dlt Data Pipeline - Google Sheets to Snowflake

This guide will walk you through setting up automated data pipelines that extract data from Google Sheets and load it into Snowflake using Paradime and dltHub (a Python data loading tool). By the end, you’ll be able to:

Set up a Python pipeline project alongside your dbt project
Extract data from Google Sheets automatically
Load that data into Snowflake
Run pipelines both in development and production

Prerequisites

Before starting, make sure you have:

Paradime account with access to a workspace
Google Cloud Platform (GCP) account for Google Sheets API access
Snowflake credentials with permissions to create schemas and tables
Google spreadsheet with some data

Part 1: Understanding the Project Structure

Your project will look like this:

your-project/
├── dbt_project.yml          # Your existing dbt project configuration
├── pyproject.toml            # Python dependency management (we'll create this)
├── python_dlt/               # Folder for your Python pipelines
│   ├── gsheet_pipeline.py    # Your pipeline script (we'll create this)
│   └── ...                   # dltHub scaffoldings (auto-generated)
└── models/                   # Your existing dbt models

Part 2: Set Up Google Sheets API Access

Why do we need this?

To read data from Google Sheets programmatically, you need API credentials from Google Cloud Platform.

Steps:

Go to Google Cloud Console
- Visit console.cloud.google.com
- Sign in with your Google account
Create a Service Account (if you don’t have one)
- In the left menu, go to IAM & Admin → Service Accounts
- Click + CREATE SERVICE ACCOUNT
- Give it a name like “paradime-sheets-reader”
- Click CREATE AND CONTINUE
- Skip the optional steps and click DONE
Enable Google Sheets API
- In the search bar at the top, type “Google Sheets API”
- Click on it and press ENABLE
Create API Credentials
- Go back to IAM & Admin → Service Accounts
- Find your service account and click the three dots (⋮) under Actions
- Select Manage Keys
- Click ADD KEY → Create new key
- Choose JSON format
- Click CREATE - a JSON file will download automatically
Share Your Google Sheet
- Open the JSON file you just downloaded
- Find the client_email field (looks like: your-service@project.iam.gserviceaccount.com)
- Copy this email address
- Go to your Google Sheet and click Share
- Paste the service account email and give it Viewer access

Extract Credentials from JSON Open the downloaded JSON file. You’ll need these four values:

{
  "project_id": "your-project-12345",
  "private_key": "-----BEGIN PRIVATE KEY-----\nMIIEvQIBA...",
  "client_email": "your-service@project.iam.gserviceaccount.com",
  "token_uri": "https://oauth2.googleapis.com/token"
}

Encode the Private Key The private key needs to be encoded in Base64 format. On Mac/Linux:
echo -n 'YOUR_PRIVATE_KEY_HERE' | base64

On Windows (PowerShell):

[Convert]::ToBase64String([Text.Encoding]::UTF8.GetBytes('YOUR_PRIVATE_KEY_HERE'))

Save this encoded value - you’ll use it as B64_BIGQUERY_PRIVATE_KEY.

Part 3: Prepare Snowflake Credentials

Why RSA Key Authentication?

For security, we use key-pair authentication instead of passwords for automated pipelines.

Steps:

Generate RSA Key Pair (if you don’t have one)

# Generate encrypted private key
openssl genrsa 2048 | openssl pkcs8 -topk8 -v2 des3 -inform PEM -out rsa_key.p8

# Generate public key
openssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub

Register Public Key in Snowflake

-- Connect to Snowflake and run:
ALTER USER your_username SET RSA_PUBLIC_KEY='MIIBIjANBgkqhki...';

To get the key value, open rsa_key.pub and copy everything between the header/footer lines. Format Private Key for Paradime Open rsa_key.p8 in a text editor: Original:

-----BEGIN ENCRYPTED PRIVATE KEY-----
MIIFHDBOBgkqhkiG9w0BAQEFAAOCAg8AMIICCgKCAgEA
zdLQw8fH9QxPxHFvqKVhH3zqxKgHHGxXrKJH8fH9QxPx
...more lines...
qwertyuiop
-----END ENCRYPTED PRIVATE KEY-----

Formatted (remove headers and join into one line):

MIIFHDBOBgkqhkiG9w0BAQEFAAOCAg8AMIICCgKCAgEAzdLQw8fH9QxPxHFvqKVhH3zqxKgHHGxXrKJH8fH9QxPx...qwertyuiop

Gather Snowflake Connection Details You’ll need:

Account identifier: Found in your Snowflake URL (e.g., xy12345.us-east-1)
Database name: The database where data will be loaded
Warehouse name: The compute warehouse to use
Role: Your Snowflake role (e.g., ACCOUNTADMIN, SYSADMIN)
Username: Your Snowflake username
Passphrase: [optional] if set when generating the private key

Part 4: Configure Environment Variables in Paradime

Environment variables are secure ways to store credentials without hardcoding them in your scripts.

Where to Set Them:

You need to set these in TWO places in Paradime:

Code IDE (for development)
- Click Settings → Environment Variables
- Scroll to ” Code IDE”
Bolt Scheduler (for production runs)
- Click Settings → Environment Variables
- Scroll to ” Bolt”

Variables to Set:

Google Sheets Credentials

Variable Name	Value	Example
`SOURCES__GOOGLE_SHEETS__CREDENTIALS__PROJECT_ID`	From JSON: `project_id`	`my-project-12345`
`SOURCES__GOOGLE_SHEETS__CREDENTIALS__CLIENT_EMAIL`	From JSON: `client_email`	`service@project.iam.gserviceaccount.com`
`SOURCES__GOOGLE_SHEETS__CREDENTIALS__TOKEN_URI`	From JSON: `token_uri`	`https://oauth2.googleapis.com/token`
`B64_BIGQUERY_PRIVATE_KEY`	Base64-encoded private key from Step 2.7	`LS0tLS1CRUdJTi...`

Snowflake Credentials

Variable Name	Value	Example
`DESTINATION__SNOWFLAKE__CREDENTIALS__HOST`	Snowflake account identifier	`xy12345.us-east-1`
`DESTINATION__SNOWFLAKE__CREDENTIALS__DATABASE`	Target database	`ANALYTICS`
`DESTINATION__SNOWFLAKE__CREDENTIALS__WAREHOUSE`	Warehouse name	`COMPUTE_WH`
`DESTINATION__SNOWFLAKE__CREDENTIALS__ROLE`	Your role	`TRANSFORMER`
`DESTINATION__SNOWFLAKE__CREDENTIALS__USERNAME`	Your username	`john.doe@company.com`
`DESTINATION__SNOWFLAKE__CREDENTIALS__PRIVATE_KEY`	Formatted private key (from Step 3.3)	`MIIFHDBOBgkq...`
`DESTINATION__SNOWFLAKE__CREDENTIALS__PRIVATE_KEY_PASSPHRASE`	Passphrase you created	`MySecurePass123!`

Development Variable

Variable Name	Value	Example
`DEV_SCHEMA_PREFIX`	Your initials or identifier	`JD`

Note: Only set DEV_SCHEMA_PREFIX in Code IDE settings, NOT in Bolt. This ensures development data is isolated from production.

Part 5: Initialize Your Python Project

Now we’ll set up the project structure and dependencies.

Step 5.1: Create `pyproject.toml`

In Paradime’s Code IDE, at the root level (same folder as dbt_project.yml), create a new file called pyproject.toml:

[tool.poetry]
name = "python-pipelines"
version = "0.1.0"
description = "Data pipelines using dltHub"
authors = ["Your Name <your.email@company.com>"]
readme = "README.md"
package-mode = false

[tool.poetry.dependencies]
python = ">=3.11,<3.13"
google-api-python-client = "^2.118.0"
dlt = {extras = ["snowflake"], version = "1.5.0"}

[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"

What this does:

Poetry is a Python dependency manager (like npm for JavaScript)
We’re specifying we need Python 3.11 or 3.12
We’re installing dlt with Snowflake support and Google API client

Step 5.2: Install Dependencies

Open the terminal in Paradime’s Code IDE and run:

poetry install

This will:

Create a virtual environment
Install dltHub and all required packages
Generate a poetry.lock file (commit this to git!)

Step 5.3: Initialize dltHub

Create the python_dlt folder and initialize the Google Sheets pipeline:

mkdir python_dlt
cd python_dlt
dlt init google_sheets snowflake

What this command does:

Creates .dlt/ folder with configuration files
Downloads the Google Sheets source code
Creates example pipeline script
Adds a .gitignore file

Note: We’re using environment variables instead of .dlt/secrets.toml for security, so you can ignore that file.

Part 6: Create Your Pipeline Script

Replace the example google_sheets_pipeline.py or create a new file called gsheet_pipeline.py:

import os
import dlt
import base64
from google_sheets import google_spreadsheet
from typing import List, Sequence

def setup_credentials():
    """
    Decode and set up Google Sheets API credentials.
    
    This function retrieves the base64-encoded private key from environment
    variables, decodes it, and sets it in the format dltHub expects.
    """
    b64_private_key = os.getenv("B64_BIGQUERY_PRIVATE_KEY")
    if not b64_private_key:
        raise ValueError("Missing B64_BIGQUERY_PRIVATE_KEY environment variable")

    try:
        # Decode from base64
        private_key = base64.b64decode(b64_private_key).decode('utf-8')
        # Replace escaped newlines with actual newlines
        private_key = private_key.replace('\\n', '\n').strip()
        # Set for dltHub to use
        os.environ["SOURCES__GOOGLE_SHEETS__CREDENTIALS__PRIVATE_KEY"] = private_key
    except Exception as e:
        raise ValueError(f"Failed to decode private key: {str(e)}")

def get_dataset_name() -> str:
    """
    Determine the Snowflake schema name based on the environment.
    
    Production (scheduled runs): Uses GOOGLE_SHEETS_LOAD
    Development (manual runs): Uses {DEV_SCHEMA_PREFIX}_GOOGLE_SHEETS_LOAD
    
    Returns:
        str: The schema/dataset name to use
    
    Raises:
        ValueError: If neither environment variable is set
    """
    # Paradime automatically sets this when running scheduled jobs
    schedule_run_id = os.getenv("PARADIME_SCHEDULE_RUN_ID")
    # You manually set this in Code IDE settings
    dev_prefix = os.getenv("DEV_SCHEMA_PREFIX")
    
    if schedule_run_id:
        # We're in a scheduled production run
        return "GOOGLE_SHEETS_LOAD"
    elif dev_prefix:
        # We're in development mode
        return f"{dev_prefix}_GOOGLE_SHEETS_LOAD"
    else:
        raise ValueError(
            "Must set either PARADIME_SCHEDULE_RUN_ID (for production) "
            "or DEV_SCHEMA_PREFIX (for development) environment variable"
        )

def load_pipeline(spreadsheet_url_or_id: str, range_names: Sequence[str]) -> None:
    """
    Extract data from Google Sheets and load into Snowflake.
    
    Args:
        spreadsheet_url_or_id: The Google Sheet ID or full URL
                               Example: "1U3NQQrMgodDV8t-OPrHpf9wVgrXU2HKbrJK2effbUjA"
        range_names: List of sheet names or ranges to extract
                    Example: ["Sheet1", "Sheet2!A1:B10"]
    """
    # Set up Google credentials
    setup_credentials()
    
    # Determine which schema to use
    dataset_name = get_dataset_name()
    
    # Create the dltHub pipeline
    pipeline = dlt.pipeline(
        pipeline_name="google_sheets_pipeline",  # Identifier for this pipeline
        destination='snowflake',                 # Where to load data
        dev_mode=False,                          # Disable dev mode (use for testing)
        dataset_name=dataset_name                # Target schema name
    )
    
    # Configure what to extract from Google Sheets
    data = google_spreadsheet(
        spreadsheet_url_or_id=spreadsheet_url_or_id,
        range_names=range_names,           # Only load these specific sheets/ranges
        get_sheets=False,                  # Don't automatically load all sheets
        get_named_ranges=False             # Don't load named ranges
    )
    
    # Run the pipeline
    print(f"Loading data into dataset: {dataset_name}")
    info = pipeline.run(data)
    print(info)

if __name__ == "__main__":
    # CONFIGURE YOUR GOOGLE SHEET HERE
    
    # Option 1: Use the full URL
    spreadsheet_url_or_id = "https://docs.google.com/spreadsheets/d/1U3NQQrMgodDV8t-OPrHpf9wVgrXU2HKbrJK2effbUjA/edit"
    
    # Option 2: Or just the sheet ID (the long string in the URL)
    # spreadsheet_url_or_id = "1U3NQQrMgodDV8t-OPrHpf9wVgrXU2HKbrJK2effbUjA"
    
    # Specify which sheets or ranges to load
    range_names = ["WorldCupMatches"]  # Sheet name(s)
    # range_names = ["Sheet1!A1:D100"]  # Or specific range(s)
    # range_names = ["Sheet1", "Sheet2", "DataSheet!A:Z"]  # Multiple sheets
    
    # Run the pipeline
    load_pipeline(spreadsheet_url_or_id=spreadsheet_url_or_id, range_names=range_names)

Understanding the Code

Function Breakdown:

setup_credentials(): Handles the Google API authentication
- Retrieves the encoded private key
- Decodes it from Base64
- Sets it in the format dltHub expects
get_dataset_name(): Smart schema naming
- In development: Creates {YOUR_INITIALS}_GOOGLE_SHEETS_LOAD
- In production: Creates GOOGLE_SHEETS_LOAD
- Prevents dev data from mixing with prod data
load_pipeline(): The main extraction and loading logic
- Sets up credentials
- Creates a dltHub pipeline object
- Configures the Google Sheets source
- Runs the extraction and loading
if __name__ == "__main__": Configuration section
- This is where YOU specify which Google Sheet to load
- And which specific sheets/ranges to extract

Part 7: Run Your First Pipeline

Development Run (Testing)

Open Paradime’s Code IDE terminal
Navigate to the pipeline folder:
cd python_dlt
Run the pipeline using Poetry:
poetry run python gsheet_pipeline.py

What happens:

The script executes in the Poetry virtual environment
Data is extracted from your Google Sheet
Tables are created in Snowflake under {YOUR_PREFIX}_GOOGLE_SHEETS_LOAD schema
You’ll see progress logs in the terminal

Expected Output:

Loading data into dataset: JD_GOOGLE_SHEETS_LOAD
Pipeline google_sheets_pipeline load step completed in X.XX seconds
1 load package(s) were loaded

Production Run (Scheduled)

In Paradime, go to Bolt → Schedules
Create a new schedule: name: “Load Google Sheets Data”
Add Commands:

# Install dependencies with Poetry
poetry install

# Run your Python script
poetry run python python_dlt/gsheet_pipeline.py

Schedule: Choose frequency (e.g., “Daily at 6 AM”)
Save and enable the schedule

What happens:

Paradime automatically sets PARADIME_SCHEDULE_RUN_ID
Data loads into GOOGLE_SHEETS_LOAD schema (without your prefix)
Your dbt models can now reference this data
Runs automatically on your chosen schedule

Part 8: Using the Loaded Data in dbt

Now that data is in Snowflake, you can create dbt models to transform it:

-- models/staging/stg_world_cup_matches.sql

with source as (
    select * from {{ source('google_sheets', 'worldcupmatches') }}
),

renamed as (
    select
        year,
        datetime,
        stage,
        stadium,
        city,
        home_team_name,
        home_team_goals,
        away_team_goals,
        away_team_name,
        win_conditions,
        attendance,
        half_time_home_goals,
        half_time_away_goals,
        referee,
        assistant_1,
        assistant_2,
        _dlt_load_id,
        _dlt_id
    from source
)

select * from renamed

Define the source in sources.yml:

version: 2

sources:
  - name: google_sheets
    schema: google_sheets_load  # Production schema
    tables:
      - name: worldcupmatches
        description: "Raw World Cup match data from Google Sheets"

Troubleshooting Common Issues

'Missing B64_BIGQUERY_PRIVATE_KEY environment variable'

Cause: Environment variable not set or not accessibleFix:

Double-check spelling in Paradime settings
Ensure you saved the environment variable
Restart the Code IDE to pick up new variables

'Authentication failed' for Google Sheets

Cause: Service account doesn’t have access to the sheetFix:

Open your Google Sheet
Click “Share”
Add the service account email (from client_email in JSON)
Give at least “Viewer” permission

'Unable to connect to Snowflake'

Cause: Incorrect Snowflake credentials or network issuesFix:

Verify all Snowflake environment variables are set correctly
Test the private key format (no headers, single line)
Check your Snowflake role has CREATE SCHEMA permissions
Verify the warehouse is running

Tables created but empty

Cause: Wrong sheet name or range specifiedFix:

Double-check the sheet name spelling (case-sensitive)
Verify the sheet exists in the Google Sheet
Try using just the sheet name without range specifiers

'Command not found: poetry'

Cause: Poetry not installed in the environmentFix:

pip install poetry

Best Practices

1. Schema Organization

Development:

Use personal prefixes (JD_, SARAH_, etc.)
Prevents conflicts when multiple developers test
Easy to identify and clean up dev data

Production:

Use clean, standard names (GOOGLE_SHEETS_LOAD)
Apply proper access controls
Document schema purposes

2. Pipeline Configuration

Keep it flexible:

# Good - easy to modify
SPREADSHEET_ID = "1U3NQQ..."
SHEET_NAMES = ["Sales2024", "Sales2023"]

# Then use in function call
load_pipeline(SPREADSHEET_ID, SHEET_NAMES)

Version control your changes:

Commit pyproject.toml and poetry.lock
Track pipeline scripts in git
Document any configuration changes

3. Error Handling

Add try-except blocks for robustness:

try:
    load_pipeline(spreadsheet_url_or_id, range_names)
except Exception as e:
    print(f"Pipeline failed: {str(e)}")
    # Send alert, log to monitoring system, etc.
    raise

4. Monitoring

Things to track:

Pipeline run duration
Number of rows loaded
Data freshness in Snowflake
Failed run alerts

Example logging:

import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

logger.info(f"Starting pipeline for {spreadsheet_url_or_id}")
info = pipeline.run(data)
logger.info(f"Loaded {info.dataset_name} successfully")

5. Incremental Loading

For large sheets, configure incremental loading to only extract new data:

# In your pipeline configuration
data = google_spreadsheet(
    spreadsheet_url_or_id=spreadsheet_url_or_id,
    range_names=range_names,
    get_sheets=False
)

# Add incremental configuration
data = data.add_incremental(
    "timestamp_column",  # Column to use for incrementality
    primary_key="id"     # Unique identifier column
)

Next Steps

Now that you have a working pipeline:

Add More Data Sources
- Check dltHub documentation for other verified sources
- Initialize additional pipelines: dlt init [source] snowflake
Build Transformations
- Create dbt™. models that use your loaded data
- Add tests and documentation
- Build downstream analytics models
Automate Everything
- Schedule your pipeline in Bolt
- Set up dbt™ to run after data loads
- Create alerts for pipeline failures
Explore Advanced Features
- Incremental loading strategies
- Schema evolution handling
- Custom data transformations in Python
- Multiple destination support

Additional Resources

dltHub Documentation: dlthub.com/docs
dltHub Verified Sources: dlthub.com/docs/verified-sources
Google Sheets API: developers.google.com/sheets
Poetry Documentation: python-poetry.org/docs

​Prerequisites

​Part 1: Understanding the Project Structure

​Part 2: Set Up Google Sheets API Access

​Why do we need this?

​Steps:

​Part 3: Prepare Snowflake Credentials

​Why RSA Key Authentication?

​Steps:

​Part 4: Configure Environment Variables in Paradime

​Where to Set Them:

​Variables to Set:

​Part 5: Initialize Your Python Project

​Step 5.1: Create pyproject.toml

​Step 5.2: Install Dependencies

​Step 5.3: Initialize dltHub

​Part 6: Create Your Pipeline Script

​Understanding the Code

​Part 7: Run Your First Pipeline

​Development Run (Testing)

​Production Run (Scheduled)

​Part 8: Using the Loaded Data in dbt

​Troubleshooting Common Issues

​Best Practices

​1. Schema Organization

​2. Pipeline Configuration

​3. Error Handling

​4. Monitoring

​5. Incremental Loading

​Next Steps

​Additional Resources

Prerequisites

Part 1: Understanding the Project Structure

Part 2: Set Up Google Sheets API Access

Why do we need this?

Steps:

Part 3: Prepare Snowflake Credentials

Why RSA Key Authentication?

Steps:

Part 4: Configure Environment Variables in Paradime

Where to Set Them:

Variables to Set:

Part 5: Initialize Your Python Project

Step 5.1: Create `pyproject.toml`

Step 5.2: Install Dependencies

Step 5.3: Initialize dltHub

Part 6: Create Your Pipeline Script

Understanding the Code

Part 7: Run Your First Pipeline

Development Run (Testing)

Production Run (Scheduled)

Part 8: Using the Loaded Data in dbt

Troubleshooting Common Issues

Best Practices

1. Schema Organization

2. Pipeline Configuration

3. Error Handling

4. Monitoring

5. Incremental Loading

Next Steps

Additional Resources