Skip to main content

Loading Data from google analytics to snowflake using dlt in Python

Need help deploying these pipelines, or figuring out how to run them in your data stack?

Join our Slack community or book a call with our support engineer Violetta.

This page provides technical documentation on how to load data from google analytics, a platform that gathers data from your websites and apps to generate insightful reports for your business, to snowflake, a cloud-based data warehousing platform designed for storing, processing, and analyzing large volumes of data. The process leverages an open source Python library called dlt. For more information about the source, visit Google Analytics.

dlt Key Features​

  • Google Analytics: A dlt verified source that loads data using the “Google Analytics API” to the destination of your choice. It supports loading basic Analytics info, data relevant to the report's metrics, and data related to the report's dimensions. Read more
  • Snowflake Destination: Snowflake is a supported destination for dlt pipelines. It offers three types of authentication: password authentication, key pair authentication, and external authentication. Read more
  • Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read more
  • Data Extraction: dlt simplifies data extraction by leveraging iterators, chunking, and parallelization techniques. It also utilizes implicit extraction DAGs for efficient API calls for data enrichments or transformations. Read more
  • Matomo: A dlt verified source that loads data using the “Matomo API” to the destination of your choice. It supports loading detailed analytics summaries of website traffic, visitor behavior, and more. Read more

Getting started with your pipeline locally​

0. Prerequisites​

dlt requires Python 3.8 or higher. Additionally, you need to have the pip package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference.

1. Install dlt​

First you need to install the dlt library with the correct extras for Snowflake:

pip install "dlt[snowflake]"

The dlt cli has a useful command to get you started with any combination of source and destination. For this example, we want to load data from Google Analytics to Snowflake. You can run the following commands to create a starting point for loading data from Google Analytics to Snowflake:

# create a new directory
mkdir google_analytics_pipeline
cd google_analytics_pipeline
# initialize a new pipeline with your source and destination
dlt init google_analytics snowflake
# install the required dependencies
pip install -r requirements.txt

The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt:


google-analytics-data
google-api-python-client
google-auth-oauthlib
requests_oauthlib
dlt[snowflake]>=0.3.25

You now have the following folder structure in your project:

google_analytics_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── google_analytics/                   # folder with source specific files
│   └── ...
├── google_analytics_pipeline.py        # your main pipeline script
├── requirements.txt         # dependencies for your pipeline
└── .gitignore               # ignore files for git (not required)

2. Configuring your source and destination credentials​

The dlt cli will have created a .dlt directory in your project folder. This directory contains a config.toml file and a secrets.toml file that you can use to configure your pipeline. The automatically created version of these files look like this:

generated config.toml​

# put your configuration values here

[runtime]
log_level="WARNING"  # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see https://dlthub.com/docs/telemetry
dlthub_telemetry = true

[sources.google_analytics]
property_id = 0 # please set me up!
queries = 
["a", "b", "c"] # please set me up!

generated secrets.toml​

# put your secret values and credentials here. do not share this file and do not push it to github

[sources.google_analytics.credentials]
client_id = "client_id" # please set me up!
client_secret = "client_secret" # please set me up!
refresh_token = "refresh_token" # please set me up!
project_id = "project_id" # please set me up!

[destination.snowflake.credentials]
database = "database" # please set me up!
password = "password" # please set me up!
username = "username" # please set me up!
host = "host" # please set me up!
warehouse = "warehouse" # please set me up!
role = "role" # please set me up!

2.1. Adjust the generated code to your usecase​

Further help setting up your source and destinations
  • Read more about setting up the Google Analytics source in our docs.
  • Read more about setting up the Snowflake destination in our docs.

3. Running your pipeline for the first time​

The dlt cli has also created a main pipeline script for you at google_analytics_pipeline.py, as well as a folder google_analytics that contains additional python files for your source. These files are your local copies which you can modify to fit your needs. In some cases you may find that you only need to do small changes to your pipelines or add some configurations, in other cases these files can serve as a working starting point for your code, but will need to be adjusted to do what you need them to do.

The main pipeline script will look something like this:


""" Loads the pipeline for Google Analytics V4. """

import time
from typing import Any

import dlt

from google_analytics import google_analytics

# this can also be filled in config.toml and be left empty as a parameter.
QUERIES = [
    {
        "resource_name": "sample_analytics_data1",
        "dimensions": ["browser", "city"],
        "metrics": ["totalUsers", "transactions"],
    },
    {
        "resource_name": "sample_analytics_data2",
        "dimensions": ["browser", "city", "dateHour"],
        "metrics": ["totalUsers"],
    },
]


def simple_load() -> Any:
    """
    Just loads the data normally. Incremental loading for this pipeline is on,
    the last load time is saved in dlt_state, and the next load of the pipeline will have the last load as a starting date.

    Returns:
        Load info on the pipeline that has been run.
    """
    # FULL PIPELINE RUN
    pipeline = dlt.pipeline(
        pipeline_name="dlt_google_analytics_pipeline",
        destination='snowflake',
        full_refresh=False,
        dataset_name="sample_analytics_data",
    )
    # Google Analytics source function - taking data from QUERIES defined locally instead of config
    # TODO: pass your google analytics property id as google_analytics(property_id=123,..)
    data_analytics = google_analytics(queries=QUERIES)
    info = pipeline.run(data=data_analytics)
    print(info)
    return info


def simple_load_config() -> Any:
    """
    Just loads the data normally. QUERIES are taken from config. Incremental loading for this pipeline is on,
    the last load time is saved in dlt_state, and the next load of the pipeline will have the last load as a starting date.

    Returns:
        Load info on the pipeline that has been run.
    """
    # FULL PIPELINE RUN
    pipeline = dlt.pipeline(
        pipeline_name="dlt_google_analytics_pipeline",
        destination='snowflake',
        full_refresh=False,
        dataset_name="sample_analytics_data",
    )
    # Google Analytics source function - taking data from QUERIES defined locally instead of config
    data_analytics = google_analytics()
    info = pipeline.run(data=data_analytics)
    print(info)
    return info


def chose_date_first_load(start_date: str = "2000-01-01") -> Any:
    """
    Chooses the starting date for the first pipeline load. Subsequent loads of the pipeline will be from the last loaded date.

    Args:
        start_date: The string version of the date in the format yyyy-mm-dd and some other values.
            More info: https://developers.google.com/analytics/devguides/reporting/data/v1/rest/v1beta/DateRange

    Returns:
        Load info on the pipeline that has been run.
    """
    # FULL PIPELINE RUN
    pipeline = dlt.pipeline(
        pipeline_name="dlt_google_analytics_pipeline",
        destination='snowflake',
        full_refresh=False,
        dataset_name="sample_analytics_data",
    )
    # Google Analytics source function
    data_analytics = google_analytics(start_date=start_date)
    info = pipeline.run(data=data_analytics)
    print(info)
    return info


if __name__ == "__main__":
    start_time = time.time()
    simple_load()
    end_time = time.time()
    print(f"Time taken: {end_time-start_time}")

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:

python google_analytics_pipeline.py

4. Inspecting your load result​

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline dlt_google_analytics_pipeline info

You can also use streamlit to inspect the contents of your Snowflake destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline dlt_google_analytics_pipeline show

5. Next steps to get your pipeline running in production​

One of the beauties of dlt is, that we are just a plain Python library, so you can run your pipeline in any environment that supports Python >= 3.8. We have a couple of helpers and guides in our docs to get you there:

The Deploy section will show you how to deploy your pipeline to​

  • Deploy with Github Actions: dlt can be easily deployed using Github Actions. This allows you to automate your workflows and set up continuous integration and continuous deployment pipelines.
  • Deploy with Airflow: You can deploy your dlt pipelines using Airflow. This allows you to schedule and monitor workflows and provides a rich user interface for pipeline management.
  • Deploy with Google Cloud Functions: dlt supports deployment with Google Cloud Functions. This allows you to run your pipelines in a fully managed environment on Google Cloud.
  • And more: There are many other ways to deploy your dlt pipelines. Check out the deployment guide for more information.

The running in production section will teach you about:​

  • Monitor your pipeline: dlt provides detailed monitoring capabilities to keep track of your pipeline's performance and data loads. You can inspect the load info, trace runtime, and even save schema changes. Learn more about it here.
  • Set up alerts: With dlt, you can set up alerts to be notified of any issues or changes in your pipeline. This allows you to respond quickly to any potential problems and ensure your pipeline runs smoothly. Find out how to set up alerts here.
  • Set up tracing: Tracing helps you understand the flow of data through your pipeline. It provides information about the extract, normalize and load steps, and can be saved to the destination. This can be a valuable tool for debugging and optimizing your pipeline. Learn how to set up tracing here.

Additional pipeline guides​

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!

DHelp

Ask a question

Welcome to "Codex Central", your next-gen help center, driven by OpenAI's GPT-4 model. It's more than just a forum or a FAQ hub – it's a dynamic knowledge base where coders can find AI-assisted solutions to their pressing problems. With GPT-4's powerful comprehension and predictive abilities, Codex Central provides instantaneous issue resolution, insightful debugging, and personalized guidance. Get your code running smoothly with the unparalleled support at Codex Central - coding help reimagined with AI prowess.