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Load Google Analytics Data to Timescale with dlt in Python

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We will be using the dlt PostgreSQL destination to connect to Timescale. You can get the connection string for your timescale database as described in the Timescale Docs.

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Google Analytics is a platform that collects data from your websites and apps to create reports that provide insights into your business. Timescale is engineered to handle demanding workloads, such as time series, vector, events, and analytics data. Built on PostgreSQL, it offers expert support at no extra charge. This documentation explains how to load data from Google Analytics to Timescale using the open-source python library called dlt. For more details about Google Analytics, visit here.

dlt Key Features​

  • Scalable Data Extraction: dlt offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. This approach allows for efficient processing of large datasets by breaking them down into manageable chunks. Learn more
  • Pipeline Metadata: dlt pipelines leverage metadata to provide governance capabilities. This metadata includes load IDs, which consist of a timestamp and pipeline name, enabling incremental transformations and data vaulting. Learn more
  • Schema Enforcement and Curation: dlt empowers users to enforce and curate schemas, ensuring data consistency and quality. Schemas define the structure of normalized data and guide the processing and loading of data. Learn more
  • Implicit Extraction DAGs: dlt incorporates the concept of implicit extraction DAGs to handle the dependencies between data sources and their transformations automatically, ensuring data consistency and integrity. Learn more
  • Detailed Analytics with Matomo: Matomo is a free and open-source web analytics platform that provides detailed insights into website and application performance with features like visitor maps, site search analytics, real-time visitor tracking, and custom reports. Learn 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 Timescale:

pip install "dlt[postgres]"

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 Timescale. You can run the following commands to create a starting point for loading data from Google Analytics to Timescale:

# 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 postgres
# 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[postgres]>=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.postgres]
dataset_name = "dataset_name" # please set me up!

[destination.postgres.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!
port = 5432
connect_timeout = 15

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 Timescale 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='postgres',
        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='postgres',
        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='postgres',
        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 Timescale 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: Learn how to deploy a dlt pipeline using GitHub Actions.
  • Deploy with Airflow and Google Composer: Follow this guide to deploy a dlt pipeline with Airflow and Google Composer.
  • Deploy with Google Cloud Functions: Discover how to set up a dlt pipeline using Google Cloud Functions.
  • Explore other deployment options: Check out various methods to deploy a dlt pipeline in the comprehensive guide.

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline in production to ensure smooth and reliable operation. How to Monitor your pipeline
  • Set up alerts: Set up alerts to notify you of any issues or changes in your dlt pipeline, enabling proactive management and quick response to problems. Set up alerts
  • Set up tracing: Implement tracing to gain detailed insights into the execution of your dlt pipeline, helping you diagnose and troubleshoot issues more efficiently. And set up tracing

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!

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