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Version: 1.1.0 (latest)

How to add credentials

Adding credentials locally

When using a pipeline locally, we recommend using the .dlt/secrets.toml method.

To do so, open your dlt secrets file and match the source names and credentials to the ones in your script, for example:

[sources.pipedrive]
pipedrive_api_key = "pipedrive_api_key" # please set me up!

[destination.bigquery]
location = "US"

[destination.bigquery.credentials]
project_id = "project_id" # please set me up!
private_key = "private_key" # please set me up!
client_email = "client_email" # please set me up!

Note that for toml names are case-sensitive and sections are separated with ".".

For destination credentials, read the documentation pages for each destination to create and configure credentials.

For Verified Source credentials, read the Setup Guides for each source to find how to get credentials.

Once you have credentials for the source and destination, add them to the file above and save them.

Read more about credential configuration.

Adding credentials to your deployment

To add credentials to your deployment,

Reading credentials from environment variables

dlt supports reading credentials from the environment. For example, our .dlt/secrets.toml might look like:

[sources.pipedrive]
pipedrive_api_key = "pipedrive_api_key" # please set me up!

[destination.bigquery]
location = "US"

[destination.bigquery.credentials]
project_id = "project_id" # please set me up!
private_key = "private_key" # please set me up!
client_email = "client_email" # please set me up!

If dlt tries to read this from environment variables, it will use a different naming convention.

For environment variables, all names are capitalized and sections are separated with a double underscore "__".

For example, for the secrets mentioned above, we would need to set them in the environment:

SOURCES__PIPEDRIVE__PIPEDRIVE_API_KEY
DESTINATION__BIGQUERY__CREDENTIALS__PROJECT_ID
DESTINATION__BIGQUERY__CREDENTIALS__PRIVATE_KEY
DESTINATION__BIGQUERY__CREDENTIALS__CLIENT_EMAIL
DESTINATION__BIGQUERY__LOCATION

Retrieving credentials from Google Cloud Secret Manager

To retrieve secrets from Google Cloud Secret Manager using Python, and convert them into a dictionary format, you'll need to follow these steps. First, ensure that you have the necessary permissions to access the secrets on Google Cloud, and have the google-cloud-secret-manager library installed. If not, you can install it using pip:

pip install google-cloud-secret-manager

Google Cloud Documentation: Secret Manager client libraries.

Here's how you can retrieve secrets and convert them into a dictionary:

  1. Set up the Secret Manager client: Create a client that will interact with the Secret Manager API.
  2. Access the secret: Use the client to access the secret's latest version.
  3. Convert to a dictionary: If the secret is stored in a structured format (like JSON), parse it into a Python dictionary.

Assume we store secrets in JSON format with name "temp-secret":

{"api_token": "ghp_Kskdgf98dugjf98ghd...."}

Set .dlt/secrets.toml as:

[google_secrets.credentials]
"project_id" = "<project_id>"
"private_key" = "-----BEGIN PRIVATE KEY-----\n....\n-----END PRIVATE KEY-----\n"
"client_email" = "....gserviceaccount.com"

or GOOGLE_SECRETS__CREDENTIALS to the path of your service account key file.

Retrieve the secrets stored in the Secret Manager as follows:

import json as json_lib  # Rename the json import to avoid name conflict

import dlt
from dlt.sources.helpers import requests
from dlt.common.configuration.inject import with_config
from dlt.common.configuration.specs import GcpServiceAccountCredentials
from google.cloud import secretmanager

@with_config(sections=("google_secrets",))
def get_secret_dict(secret_id: str, credentials: GcpServiceAccountCredentials = dlt.secrets.value) -> dict:
"""
Retrieve a secret from Google Cloud Secret Manager and convert it to a dictionary.
"""
# Create the Secret Manager client with provided credentials
client = secretmanager.SecretManagerServiceClient(credentials=credentials.to_native_credentials())

# Build the resource name of the secret version
name = f"projects/{credentials.project_id}/secrets/{secret_id}/versions/latest"

# Access the secret version
response = client.access_secret_version(request={"name": name})

# Decode the payload to a string and convert it to a dictionary
secret_string = response.payload.data.decode("UTF-8")
secret_dict = json_lib.loads(secret_string)

return secret_dict

# Retrieve secret data as a dictionary for use in other functions.
secret_data = get_secret_dict("temp-secret")

# Set up the request URL and headers
url = "https://api.github.com/orgs/dlt-hub/repos"
headers = {
"Authorization": f"token {secret_data['api_token']}", # Use the API token from the secret data
"Accept": "application/vnd.github+json", # Set the Accept header for GitHub API
}

# Make a request to the GitHub API to get the list of repositories
response = requests.get(url, headers=headers)

# Set up the DLT pipeline
pipeline = dlt.pipeline(
pipeline_name="quick_start", destination="duckdb", dataset_name="mydata"
)
# Run the pipeline with the data from the GitHub API response
load_info = pipeline.run(response.json())
# Print the load information to check the results
print(load_info)

Points to Note:

  • Permissions: Ensure the service account or user credentials you are using have the necessary permissions to access the Secret Manager and the specific secrets.
  • Secret format: This example assumes that the secret is stored in a JSON string format. If your secret is in a different format, you will need to adjust the parsing method accordingly.
  • Google Cloud authentication: Make sure your environment is authenticated with Google Cloud. This can typically be done by setting credentials in .dlt/secrets.toml or setting the GOOGLE_SECRETS__CREDENTIALS environment variable to the path of your service account key file or the dict of credentials as a string.

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

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