Loading Data from Google Analytics
to EDB BigAnimal
with dlt
in Python
We will be using the dlt PostgreSQL destination to connect to EDB BigAnimal. You can get the connection string for your EDB BigAnimal database as described in the EDB BigAnimal Docs.
Join our Slack community or book a call with our support engineer Violetta.
Google Analytics
is a platform that collects data from your websites and apps to create reports that provide insights into your business. EDB BigAnimal
is a fully managed database-as-a-service that runs in your cloud account or BigAnimal
's cloud account, operated by one of the builders of Postgres
. It simplifies setting up, managing, and scaling databases. You can use PostgreSQL
or EDB Postgres Advanced Server
with Oracle compatibility, or opt for distributed high-availability cluster types for geographically distributed databases. This documentation explains how to load data from Google Analytics
to EDB BigAnimal
using the open-source Python library called dlt
. Further information about Google Analytics
can be found here.
dlt
Key Features
- Scalability via iterators, chunking, and parallelization:
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. Read more - Implicit extraction DAGs:
dlt
incorporates the concept of implicit extraction DAGs to handle the dependencies between data sources and their transformations automatically. Read more - Pipeline Metadata:
dlt
pipelines leverage metadata to provide governance capabilities. This metadata includes load IDs, which consist of a timestamp and pipeline name. Read 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. Read more - Schema evolution:
dlt
enables proactive governance by alerting users to schema changes. When modifications occur in the source data’s schema, such as table or column alterations,dlt
notifies stakeholders. Read 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 EDB BigAnimal
:
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 EDB BigAnimal
. You can run the following commands to create a starting point for loading data from Google Analytics
to EDB BigAnimal
:
# 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 EDB BigAnimal
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 for a CI/CD workflow. Read more - Deploy with Airflow and Google Composer: Follow this guide to deploy a
dlt
pipeline using Airflow and Google Composer. Read more - Deploy with Google Cloud Functions: Discover how to deploy a
dlt
pipeline using Google Cloud Functions. Read more - Explore other deployment options: Check out additional methods for deploying
dlt
pipelines, including various cloud and orchestration services. 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 operations and quick identification of issues. How to Monitor your pipeline - Set up alerts: Set up alerts to get notified about critical events and issues in your pipeline, ensuring you can respond promptly to any disruptions. Set up alerts
- Set up tracing: Implement tracing in your
dlt
pipeline to gain detailed insights into the execution flow and performance, helping you to diagnose and resolve problems efficiently. And set up tracing
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