Complete Tutorial

Welcome! If you’re ready to get started with Meltano and run an EL[T] pipeline with a data source and destination of your choosing, you’ve come to the right place!

Install Meltano #

Before you can get started with Meltano and the meltano command line interface (CLI), you’ll need to install it onto your system.

To learn more about the different installation methods, refer to the Installation guide.

Local Installation #

You will need to be running Linux, macOS, or Windows, and have Python 3.7, 3.8, 3.9, 3.10, 3.11 installed. We recommend installing Meltano into a dedicated Python virtual environment inside the directory that will hold your Meltano projects.

1. Create and navigate to a directory to hold your Meltano projects:

   mkdir meltano-projects
   cd meltano-projects
   

2. Install the pipx package manager:

   #For Windows (PowerShell): New-Alias Python3 Python
   python3 -m pip install --user pipx
   python3 -m pipx ensurepath
   #For Windows (PowerShell): Open up a new powershell instance to load your new path variables
   source ~/.bashrc
   

   For Windows, instead of source ~/.bashrc, you'll want to open a new PowerShell instance.

3. Install the meltano package from the Python Package Index (PyPI):

   pipx install meltano
   

   If you have multiple versions of Python installed, you can use a specific one with the --python arugment:

   pipx install meltano --python <path to desired Python executable>
   

4. Optionally, verify that the meltano CLI is now available by viewing the version:

   meltano --version
   

If anything’s not performing as expected, refer to the “Local Installation” section of the Installation guide for more details.

Create Your Meltano Project #

Now that you have a way of running the meltano CLI, it’s time to create a new Meltano project that (among other things) will hold the plugins that implement the details of your ELT pipelines.

To learn more about Meltano projects, refer to the Projects concept doc.

1. Navigate to the directory that you’d like to hold your Meltano projects if you haven’t already done so:

   mkdir meltano-projects
   cd meltano-projects
   

2. Initialize a new project in a directory of your choosing using meltano init:

   meltano init <project directory path>
   
   # For example:
   meltano init my-meltano-project
   
   # If you're using Docker, don't forget to mount the current working directory:
   docker run -v $(pwd):/projects -w /projects meltano/meltano init my-meltano-project
   

   This action will create a new directory with, among other things, your meltano.yml project file:

   version: 1
   default_environment: dev
   project_id: <random UUID>
   environments:
   - name: dev
   - name: staging
   - name: prod
   

   The meltano.yml file does not define any plugins, or pipeline schedules yet, but does include 3 environments that you can use if you wish.

   Note that anonymous usage stats are enabled by default; if you want to learn more about how the product benefits from them or how to change the default settings, see the settings reference page for more details.

3. Navigate to the newly created project directory:

   cd <project directory>
   
   # For example:
   cd my-meltano-project
   

4. Optionally, if you’d like to version control your changes, initialize a Git repository and create an initial commit:

   git init
   git add --all
   git commit -m 'Initial Meltano project'
   

This will allow you to use git diff to easily check the impact of the meltano commands you’ll run below on your project files, most notably your meltano.yml project file.

View and Activate Your Environments #

As part of creating your Meltano project, we automatically added your first environments called dev, staging and prod. This allows you to define configurations specific to the environment in which you’re running your project. Theres also a default_environment setting in the meltano.yml that get automatically set to dev, you can list and change the active environment using:

1. List your available environments:

   meltano environment list
   

2. Activate your environment for your shell session:

   export MELTANO_ENVIRONMENT=dev
   

   or for Windows PowerShell:

   $env:MELTANO_ENVIRONMENT="dev"
   

   Alternatively you can include the --environment=dev argument to each meltano command. You should now see a log message that says Environment 'dev' is active each time you run a meltano command.

3. [optional] Add a new environment:

   meltano environment add <environment name>
   

Add an Extractor to Pull Data from a Source #

Now that you have your very own Meltano project, it’s time to add some plugins to it!

The first plugin you’ll want to add is an extractor, which will be responsible for pulling data out of your data source.

To learn more about adding plugins to your project, refer to the Plugin Management guide.

1. Find out if an extractor for your data source is supported out of the box by checking the Extractors list or using meltano discover:

   meltano discover extractors
   

2. Depending on the result, pick your next step:

   • If an extractor is supported out of the box, add it to your project using meltano add:

   meltano add extractor <plugin name>
   
   # For example:
   meltano add extractor tap-gitlab
   
   # If you have a preference for a non-default variant, select it using `--variant`:
   meltano add extractor tap-gitlab --variant=singer-io
   
   # If you're using Docker, don't forget to mount the project directory:
   docker run -v $(pwd):/project -w /project meltano/meltano add extractor tap-gitlab
   

   This will add the new plugin to your meltano.yml project file:

   plugins:
     extractors:
     - name: tap-gitlab
       variant: meltanolabs
       pip_url: git+https://github.com/MeltanoLabs/tap-gitlab.git
   

   Also note that if you’re using Meltano version >=2.0 you will see a plugins/extractors/tap-gitlab--meltanolabs.lock file added to your project. This pins your settings definitions for stability, they should be checked into your git repository. For additional stability you can consider pinning your pip_url to a specific release version (e.g. tap-gitlab==1.0.0) or commit hash (e.g. git+https://github.com/MeltanoLabs/tap-gitlab.git@v1.0.0).

   You can now continue to step 4.

   • If an extractor is not yet discoverable, find out if a Singer tap for your data source already exists by checking out MeltanoHub for Singer, which is the best place to find and explore existing Singer taps and targets.

3. Depending on the result, pick your next step:

   • If a Singer tap for your data source is available, add it to your project as a custom plugin using meltano add --custom:

     meltano add --custom extractor <tap name>
     
     # For example:
     meltano add --custom extractor tap-covid-19
     
     # If you're using Docker, don't forget to mount the project directory,
     # and ensure that interactive mode is enabled so that Meltano can ask you
     # additional questions about the plugin and get your answers over STDIN:
     docker run --interactive -v $(pwd):/project -w /project meltano/meltano add --custom extractor tap-covid-19
     

     Meltano will now ask you some additional questions to learn more about the plugin.

     This will add the new plugin to your meltano.yml project file:

     plugins:
       extractors:
         - name: tap-covid-19
           namespace: tap_covid_19
           pip_url: tap-covid-19
           executable: tap-covid-19
           capabilities:
             - catalog
             - discover
             - state
           settings:
             - name: api_token
             - name: user_agent
             - name: start_date
     

     To learn more about adding custom plugins, refer to the Plugin Management guide.

     Once you've got the extractor working in your project, please consider contributing its description to the index of discoverable plugins so that it can be supported out of the box for new users!

   • If a Singer tap for your data source doesn’t exist yet, learn how to build and use your own tap by following the “Create and Use a Custom Extractor” tutorial.

   Once you’ve got your new tap project set up, you can add it to your Meltano project as a custom plugin by following the meltano add --custom instructions above. When asked to provide a pip install argument, you can provide a local directory path or Git repository URL.

4. Optionally, verify that the extractor was installed successfully and that its executable can be invoked using meltano invoke:

   meltano invoke <plugin> --help
   
   # For example:
   meltano invoke tap-gitlab --help
   

   If you see the extractor’s help message printed, the plugin was definitely installed successfully, but an error message related to missing configuration or an unimplemented --help flag would also confirm that Meltano can invoke the plugin’s executable.

Configure the Extractor #

Chances are that the extractor you just added to your project will require some amount of configuration before it can start extracting data.

To learn more about managing the configuration of your plugins, refer to the Configuration guide.

extractors:
- name: tap-example
  config: {
    "setting": "value",
    "another_setting": true
  }

1. The simplest way to configure a new plugin in Meltano is using interactive:

   meltano config <plugin> set --interactive
   
   # For example:
   meltano config tap-gitlab set --interactive
   

Follow the prompts to step through all available settings, or select an individual setting to configure.

You can also optionally use the list, set and unset commands directly to view and change plugin configuration:

• Find out what settings your extractor supports using meltano config <plugin> list:

  meltano config <plugin> list
  
  # For example:
  meltano config tap-gitlab list
  

• Assuming the previous command listed at least one setting, set appropriate values using meltano config <plugin> set:

  See MeltanoHub for details on how to get a GitLab `private_token` for tap-gitlab.

  meltano config <plugin> set <setting> <value>
  
  # For example:
  meltano config tap-gitlab set projects "meltano/meltano meltano/tap-gitlab"
  meltano config tap-gitlab set start_date 2021-03-01T00:00:00Z
  meltano config tap-gitlab set private_token my_private_token
  

  This will add the non-sensitive configuration to your meltano.yml project file:

  environments:
    - name: dev
      config:
        plugins:
          extractors:
            - name: tap-gitlab
              config:
                projects: meltano/meltano meltano/tap-gitlab
                start_date: "2021-10-01T00:00:00Z"
  

  Sensitive configuration (like private_token) will instead be stored in your project’s .env file so that it will not be checked into version control:

  export TAP_GITLAB_PRIVATE_TOKEN=my_private_token
  

1. Optionally, verify that the configuration looks like what the Singer tap expects according to its documentation using meltano config <plugin>:

   meltano config <plugin>
   
   # For example:
   meltano config tap-gitlab
   

   This will show the current configuration:

   {
     "api_url": "https://gitlab.com",
     "private_token": "my_private_token",
     "groups": "",
     "projects": "meltano/meltano meltano/tap-gitlab",
     "ultimate_license": false,
     "fetch_merge_request_commits": false,
     "fetch_pipelines_extended": false,
     "start_date": "2022-03-01T00:00:00Z"
   }
   

Select Entities and Attributes to Extract #

Now that the extractor has been configured, it’ll know where and how to find your data, but won’t yet know which specific entities and attributes (tables and columns) you’re interested in.

By default, Meltano will instruct extractors to extract all supported entities and attributes, but it’s recommended that you specify the specific entities and attributes you’d like to extract to improve performance and save on bandwidth and storage.

To learn more about selecting entities and attributes for extraction, refer to the Data Integration (EL) guide.

1. Find out whether the extractor supports entity selection, and if so, what entities and attributes are available, using meltano select --list --all:

   meltano select <plugin> --list --all
   
   # For example:
   meltano select tap-gitlab --list --all
   

   If this command fails with an error message, it usually means that the Singer tap does not support catalog discovery mode and will always extract all supported entities and attributes.

2. Assuming the previous command succeeded, select the desired entities and attributes for extraction using meltano select:

   meltano select <plugin> <entity> <attribute>
   meltano select <plugin> --exclude <entity> <attribute>
   
   # For example:
   meltano select tap-gitlab commits id
   meltano select tap-gitlab commits project_id
   meltano select tap-gitlab commits created_at
   meltano select tap-gitlab commits author_name
   meltano select tap-gitlab commits message
   
   # Include all attributes of an entity
   meltano select tap-gitlab tags "*"
   
   # Exclude matching attributes of all entities
   meltano select tap-gitlab --exclude "*" "*_url"
   

   As you can see in the example, entity and attribute identifiers can contain wildcards (*) to match multiple entities or attributes at once.

   This will add the selection rules to your meltano.yml project file:

   plugins:
     extractors:
       - name: tap-gitlab
         variant: meltanolabs
         pip_url: git+https://github.com/MeltanoLabs/tap-gitlab.git
   environments:
     - name: dev
       config:
         plugins:
           extractors:
             - name: tap-gitlab
               config:
                 projects: meltano/meltano meltano/tap-gitlab
                 start_date: "2022-03-01T00:00:00Z"
               select:
                 - commits.id
                 - commits.project_id
                 - commits.created_at
                 - commits.author_name
                 - commits.message
                 - tags.*
                 - "!*.*_url"
   

   Note that exclusion takes precedence over inclusion. If an attribute is excluded, there is no way to include it back without removing the exclusion pattern. This information is also detailed in the CLI documentation for the --exclude parameter.

3. Optionally, verify that only the intended entities and attributes are now selected using meltano select --list:

   meltano select <plugin> --list
   
   # For example:
   meltano select tap-gitlab --list
   

Choose How to Replicate Each Entity #

If the data source you’ll be pulling data from is a database, such as PostgreSQL or MongoDB, your extractor will likely require one final setup step: setting a replication method for each selected entity (table).

Most database extractors, on the other hand, support two or more of the following replication methods and require you to choose an appropriate option for each table through the replication-method stream metadata key:

• LOG_BASED: Log-based Incremental Replication

  The extractor uses the database’s binary log files to identify what records were inserted, updated, and deleted from the table since the last run (if any), and extracts only these records.

  This option is not supported by all databases and database extractors.

• INCREMENTAL: Key-based Incremental Replication

  The extractor uses the value of a specific column on the table (the Replication Key, such as an updated_at timestamp or incrementing id integer) to identify what records were inserted or updated (but not deleted) since the last run (if any), and extracts only those records.

• FULL_TABLE: Full Table Replication

  The extractor extracts all available records in the table on every run.

  To learn more about replication methods, refer to the Data Integration (EL) guide.

  1. Find out which replication methods (i.e. options for the replication-method stream metadata key) the extractor supports by checking its documentation or the README in its repository.

  2. Set the desired replication-method metadata for each selected entity using meltano config <plugin> set and the extractor’s metadata extra:

     meltano config <plugin> set _metadata <entity> replication-method <LOG_BASED|INCREMENTAL|FULL_TABLE>
     
     # For example:
     meltano config tap-postgres set _metadata some_entity_id replication-method INCREMENTAL
     meltano config tap-postgres set _metadata other_entity replication-method FULL_TABLE
     
     # Set replication-method metadata for all entities
     meltano config tap-postgres set _metadata '*' replication-method INCREMENTAL
     
     # Set replication-method metadata for matching entities
     meltano config tap-postgres set _metadata '*_full' replication-method FULL_TABLE
     

     As you can see in the example, entity identifiers can contain wildcards (*) to match multiple entities at once.

     If you’ve set a table’s replication-method to INCREMENTAL, also choose a Replication Key by setting the replication-key metadata:

     meltano config <plugin> set _metadata <entity> replication-key <column>
     
     # For example:
     meltano config tap-postgres set _metadata some_entity_id replication-key updated_at
     meltano config tap-postgres set _metadata some_entity_id replication-key id
     

     This will add the metadata rules to your meltano.yml project file:

     environments:
       - name: dev
         config:
           plugins:
             extractors:
               - name: tap-postgres
                 metadata:
                   some_entity_id:
                     replication-method: INCREMENTAL
                     replication-key: id
                   other_entity:
                     replication-method: FULL_TABLE
                   "*":
                     replication-method: INCREMENTAL
                   "*_full":
                     replication-method: FULL_TABLE
     

  3. Optionally, verify that the stream metadata for each table was set correctly in the extractor’s generated catalog file by dumping it using meltano invoke --dump=catalog <plugin>:

     meltano invoke --dump=catalog <plugin>
     
     # For example:
     meltano invoke --dump=catalog tap-postgres
     

Add a Loader to Send Data to a Destination #

Now that your Meltano project has everything it needs to pull data from your source, it’s time to tell it where that data should go!

This is where the loader comes in, which will be responsible for loading extracted data into an arbitrary data destination.

To learn more about adding plugins to your project, refer to the Plugin Management guide.

1. Find out if a loader for your data destination is supported out of the box by checking the Loaders list or using meltano discover:

   ```bash
   meltano discover loaders
   ```
   

2. Depending on the result, pick your next step:

   • If a loader is supported out of the box, add it to your project using meltano add:

     meltano add loader <plugin name>
     
     # For this example, we'll use the default variant:
     meltano add loader target-postgres
     
     # Or if you just want to use a non-default variant you can use this,
     # selected using `--variant`:
     meltano add loader target-postgres --variant=datamill-co
     

     Sometimes extractors and loaders expect that certain dependencies are already installed. If you run into any issues while installing, refer to MeltanoHub for more help troubleshooting or join the Meltano Slack workspace to ask questions.

     This will add the new plugin to your meltano.yml project file:

     plugins:
     loaders:
       - name: target-postgres
         variant: transferwise
         pip_url: pipelinewise-target-postgres
     

     You can now continue to step 4.

   • If a loader is not yet discoverable, find out if a Singer target for your data source already exists by checking Singer’s index of targets and/or doing a web search for Singer target <data destination>, for example, Singer target BigQuery.

3. Depending on the result, pick your next step:

   • If a Singer target for your data destination is available, add it to your project as a custom plugin using meltano add --custom:

     meltano add --custom loader <target name>
     
     # For example:
     meltano add --custom loader target-bigquery
     
     # If you're using Docker, don't forget to mount the project directory,
     # and ensure that interactive mode is enabled so that Meltano can ask you
     # additional questions about the plugin and get your answers over STDIN:
     docker run --interactive -v $(pwd):/project -w /project meltano/meltano add --custom loader target-bigquery
     

     Meltano will now ask you some additional questions to learn more about the plugin.

     This will add the new plugin to your meltano.yml project file:

     plugins:
       loaders:
         - name: target-bigquery
           namespace: target_bigquery
           pip_url: target-bigquery
           executable: target-bigquery
           settings:
             - name: project_id
             - name: dataset_id
             - name: table_id
     

     To learn more about adding custom plugins, refer to the Plugin Management guide.

     Once you've got the loader working in your project, please consider contributing its description to the index of discoverable plugins so that it can be supported out of the box for new users!

   • If a Singer target for your data source doesn’t yet exist, learn how to build your own target by following Singer’s “Developing a Target” guide.

   Once you’ve got your new target project set up, you can add it to your Meltano project as a custom plugin by following the meltano add --custom instructions above. When asked to provide a pip install argument, you can provide a local directory path or Git repository URL.

4. Optionally, verify that the loader was installed successfully and that its executable can be invoked using meltano invoke:

   meltano invoke <plugin> --help
   
   # For example:
   meltano invoke target-postgres --help
   

   If you see the loader’s help message printed, the plugin was definitely installed successfully, but an error message related to missing configuration or an unimplemented --help flag would also confirm that Meltano can invoke the plugin’s executable.

Configure the Loader #

Chances are that the loader you just added to your project will require some configuration before it can start loading data.

To learn more about managing the configuration of your plugins, refer to the Configuration guide.

loaders:
- name: target-example
  config: {
    "setting": "value",
    "another_setting": true
  }

1. Find out what settings your loader supports using meltano config <plugin> list:

   meltano config <plugin> list
   
   # For example:
   meltano config target-postgres list
   

2. Assuming the previous command listed at least one setting, set appropriate values using meltano config <plugin> set:

   meltano config <plugin> set <setting> <value>
   
   # For example:
   meltano config target-postgres set user meltano
   meltano config target-postgres set password meltano
   meltano config target-postgres set dbname warehouse
   meltano config target-postgres set default_target_schema public
   

   You can turn on a local postgres docker instance with these configs using docker run --name postgres -e POSTGRES_PASSWORD=meltano -e POSTGRES_USER=meltano -e POSTGRES_DB=warehouse -d -p 5432:5432 postgres.

   This will add the non-sensitive configuration to your meltano.yml project file:

   plugins:
     loaders:
       - name: target-postgres
         variant: transferwise
         pip_url: pipelinewise-target-postgres
         config:
           user: meltano
           dbname: warehouse
           default_target_schema: public
   

   Sensitive configuration information (such as password) will instead be stored in your project’s .env file so that it will not be checked into version control:

   export TARGET_POSTGRES_PASSWORD=meltano
   

3. Optionally, verify that the configuration looks like what the Singer target expects according to its documentation using meltano config <plugin>:

   meltano config <plugin>
   
   # For example:
   meltano config target-postgres
   

   This will show the current configuration:

   {
     "host": "localhost",
     "port": 5432,
     "user": "meltano",
     "password": "meltano",
     "dbname": "warehouse",
     "ssl": "false",
     "default_target_schema": "public",
     "batch_size_rows": 100000,
     "flush_all_streams": false,
     "parallelism": 0,
     "parallelism_max": 16,
     "add_metadata_columns": false,
     "hard_delete": false,
     "data_flattening_max_level": 0,
     "primary_key_required": true,
     "validate_records": false
   }
   

Run a Data Integration (EL) Pipeline #

Now that your Meltano project, extractor, and loader are all set up, we’ve reached the final chapter of this adventure, and it’s time to run your first data integration (EL) pipeline!

To learn more about data integration, refer to the Data Integration (EL) guide.

There’s just one step here: run your newly added extractor and loader in a pipeline using meltano run:

meltano run <extractor> <loader>

# For example:
meltano run tap-gitlab target-postgres

If everything was configured correctly, you should now see your data flow from your source into your destination! Check your postgres instance for the tables warehouse.schema.commits and warehouse.schema.tags.

If the command failed, but it’s not obvious how to resolve the issue, consider enabling debug mode to get some more insight into what’s going on behind the scenes. If that doesn’t get you closer to a solution, learn how to get help with your issue.

If you run meltano run at another time, it will automatically pick up where the previous run left off, assuming the extractor supports incremental replication and you have an active environment. Behind the scenes Meltano is tracking state using a State ID that’s auto-generated based on the extractor name, loader name, and active environment name. To override the state and extract all data from the beginning again you can use the --full-refresh argument.

# Example
meltano state get dev:tap-gitlab-to-target-postgres

There is also the meltano elt command which is a more rigid command for running only EL pipelines.

Or directly using the meltano invoke, which only executes a single plugin at a time. This can be useful for debugging a failing extractor or loader.

Next Steps #

Now that you’ve successfully run your first data integration (EL) pipeline using Meltano, you have a few possible next steps:

• Schedule Pipelines to Run Regularly
• Transform Loaded Data for Analysis
• Analyze Your Data with Superset
• Containerize Your Project
• Deploy Your Pipelines in Production

Schedule Pipelines to Run Regularly #

Most pipelines aren’t run just once, but over and over again, to make sure additions and changes in the source eventually make their way to the destination.

To help you achieve this, Meltano supports scheduled pipelines that can be orchestrated using Apache Airflow.

To learn more about orchestration, refer to the Orchestration guide.

1. Schedule a new pipeline to be invoked on an interval using meltano schedule:

meltano schedule add <pipeline name> --extractor <extractor> --loader <loader> --interval <interval>

# For example:
meltano schedule add gitlab-to-postgres --extractor tap-gitlab --loader target-postgres --interval @daily

The pipeline name argument corresponds to the --state-id option on meltano elt, which identifies related EL(T) runs when storing and looking up incremental replication state.

To have scheduled runs pick up where your earlier manual run left off, ensure you use the same pipeline name.

This will add the new schedule to your meltano.yml project file:

schedules:
  - name: gitlab-to-postgres
    extractor: tap-gitlab
    loader: target-postgres
    transform: skip
    interval: "@daily"

1. Optionally, verify that the schedule was created successfully using meltano schedule list:

   meltano schedule list
   

2. Add the Apache Airflow orchestrator to your project using meltano add, which will be responsible for managing the schedule and executing the appropriate meltano run commands:

   meltano add orchestrator airflow
   

   This will add the new plugin to your meltano.yml project file:

   plugins:
     orchestrators:
       - name: airflow
         pip_url: apache-airflow==1.10.14
   

   It will also automatically add a meltano run DAG generator to your project’s orchestrate/dags directory, where Airflow will be configured to look for DAGs by default.

3. Start the Airflow scheduler using meltano invoke:

   meltano invoke airflow scheduler
   
   # Add `-D` to run the scheduler in the background:
   meltano invoke airflow scheduler -D
   

   As long as the scheduler is running, your scheduled pipelines will run at the appropriate times.

4. Optionally, verify that a DAG was automatically created for each scheduled pipeline by starting the Airflow web interface:

   meltano invoke airflow webserver
   
   # Add `-D` to run the scheduler in the background:
   meltano invoke airflow webserver -D
   

5. Create an Admin user called melty for logging in.

   meltano invoke airflow users create --username melty \
   --firstname melty \
   --lastname meltano \
   --role Admin \
   --password melty \
   --email melty@meltano.com
   

   The web interface and DAG overview will be available at http://localhost:8080.

Transform Loaded Data for Analysis #

Once your raw data has arrived in your data warehouse, its schema will likely need to be transformed to be more appropriate for analysis.

To help you achieve this, Meltano supports transformation using dbt. If you already have an existing dbt project that you’d like to migrate to Meltano, check out the existing dbt project guide for more details.

To learn about data transformation, refer to the Data Transformation (T) guide. dbt plugins are adapter specific so you should install the plugin that matches your warehouse (i.e. Postgres = dbt-postgres, Snowflow = dbt-snowflake, etc.). Refer to the transformers page on MeltanoHub to see all available plugins.

1. Install the dbt transformer to your project:

   meltano add transformer dbt-postgres
   

2. Configure dbt-postgres

   meltano config dbt-postgres list
   
   # For example:
   meltano config dbt-postgres set host localhost
   meltano config dbt-postgres set user meltano
   meltano config dbt-postgres set password meltano
   meltano config dbt-postgres set port 5432
   meltano config dbt-postgres set dbname warehouse
   meltano config dbt-postgres set schema analytics
   

3. Once dbt has been installed and configured in your Meltano project, you will see the /transform directory populated with dbt artifacts.

   These artifacts are installed via the dbt file bundle. For more about file bundles, refer to the Plugin File bundles.

   Now all you need to do is start writing your dbt models in the /transform/models directory. This usually consists of a source.yml file defining the source tables you will be referencing inside your dbt models.

   For example, the /transform/models/tap_gitlab/source.yml below configures dbt sources from the postgres tables where our tap-gitlab EL job output to.

   Create and navigate to the /transform/models/tap_gitlab directory to hold your dbt models:

   mkdir ./transform/models/tap_gitlab
   touch  ./transform/models/tap_gitlab/source.yml
   

   Add the following content to your new source.yml file:

   config-version: 2
   version: 2
   sources:
     - name: tap_gitlab
       schema: public
       tables:
         - name: commits
         - name: tags
   

   The organization of your dbt project is up to you, but the Meltano convention is to name the model directory after the extractor using snake_case (i.e. tap_gitlab).

   See more in the Data Transformation (T) guide - transform in your ELT pipeline.

4. Then add a model file with your SQL transformation logic. For example the dbt model SQL below generates a table with new commits in the last 7 days /transform/models/tap_gitlab/commits_last_7d.sql.

   Create your model file:

   touch  ./transform/models/tap_gitlab/commits_last_7d.sql
   

   Add the following content to your new commits_last_7d.sql file:

      
   {{
     config(
       materialized='table'
     )
   }}
   
   select *
   from {{ source('tap_gitlab', 'commits') }}
   where created_at::date >= current_date - interval '7 days'
      
   

5. Run your dbt models either using meltano run or meltano invoke:

   meltano invoke dbt-postgres:<command>
   
   # For example:
   meltano invoke dbt-postgres:run
   

   The meltano run command allows you to execute dbt in the same way as invoke but in a much more flexible fashion. This allows for inline dbt execution and more advanced reverse ETL use cases:

   meltano run <extractor> <loader> <other_plugins>
   
   # For example:
   meltano run tap-gitlab target-postgres dbt-postgres:test dbt-postgres:run tap-postgres target-gsheet
   

   After your transform run is complete, you should see a new table named after your model warehouse.analytics.commits_last_7d in your target.

   See the transformer docs from other supported dbt commands like dbt-postgres:test, dbt-postgres:seed, dbt-postgres:snapshot and selection criteria like dbt-postgres:run --models tap_gitlab.*.

Analyze Your Data with Superset #

To learn how to install and use Superset in your project, refer to the Analyze data docs.

Containerize Your Project #

To learn how to containerize your project, refer to the Containerization guide.

Deploy Your Pipelines in Production #

To learn how to deploy your pipelines in production, refer to the Deployment in Production guide.