Projects

At the core of the Meltano experience is your Meltano project, which represents the single source of truth regarding your ELT pipelines: how data should be integrated and transformed, how the pipelines should be orchestrated, and how the various plugins that make up your pipelines should be configured.

Since a Meltano project is just a directory on your filesystem containing text-based files, you can treat it like any other software development project and benefit from DataOps best practices such as version control, code review, and continuous integration and deployment (CI/CD).

You can initialize a new Meltano project using meltano init.

meltano.yml project file

At a minimum, a Meltano project must contain a project file named meltano.yml, which contains your project configuration and tells Meltano that a particular directory is a Meltano project.

The only required property is version, which currently always holds the value 1. You can find a formal JSON Schema for the specification on SchemaStore.org or directly in the main repository here, which can be useful for code generation by many tools like datamodel-code-generator or swagger-codegen.

Configuration

At the root of meltano.yml, and usually at the top of the file, you will find project-specific configuration.

In a newly initialized project, a few environments will be populated to get you started.

To learn which settings are available, refer to the Settings reference.

Plugins

Your project's plugins, typically added to your project using meltano add, are defined under the plugins property, inside an array named after the plugin type (e.g. extractors, loaders).

Every plugin in your project needs to have:

1. a name that's unique among plugins of the same type,
2. a base plugin description describing the package in terms Meltano can understand, and
3. configuration that can be defined across various layers, including the definition's config property.

A base plugin description consists of the pip_url, executable, capabilities, and settings properties, but not every plugin definition will specify these explicitly:

• An inheriting plugin definition has an inherit_from property and inherits its base plugin description from another plugin in your project or a discoverable plugin identified by name.
• A custom plugin definition has a namespace property instead and explicitly defines its base plugin description.
• A shadowing plugin definition has neither property and implicitly inherits its base plugin description from the discoverable plugin with the same name.

When inheriting a base plugin description, the plugin definition does not need to explicitly specify a pip_url (the package's pip install argument), but you may want to override the inherited value and set the property explicitly to point at a (custom) fork or to pin a package to a specific version. When a plugin is added using meltano add, the pip_url is automatically repeated in the plugin definition for convenience.

In order to support version-specific pip constraint files, the pip_url value can optionally be parameterized using the ${MELTANO__PYTHON_VERSION} variable. This is a special variable populated by Meltano with the specific version of Python used to install the plugin and will inject the major and minor versions (e.g. 3.9, 3.10, etc.).

Inheriting plugin definitions

A plugin defined with an inherit_from property inherits its base plugin description from another plugin identified by name. To find the matching plugin, other plugins in your project are considered first, followed by discoverable plugins:

meltano.yml

plugins:
  extractors:
  - name: tap-postgres          # Shadows discoverable `tap-postgres` (see below)
  - name: tap-postgres--billing
    inherit_from: tap-postgres  # Inherits from project's `tap-postgres`
  - name: tap-bigquery--events
    inherit_from: tap-bigquery  # Inherits from discoverable `tap-bigquery`

When inheriting from another plugin in your project, its configuration is also inherited as if the values were defaults, which can then be overridden as appropriate:

meltano.yml

plugins:
  extractors:
  - name: tap-google-analytics
    variant: meltano
    config:
      key_file_location: client_secrets.json
      start_date: '2020-10-01T00:00:00Z'
  - name: tap-ga--view-foo
    inherit_from: tap-google-analytics
    config:
      # `key_file_location` and `start_date` are inherited
      view_id: 123456
  - name: tap-ga--view-bar
    inherit_from: tap-google-analytics
    config:
      # `key_file_location` is inherited
      start_date: '2020-12-01T00:00:00Z' # `start_date` is overridden
      view_id: 789012

Note that the presence of a variant property causes only discoverable plugins to be considered (even if there is also a matching plugin in the project), since only these can have multiple variants:

meltano.yml

plugins:
  loaders:
  - name: target-snowflake          # Shadows discoverable `target-snowflake` (see below)
    variant: datamill-co            # using variant `datamill-co`
  - name: target-snowflake--derived
    inherit_from: target-snowflake  # Inherits from project's `target-snowflake`
  - name: target-snowflake--transferwise
    inherit_from: target-snowflake  # Inherits from discoverable `target-snowflake`
    variant: transferwise           # using variant `transferwise`

To learn how to add an inheriting plugin to your project, refer to the Plugin Management guide.

Custom plugin definitions

A plugin defined with a namespace property (but no inherit_from property) is a custom plugin that explicitly defines its base plugin description:

meltano.yml

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 how to add a custom plugin to your project, refer to the Plugin Management guide.

Shadowing plugin definitions

A plugin defined without an inherit_from or namespace property implicitly inherits its base plugin description from the discoverable plugin with the same name, as a form of shadowing:

meltano.yml

plugins:
  extractors:
  - name: tap-gitlab

To learn how to add a discoverable plugin to your project, refer to the Plugin Management guide.

Variants

If multiple variants of a discoverable plugin are available, the variant property can be used to choose a specific one:

meltano.yml

plugins:
  extractors:
  - name: tap-gitlab
    variant: meltano

If no variant is specified, the original variant supported by Meltano is used. Note that this is not necessarily the default variant that is recommended to new users and would be used if the plugin were newly added to the project.

Plugin configuration

A plugin's configuration is stored under a config property. Values for plugin extras are stored among the plugin's other properties, outside of the config object:

extractors:
- name: tap-example
  config:
    # Configuration goes here!
    example_setting: value
  # Extras go here!
  example_extra: value

Plugin commands

Plugin commands are defined by the commands property. The keys are the name of the command and the values are the arguments to be passed to the plugin executable. These can contain dynamic references to configuration using the Environment variable form of the configuration option.

utilities:
- name: dbt-snowflake
  variant: dbt-labs
  commands:
    my_models:
      args: run --select +my_model_name
      description: Run dbt, selecting model `my_model_name` and all upstream models. Read more about the dbt node selection syntax at https://docs.getdbt.com/reference/node-selection/syntax

Commands can optionally specify some documentation displayed when listing commands. They can also optionally specify an alternative executable from the default one for the plugin.

- name: dagster
  variant: quantile-development
  commands:
    start:
      args: -f $REPOSITORY_DIR/repository.py
      description: Start Dagster.
      executable: dagit_invoker

Containerized commands

Commands can specify a container_spec for containerized execution. To execute containerized commands where possible, use the --containers flag.

See the full YAML reference for the container spec for more information.

Jobs

Your project's predefined pipelines, typically created using meltano job, are defined under the jobs property.

A job definition must have a name and one or more tasks:

jobs:
  - name: tap-foo-to-target-bar-dbt
    tasks:
      - tap-foo target-bar dbt:run
  - name: tap-foo-to-targets-bar-and-baz
    tasks:
      - tap-foo target-bar
      - tap-foo target-baz

You can learn more about how tasks are defined and run in the meltano job documentation.

Schedules

Your project's pipeline schedules, typically created using meltano schedule, are defined under the schedules property.

A scheduled job must have a name, job and interval:

schedules:
  - name: foo-to-bar
    job: tap-foo-to-target
    interval: "@hourly"

The value for job must be the name of an existing job within the project.

Alternatively, you can provide a name, extractor, loader, transform, and interval in place of a job:

- name: foo-to-bar-elt
  extractor: tap-foo
  loader: target-bar
  transform: skip
  interval: "@hourly"

Pipeline-specific configuration can be specified using environment variables in an env dictionary:

schedules:
- name: foo-to-bar
  job: tap-foo-to-target-bat
  interval: "@hourly"
  env:
    TAP_FOO_BAR: bar
    TAP_FOO_BAZ: baz

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

Multiple YAML Files

As your project grows, and your meltano.yml with it, you may wish to break your config into multiple .yml files and to store those subfiles in various places in your Project folder hierachy.

This can be done by creating new .yml files and adding them (directly or via a glob pattern) to the include_paths key of your meltano.yml:

include_paths:
  - "./subconfig_[0-9].yml"
  - "./*/subconfig_[0-9].yml"
  - "./*/**/subconfig_[0-9].yml"

Meltano will use these paths or patterns to collect the config from them for use in your Project. Although the creation of subfiles is manual, once created any elements within each subfile can be updated using the meltano config CLI. Adding new config elements places them in meltano.yml. We are working on ways to direct new config into specific subfiles (#2985).

Currently supported elements in subfiles are plugins, schedules and environments.

Annotations

To better integrate with software other than the core Meltano library and CLI, meltano.yml support "annotations", which is a dictionary that map from tool/vendor names to arbitrary dictionaries with whatever that tool/vendor wants to annotate the Meltano config with.

annotations:
  arbitrary-third-party-tool: {
    # Configuration for the third party tool
  }
  # etc.

The core Meltano library and CLI never access the annotations field. To access it, one must read meltano.yml. Nothing within an annotations field should be thought of as part of Meltano's own configuration - it is merely extra data that Meltano permits within its configuration files.

Annotations are supported in the following locations within meltano.yml:

• At the top level
• In a job definition
• In a schedule definition
• In an environment definition
• In a plugin definition
• In an environment plugin definition
• In a plugin setting definition

.gitignore

A newly initialized project comes with a .gitignore file to ensure that environment-specific and potentially sensitive configuration stored inside the .meltano directory and .env file is not leaked accidentally.

All other files are recommended to be checked into the repository and shared between all users and environments that may use the project.

.env

Optionally, your project can contain a .env file specifying environment variables used to configure Meltano and its plugins.

Typically, this file is used to store configuration that is environment-specific or sensitive, and should not be stored in meltano.yml and checked into version control.

meltano config <plugin> set will automatically store configuration in meltano.yml or .env as appropriate.

In a newly initialized project, this file will be included in .gitignore by default.

.meltano directory

Meltano stores various files for internal use inside a .meltano directory inside your project.

Note: $MELTANO_SYS_DIR_ROOT can be used as a replacement to $MELTANO_PROJECT_ROOT/.meltano directory.

These files are specific to the environment Meltano is running in, and should not be checked into version control. In a newly initialized project, this directory will be included in .gitignore by default.

While you would usually not want to modify files in this directory directly, knowing what's in there can aid in debugging:

• .meltano/meltano.db: The default SQLite system database.
• .meltano/logs/elt/<state_id>/<run_id>/elt.log, e.g. .meltano/logs/elt/gitlab-to-postgres/<UUID>/elt.log: meltano el, meltano elt and meltano run output logs for the specified pipeline run.
• .meltano/run/bin: Symlink to the meltano executable most recently used in this project.
• .meltano/run/elt/<state_id>/<run_id>/, e.g. .meltano/run/elt/gitlab-to-postgres/<UUID>/: Directory used by meltano el, meltano elt and meltano run to store pipeline-specific generated plugin config files, like an extractor's tap.config.json, tap.properties.json, and state.json.
• .meltano/run/<plugin name>/, e.g. .meltano/run/tap-gitlab/: Directory used by meltano invoke to store generated plugin config files.
• .meltano/<plugin type>/<plugin name>/venv/, e.g. .meltano/extractors/tap-gitlab/venv/: Python virtual environment directory that a plugin's pip package was installed into by meltano add or meltano install.

If $MELTANO_SYS_DIR_ROOT is set, all the above mentioned paths .meltano/* will point to $MELTANO_SYS_DIR_ROOT/*.

System database

Meltano stores various types of metadata in a project-specific system database, that takes the shape of a meltano.db SQLite database stored inside the .meltano directory by default. Other database backends are supported as well. Like all files stored in the .meltano directory, the system database is also environment-specific.

You can choose to use a different system database backend or configuration using the database_uri setting.

While you would usually not want to modify the system database directly, knowing what's in there can aid in debugging.

Meltano's CLI utilizes the following tables:

• runs table: One row for each meltano el, meltano elt or meltano run pipeline run, holding started/ended timestamps and incremental replication state.
• plugin_settings table: Plugin configuration set using meltano config <plugin> set or the UI when the project is deployed as read-only.

Support for other database types

Meltano is currently tested with the following databases as backends for state and configuration:

Database	Supported Versions	Extra Requirement	Example URL
SQLite	Version 3.25.0 or higher is required.	None	sqlite:///$MELTANO_SYS_DIR_ROOT/meltano.db (default)
PostgreSQL	Version 13 or higher is required.	postgres or psycopg2	postgresql+psycopg://<user>:<password>@<host>:<port>/<dbname>
MS SQL Server	Version 2019 or higher is required.	mssql	mssql+pymssql://<user>:<password@<freetds_name>/?charset=utf8

Support for other databases has been requested and is being tracked in the following issues:

• MySQL
• Snowflake

If you would like to see support for a specific database, please open an issue