must return no results.
    
    select home_score, visitor_score
    from {{ ref('zzz_game_details') }}
    where home_score < 0
    or visitor_score < 0
    or home_score = visitor_score
    
  4. In the project’s tests directory, create a file named zzz_win_loss_records_check_records.sql with the following SQL statement. This file includes a data test to determine whether any teams had negative win or loss records, had more win or loss records than games played, or played more games than were allowed.

    -- Each team participated in 4 games this season.
    -- For this test to pass, this query must return no results.
    
    select wins, losses
    from {{ ref('zzz_win_loss_records') }}
    where wins < 0 or wins > 4
    or losses < 0 or losses > 4
    or (wins + losses) > 4
    
  5. With the virtual environment activated, run the dbt test command with the --schema option and names of the two models in the models/schema.yml file to run the tests that are specified for those models.

    dbt test --schema --models zzz_game_details zzz_win_loss_records
    
    ...
    ... | 1 of 15 START test accepted_values_zzz_game_details_home__Amsterdam__San_Francisco__Seattle [RUN]
    ... | 1 of 15 PASS accepted_values_zzz_game_details_home__Amsterdam__San_Francisco__Seattle [PASS ...]
    ...
    ... |
    ... | Finished running 15 tests ...
    
    Completed successfully
    
    Done. PASS=15 WARN=0 ERROR=0 SKIP=0 TOTAL=15
    
  6. Run the dbt test command with the --data option to run the tests in the project’s tests directory.

    dbt test --data
    
    ...
    ... | 1 of 3 START test zzz_game_details_check_dates....................... [RUN]
    ... | 1 of 3 PASS zzz_game_details_check_dates............................. [PASS ...]
    ...
    ... |
    ... | Finished running 3 tests ...
    
    Completed successfully
    
    Done. PASS=3 WARN=0 ERROR=0 SKIP=0 TOTAL=3
    

Step 6: Clean up

You can delete the tables and views you created for this example by running the following SQL code.

If you are connecting to a cluster, you can run this SQL code from a notebook that is connected to the cluster, specifying SQL as the default language for the notebook. If you are connecting to a SQL warehouse, you can run this SQL code from a query.

DROP TABLE zzz_game_opponents;
DROP TABLE zzz_game_scores;
DROP TABLE zzz_games;
DROP TABLE zzz_teams;
DROP TABLE zzz_game_details;
DROP VIEW zzz_win_loss_records;

DROP TABLE diamonds;
DROP TABLE diamonds_four_cs;
DROP VIEW diamonds_list_colors;
DROP VIEW diamonds_prices;

Next steps

  • Learn more about dbt models.

  • Learn more about how to test your dbt projects.

  • Learn how to use Jinja, a templating language, for programming SQL in your dbt projects.

  • Learn about dbt best practices.

  • Learn about dbt Cloud, a hosted version of dbt.

Troubleshooting

This section addresses common issues when using dbt Core with Databricks.

General troubleshooting

See Getting help on the dbt Labs website.