Loading Data from Google Analytics
to YugabyteDB
with dlt
in Python
We will be using the dlt PostgreSQL destination to connect to YugabyteDB. You can get the connection string for your YugabyteDB database as described in the YugabyteDB 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. This documentation explains how to load data from Google Analytics
into YugabyteDB
using the open-source Python library dlt
. YugabyteDB
is a distributed PostgreSQL database designed for modern applications, offering built-in resilience, seamless scalability, and flexible geo-distribution. By leveraging dlt
, you can efficiently transfer your analytics data to YugabyteDB
, enabling robust data management and analysis. Further information about Google Analytics
can be found at https://analytics.google.com.
dlt
Key Features
- Scalable Data Extraction:
dlt
offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. Learn more - Robust Governance Support:
dlt
pipelines provide governance capabilities through metadata utilization, schema enforcement, and schema change alerts. Read more about governance - Flexible Schema Management:
dlt
empowers users to enforce and curate schemas, ensuring data consistency and quality. Adjust a schema - Automatic Data Normalization: The normalization engine in
dlt
recursively unpacks nested structures into relational tables, creating a schema that evolves with source data changes. Understand normalization - Configurable and Reliable Loading:
dlt
uses idempotent and atomic loads to ensure that data safely ends up in your chosen destination. Howdlt
works
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 YugabyteDB
:
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 YugabyteDB
. You can run the following commands to create a starting point for loading data from Google Analytics
to YugabyteDB
:
# 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
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 YugabyteDB
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: Use GitHub Actions to automate your pipeline deployment. Follow the guide here.
- Deploy with Airflow and Google Composer: Learn how to deploy your pipeline using Airflow and Google Composer by following this guide.
- Deploy with Google Cloud Functions: Use Google Cloud Functions for deploying your pipeline. Detailed instructions can be found here.
- Other Deployment Methods: Explore other methods for deploying your pipeline by visiting the comprehensive guide 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 efficient operations. How to Monitor your pipeline - Set up alerts: Set up alerts to stay informed about your pipeline's performance and potential issues. Set up alerts
- Set up tracing: Implement tracing to gain insights into your pipeline's execution and diagnose problems quickly. And set up tracing
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