Streamlit

Streamlit uses Python to build shareable dashboards without the need for front-end development experience.

Step 1: Clone the repository

Run the command below to download the example dashboard:

git clone --depth 1  --filter=blob:none --sparse https://github.com/snowplow-incubator/snowplow-accelerator-resources.git ;
cd snowplow-accelerator-resources
git sparse-checkout set enhanced-consent-accelerator/streamlit

Next, move into the streamlit repository

cd enhanced-consent-accelerator/streamlit

Step 2: Install requirements

Run the command below to install the project requirements and run the virtual environment.

❗❗ This implementation has been tested with the following dependencies: python = 3.9.13, streamlit = 1.18.1, pandas = 1.5.3, pandas-gbq = 0.19.1, plotly = 5.13.0, snowflake-connector-python = 3.0.0, pyarrow = 10.0.1 . If you run into package compatibility issues or encounter any errors try using them to build your own environment.

pipenv install
pipenv shell

Step 3: Set-up Database Connection

Create a secrets file at .streamlit/secrets.toml and add your BigQuery/Databricks/Snowflake connection details. For BigQuery: make sure you specify your custom derived dataset which will be the source schema for the dashboard.

Ensure secrets.toml is in .gitignore to keep your information safe.

For BigQuery, we recommend setting up your credentials in a similar way to your dbt profiles.yml, as seen here

# .streamlit/secrets.toml

[gcp_service_account]
type = "service_account"
project_id = "xxx"
private_key_id = "xxx"
private_key = "xxx"
client_email = "xxx"
client_id = "xxx"
auth_uri = "https://accounts.google.com/o/oauth2/auth"
token_uri = "https://oauth2.googleapis.com/token"
auth_provider_x509_cert_url = "https://www.googleapis.com/oauth2/v1/certs"
client_x509_cert_url = "xxx"

[bigquery]
project_id = "xxx"
dataset = "dbt_xxx_derived"

For Snowflake we recommend setting up your credentials in a similar way to your dbt profile.yml, as seen here

# .streamlit/secrets.toml

[snowflake]
user = "xxx"
password = "xxx"
account = "xxx"
database = "xxx"
schema = "xxx" # This should point to your derived schema
warehouse = "xxx"
role = "xxx"

For Databricks, we recommend setting up your credentials in a similar way to your dbt profiles.yml, as seen here

# .streamlit/secrets.toml

[databricks]
databricks_server_hostname = "xxx"
databricks_http_path = "xxx"
databricks_token = "xxx"
schema = "xxx" # This should point to your derived schema
catalog = "xxx"