Python Data Loading: google analytics
to aws s3
using dlt
Library
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This page provides a guide on how to use the open source Python library, dlt
, to load data from Google Analytics
to AWS S3
. Google Analytics
is a comprehensive platform that gathers data from your websites and applications, generating reports that offer valuable insights into your business operations. On the other hand, AWS S3
is a filesystem destination that allows you to store data and effortlessly create data lakes. This service supports data upload in various formats such as JSONL, Parquet, or CSV. By using dlt
, you can streamline the process of transferring data from Google Analytics
to AWS S3
. For more details on Google Analytics
, you can visit https://analytics.google.com.
dlt
Key Features
Google Analytics Data Access: The Google Analytics verified source provides user engagement data from your website or application using the Google Analytics API.
Multiple Storage Options: The setup guide includes instructions for accessing data from various bucket types, including AWS S3, Google Cloud Storage, and Azure Blob Storage.
Easy Initialization: The
dlt init
command simplifies the process of starting a new data pipeline. More information can be found in the Walkthrough: Add a verified source guide.Flexible Deployment Options:
dlt init
supports deployment from different branches or repos, allowing you to easily customize your data pipeline. This feature is explained in more detail in the Add a verified source walkthrough.Matomo Data Access: The Matomo verified source provides detailed insights into website and application performance using the Matomo API.
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 google_analytics_pipeline
cd 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:
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.filesystem]
dataset_name = "dataset_name" # please set me up!
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!
2.1. Adjust the generated code to your usecase
By default, the filesystem destination will store your files as JSONL
. You can tell your pipeline to choose a different format with the loader_file_format
property that you can set directly on the pipeline or via your config.toml
. Available values are jsonl
, parquet
and csv
:
[pipeline] # in ./dlt/config.toml
loader_file_format="parquet"
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: Learn how to deploy your
dlt
pipeline using GitHub Actions. - Deploy with Airflow and Google Composer: Discover the steps to deploy a pipeline with Airflow and Google Composer.
- Deploy with Google Cloud Functions: Follow this guide to deploy your pipeline using Google Cloud Functions.
- Explore other deployment options: Check out additional methods to deploy your
dlt
pipeline here.
The running in production section will teach you about:
- How to Monitor your pipeline: Learn how to effectively monitor your
dlt
pipeline to ensure smooth and reliable operation in production. How to Monitor your pipeline - Set up alerts: Configure alerts to stay informed about your pipeline's status and catch issues early. Set up alerts
- And set up tracing: Implement tracing to gain deeper insights into your pipeline's performance and troubleshoot issues efficiently. And set up tracing
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