Python Data Loading from Google Analytics to Azure with dlt Library
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This guide provides instructions on using the open-source Python library dlt
to load data from Google Analytics
into Azure Cloud Storage
. Google Analytics
is a service that gathers data from your websites and apps, generating reports that offer valuable insights into your business. Azure Cloud Storage
is a filesystem destination that stores data on Microsoft Azure, facilitating the creation of data lakes. The data can be uploaded in several formats, including JSONL, Parquet, or CSV. For more details on Google Analytics
, visit https://analytics.google.com.
dlt
Key Features
- Google Analytics: Google Analytics is a verified source in
dlt
that loads data using the Google Analytics API to the destination of your choice. It offers resources like basic Analytics info, metrics table, and dimensions table. - Governance Support in
dlt
Pipelines:dlt
pipelines provide robust governance support through mechanisms like pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read more about it here. - Google Storage:
dlt
supports Google Storage. You can install thegcfs
package withpip install dlt[gs]
and edit thedlt
credentials file with your secret info. More about this here. - Setup Guide:
dlt
provides a detailed setup guide for accessing various bucket types, including AWS S3, Google Cloud Storage, Azure Blob Storage, and Local Storage. You can find the guide here. - Advanced Usage:
dlt
allows advanced usage with branches, local folders, or git repos. You can deploy from a branch of the verified-sources repo or from another repo. More about this here.
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 Azure Cloud Storage
:
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 Azure Cloud Storage
. You can run the following commands to create a starting point for loading data from Google Analytics
to Azure Cloud Storage
:
# 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
The default filesystem destination is configured to connect to AWS S3. To load to Azure Cloud Storage, update the [destination.filesystem.credentials]
section in your secrets.toml
.
[destination.filesystem.credentials]
azure_storage_account_name="Please set me up!"
azure_storage_account_key="Please set me up!"
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 Azure Cloud Storage
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 pipeline using GitHub Actions. Follow the step-by-step guide to set up and schedule your pipeline. Read more
Deploy with Airflow and Google Composer: Discover how to deploy a pipeline using Airflow and Google Composer. This guide provides detailed instructions on setting up your environment and running your pipeline. Read more
Deploy with Google Cloud Functions: Explore how to deploy your pipeline using Google Cloud Functions. This tutorial covers the necessary steps to get your pipeline running on Google Cloud's serverless environment. Read more
Other Deployment Options: Check out additional methods for deploying your pipeline, including various cloud services and CI/CD tools. Read more
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 operation and timely detection of issues. How to Monitor your pipeline - Set up alerts: Set up alerts to get notified about any issues or important events in your
dlt
pipeline. This helps in proactive management and quick resolution of problems. Set up alerts - Set up tracing: Implement tracing to get detailed insights into the execution of your
dlt
pipeline. Tracing helps in identifying bottlenecks and understanding the flow of data through the pipeline. And set up tracing
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