Add a verified source

Follow the steps below to create a pipeline from a verified source contributed by dlt users.

Please make sure you have installed dlt before following the steps below.

1. Initialize project

Create a new empty directory for your dlt project by running:

mkdir various_pipelines
cd various_pipelines

List available sources to see their names and descriptions:

dlt init --list-sources

Now pick one of the source names, for example, pipedrive and a destination, i.e., bigquery:

dlt init pipedrive bigquery

The command will create your pipeline project by copying over the pipedrive folder and creating a .dlt folder:

├── .dlt
│   ├── config.toml
│   └── secrets.toml
├── pipedrive
│   └── helpers
│   └── __init__.py
│   └── settings.py
│   └── typing.py
├── .gitignore
├── pipedrive_pipeline.py
└── requirements.txt

After running the command, read the command output for the instructions on how to install the dependencies:

Verified source pipedrive was added to your project!
* See the usage examples and code snippets to copy from pipedrive_pipeline.py
* Add credentials for bigquery and other secrets in .dlt/secrets.toml
* Add the required dependencies to pyproject.toml:
  dlt[bigquery]>=0.3.1
  If the dlt dependency is already added, make sure you install the extra for bigquery to it
  If you are using poetry you may issue the following command:
  poetry add dlt -E bigquery
  For uv, use:
  uv add "dlt[bigquery]"

* Read https://dlthub.com/docs/walkthroughs/create-a-pipeline for more information

So make sure you install the requirements with pip install -r requirements.txt. When deploying to an online orchestrator, you can install the requirements to it from requirements.txt in the ways supported by the orchestrator.

Finally, run the pipeline, fill the secrets.toml with your credentials or place your credentials in the supported locations.

2. Adding credentials

For adding them locally or on your orchestrator, please see the following guide credentials.

3. Customize or write a pipeline script

Once you have initialized the pipeline, you will have a sample file pipedrive_pipeline.py.

This is the developer's suggested way to use the pipeline, so you can use it as a starting point - in our case, we can choose to run a method that loads all data, or we can choose which endpoints should load.

You can also use this file as a suggestion and write your own instead.

4. Hack a verified source

You can modify an existing verified source in place.

• If that modification is generally useful for anyone using this source, consider contributing it back via a PR. This way, we can ensure it is tested and maintained.
• If that modification is not a generally shared case, then you are responsible for maintaining it. We suggest making any of your own customizations modular if possible, so you can keep pulling the updated source from the community repo in the event of source maintenance.

5. Add more sources to your project

dlt init chess duckdb

To add another verified source, just run the dlt init command at the same location as the first pipeline:

• The shared files will be updated (secrets, config).
• A new folder will be created for the new source.
• Do not forget to install the requirements for the second source!

6. Update the verified source with the newest version

To update the verified source you have to the newest online version just do the same init command in the parent folder:

dlt init pipedrive bigquery

7. Advanced: Using dlt init with branches, local folders, or git repos

To find out more info about this command, use --help:

dlt init --help

To deploy from a branch of the verified-sources repo, you can use the following:

dlt init source destination --branch <branch_name>

To deploy from another repo, you could fork the verified-sources repo and then provide the new repo URL as below, replacing dlt-hub with your fork name:

dlt init pipedrive bigquery --location "https://github.com/dlt-hub/verified-sources"