# Getting Started

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!

Short on time, or just curious what the fuss is about?

To get a sense of the Meltano experience in just a few minutes, follow the examples on the homepage, or watch the "from 0 to ELT in 90 seconds" speedrun.

They can be copy-pasted right into your terminal and will take you all the way through installation, data integration (EL), data transformation (T), orchestration, and containerization with the tap-gitlab extractor and the target-jsonl and target-postgres loaders.

# Install Meltano

Before you can get started with Meltano and the meltano 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

If you're running Linux or macOS and have Python 3.6, 3.7, 3.8 or 3.9 installed, we recommend installing Meltano into a dedicated Python virtual environment inside the directory that will hold your Meltano projects.

Create and navigate to a directory to hold your Meltano projects:
```
mkdir meltano-projects
cd meltano-projects
```
Create and activate a virtual environment for Meltano inside the .venv directory:
```
python3 -m venv .venv
source .venv/bin/activate
```
Install the meltano package from PyPI:
```
pip3 install meltano
```
Optionally, verify that the meltano CLI is now available by viewing the version:
```
meltano --version
```

If anything's not behaving as expected, refer to the "Local Installation" section of the Installation guide for more details.

# Docker installation

Alternatively, and assuming you already have Docker installed and running, you can use the meltano/meltano Docker image which exposes the meltano CLI command as its entrypoint.

Pull or update the latest version of the Meltano Docker image:
```
docker pull meltano/meltano:latest
```
By default, this image comes with the oldest version of Python supported by Meltano, currently Python 3.6. If you'd like to use a newer version of Python instead, add a -python<X.Y> suffix to the image tag, e.g. latest-python3.8.
Optionally, verify that the meltano CLI is now available through the Docker image by viewing the version:
```
docker run meltano/meltano --version
```

Now, whenever this guide or the documentation asks you to run the meltano command, you'll need to run it using docker run meltano/meltano <args> as in the example above.

When running a meltano subcommand that requires access to your project (which you'll create in the next step), you'll also need to mount the project directory into the container and set it as the container's working directory:

docker run -v $(pwd):/project -w /project meltano/meltano <args>

If anything's not behaving as expected, refer to the "Installing on Docker" 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 various details of your ELT pipelines.

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

Navigate to the directory that you'd like to hold your Meltano projects, if you didn't already do so earlier:
```
mkdir meltano-projects
cd meltano-projects
```
Initialize a new project in a directory of your choosing using meltano init:
```
meltano init <project directory name>

# 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 will create a new directory with, among other things, your meltano.yml project file:
```
version: 1
send_anonymous_usage_stats: true
project_id: <random UUID>
```
It doesn't define any plugins or pipeline schedules yet, but note that the send_anonymous_usage_stats setting is enabled by default. To disable it, change the value to false and optionally remove the project_id setting.

Navigate to the newly created project directory:

cd <project directory>

# For example:
cd my-meltano-project

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.

# Add an environment

Now that you have your very own Meltano project, it's time to create your first environment (i.e. dev, test, prod). This allows you to define configurations specific to the environment you're running your project in.

Add dev, test, and prod environments:

meltano environment add dev
meltano environment add test
meltano environment add prod

Activate your environment for your shell session:
```
export MELTANO_ENVIRONMENT=dev
```

Alternatively you can include the --environment=dev argument to each meltano command.

List your available environments:
```
meltano environment list
```

You should now see a log message that says Environment 'dev' is active each time you run a meltano command.

# 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.

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
```

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

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.

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.
  
  TIP
  
  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.
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.

What if I already have a config file for this extractor?

If you've used this Singer tap before without Meltano, you may have a config file already.

If you'd like to use the same configuration with Meltano, you can skip this section and copy and paste the JSON config object into your meltano.yml project file under the plugin's config key:

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

Since YAML is a superset of JSON, the object should be indented correctly, but formatting does not need to be changed.

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:

TIP

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

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": "2021-03-01T00:00:00Z"
}

# Select entities and attributes to extract

Now that the extractor has been configured, it'll know where and how to find your data, but not yet 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.

What if I already have a catalog file for this extractor?

If you've used this Singer tap before without Meltano, you may have generated a catalog file already.

If you'd like Meltano to use it instead of generating a catalog based on the entity selection rules you'll be asked to specify below, you can skip this section and either set the catalog extractor extra or use meltano elt's --catalog option when running the data integration (EL) pipeline later on in this guide.

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, this usually means that the Singer tap does not support catalog discovery mode, and will always extract all supported entities and attributes.

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: '2021-03-01T00:00:00Z'
        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 first. This is also detailed in the CLI documentation for the --exclude parameter.

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, like PostgreSQL or MongoDB, your extractor likely requires one final setup step: setting a replication method for each selected entity (table).

TIP

Extractors for SaaS APIs typically hard-code the appropriate replication method for each supported entity, so if you're using one, you can skip this section and move on to setting up a loader.

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, e.g. 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.

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.

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-gitlab
        metadata:
          some_entity_id:
            replication-method: INCREMENTAL
            replication-key: id
          other_entity:
            replication-method: FULL_TABLE
          '*':
            replication-method: INCREMENTAL
          '*_full':
            replication-method: FULL_TABLE

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.

Find out if a loader for your data destination is supported out of the box by checking the Loaders list or using meltano discover:
```
meltano discover loaders
```
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
```
  TIP
  
  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>, e.g. Singer target BigQuery.
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.
  
  TIP
  
  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 exist yet, 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.
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 amount of configuration before it can start loading data.

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

What if I already have a config file for this loader?

If you've used this Singer target before without Meltano, you may have a config file already.

If you'd like to use the same configuration with Meltano, you can skip this section and copy and paste the JSON config object into your meltano.yml project file under the plugin's config key:

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

Since YAML is a superset of JSON, the object should be indented correctly, but formatting does not need to be changed.

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

meltano config <plugin> list

# For example:
meltano config target-postgres list

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 host localhost
meltano config target-postgres set port 5432
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

TIP

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
environments:
- name: dev
  config:
    plugins:
      loaders:
      - name: target-postgres
        config:
          host: localhost
          port: 5432
          user: meltano
          dbname: warehouse
          default_target_schema: public

Sensitive configuration (like 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

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 elt:

meltano elt <extractor> <loader> --job_id=<pipeline name>

# For example:
meltano elt tap-gitlab target-postgres --job_id=gitlab-to-postgres

TIP

The --job_id must be included on each execution if you want to run incremental syncs. This argument should define a globally unique job identifier which is used to store and retrieve state from the system database across executions. Its a good idea to make this a unique string based on the job being run (i.e. gitlab-to-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 elt another time with the same Job ID, you'll see it automatically pick up where the previous run left off, assuming the extractor supports incremental replication.

What if I already have a state file for this extractor?

If you've used this Singer tap before without Meltano, you may have a state file already.

If you'd like Meltano to use it instead of looking up state based on the Job ID, you can either use meltano elt's --state option or set the state extractor extra.

If you'd like to dump the state generated by the most recent run into a file, so that you can explicitly pass it along to the next invocation, you can use meltano elt's --dump=state option:

meltano elt <extractor> <loader> --job_id=<pipeline name> --dump=state > state.json

# For example:
meltano elt tap-gitlab target-postgres --job_id=gitlab-to-postgres --dump=state > state.json

There is also a beta meltano run command which allows you to execute the same EL pipelines in a much more flexible fashion. This command allows you to chain multiple EL pipelines and add in other plugins inline too:

meltano run <extractor> <loader> <other_plugins>

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

# 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
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 realize this, Meltano supports scheduled pipelines that can be orchestrated using Apache Airflow.

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

Schedule a new meltano elt pipeline to be invoked on an interval using meltano schedule:
```
meltano schedule <pipeline name> <extractor> <loader> <interval>

# For example:
meltano schedule gitlab-to-postgres tap-gitlab target-postgres @daily
```
The pipeline name argument corresponds to the --job_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'
```
TIP

The name setting in schedules acts as the job_id so that state is preserved across scheduled executions. This should generally be a globally unique string based on the job being run (i.e. gitlab-to-postgres or gitlab-to-postgres-prod if you have multiple environemnts).
Optionally, verify that the schedule was created successfully using meltano schedule list:
```
meltano schedule list
```
Add the Apache Airflow orchestrator to your project using meltano add, which will be responsible for managing the schedule and executing the appropriate meltano elt 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 elt DAG generator to your project's orchestrate/dags directory, where Airflow will be configured to look for DAGs by default.
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.

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

Create melty the Admin user 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 realize this, Meltano supports transformation using dbt.

To learn about data transformation, refer to the Data Transformation (T) guide.

To install the dbt transformer to your project run:
```
meltano add transformer dbt
```
Once dbt has been installed 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 ELT 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 if you'd like to run a specific set of models as part of a Meltano ELT pipeline it can be done via meltano elt tap target --transform=run which requires the model directory to match the extractor's name using snake_case (i.e. tap_gitlab) so it can automatically find your models. Running as part of a pipeline allows Meltano to simplify dbt configuration by inferring some of your settings based on the pipeline tap and target.

See more in the Data Transformation (T) guide - transform in your ELT pipeline.
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'
```
Run your dbt models, either using a pipeline transform:

meltano elt <extractor> <loader>  --transform=run --job_id=<pipeline name>

# For example:
meltano elt tap-gitlab target-postgres --transform=run --job_id=gitlab-to-postgres

Or alternatively you can run dbt directly using the meltano invoke, which requires more settings to be defined prior to running:

First add the following configs to your dbt settings:

meltano config dbt set target postgres
meltano config dbt set source_schema public

Then add the following env config, which sets environment variables at runtime, to your dev environment in the meltano.yml file.

environments:
- name: dev
  config:
    ...
  env:
    PG_ADDRESS: localhost
    PG_PORT: '5432'
    PG_USERNAME: meltano
    PG_DATABASE: warehouse

And finally add the postgres password to your .env file so that it doesnt get checked into git:
```
PG_PASSWORD="meltano"
```
After these configurations are set you can run the dbt models using invoke:
```
meltano invoke dbt:<command>

# For example:
meltano invoke dbt:run
```

There is also a beta meltano run command which 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:test dbt: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:test, dbt:seed, dbt:snapshot and selection criteria like dbt:run --models tap_gitlab.*.

# 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.

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