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Python Data Loading from google analytics to aws s3 with dlt Library

Connecting other file destinations

This document describes how to set up loading to aws 3, but our filesystem source can not only load to s3, but also to Google Cloud Storage, Google Drive, Azure, or local filesystem. Learn more about this here.

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dlt is an open-source Python library used for data loading tasks. This technical documentation will guide you on how to use dlt to load data from Google Analytics, a platform that gathers data from your websites and apps to generate insightful reports about your business. The loaded data will be stored in AWS S3, a remote filesystem destination that uses fsspec for file operations. AWS S3 is primarily used as a staging area for other destinations, but it can also be utilized to quickly build a data lake. For more information on Google Analytics, visit https://analytics.google.com.

dlt Key Features​

  • Google Analytics: dlt verified Google Analytics source provides a way to load data using the Google Analytics API to the destination of your choice. It supports loading basic Analytics info to the pipeline and assembles and presents data relevant to the report's metrics and dimensions.

  • Governance Support in dlt Pipelines: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. These features contribute to better data management practices, compliance adherence, and overall data governance.

  • Configuration Options: dlt provides configuration options to scale up and fine-tune pipelines. It offers mechanisms to run extraction, normalization, and load in parallel. It also allows you to fine-tune the memory buffers, intermediary file sizes, and compression options.

  • Using dlt init with branches, local folders or git repos: dlt allows deployments from a branch of the verified-sources repo or another repo. You can also fork the verified-sources repo and provide the new repo URL.

  • Data Extraction with dlt: dlt facilitates easy data extraction by allowing you to decorate your data-producing functions with loading or incremental extraction metadata. It offers scalability through iterators, chunking, parallelization and utilizes implicit extraction DAGs for efficient API calls for data enrichments or transformations.

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 AWS S3:

pip install "dlt[filesystem]"

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 AWS S3. You can run the following commands to create a starting point for loading data from Google Analytics to AWS S3:

# create a new directory
mkdir my-google_analytics-pipeline
cd my-google_analytics-pipeline
# initialize a new pipeline with your source and destination
dlt init google_analytics filesystem
# 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[filesystem]>=0.3.25

You now have the following folder structure in your project:

my-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:

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!

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.filesystem]
bucket_url = "bucket_url" # please set me up!

[destination.filesystem.credentials]
aws_access_key_id = "aws_access_key_id" # please set me up!
aws_secret_access_key = "aws_secret_access_key" # please set me up!
Further help setting up your source and destinations

Please consult the detailed setup instructions for the AWS S3 destination in the dlt destinations documentation.

Likewise you can find the setup instructions for Google Analytics source in the dlt verifed sources documentation.

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='filesystem',
        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='filesystem',
        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='filesystem',
        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 AWS S3 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 is a CI/CD runner that you can use for free.
  • Deploy with Airflow: dlt provides a simple way to deploy your pipeline using Airflow. It generates an Airflow DAG for your pipeline script that you can customize.
  • Deploy with Google Cloud Functions: You can also deploy dlt using Google Cloud Functions. This serverless execution environment allows you to build and connect cloud services with code.
  • Other Deployment Methods: There are several other methods to deploy dlt. You can find more information about them here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides a comprehensive monitoring solution that allows you to keep track of your pipeline's performance and health. You can inspect the load information, trace runtime, and check schema changes. Learn more about it here.
  • Set Up Alerts: With dlt, you can set up alerts to notify you of any issues or changes in your pipeline. This can help you respond to problems promptly and keep your pipeline running smoothly. Find out how to set up alerts here.
  • Enable Tracing: dlt also offers tracing capabilities that provide detailed information about the execution of your pipeline. This can be invaluable 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!

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