Loading Data from Braze to Timescale Using dlt in Python
We will be using the dlt PostgreSQL destination to connect to Timescale. You can get the connection string for your timescale database as described in the Timescale Docs.
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This page provides technical documentation on loading data from Braze to Timescale using the open-source Python library dlt. Braze, Inc. is a cloud-based software company based in New York City, offering a customer engagement platform for multichannel marketing. Timescale is engineered to handle demanding workloads, including time series, vector, events, and analytics data. Built on PostgreSQL, it offers expert support at no extra charge. This guide will help you utilize dlt to efficiently transfer data from Braze to Timescale. For more information about Braze, visit their website.
dlt Key Features
- Pipeline Metadata:
dltpipelines leverage metadata to provide governance capabilities, including load IDs for incremental transformations and data vaulting. Read more. - Schema Enforcement and Curation: Enforce and curate schemas to ensure data consistency and quality. Read more.
- Scalability via Iterators, Chunking, and Parallelization: Efficiently process large datasets by breaking them down into manageable chunks. Read more.
- Implicit Extraction DAGs: Automatically handle dependencies between data sources and their transformations. Read more.
- Schema Change Alerts: Receive notifications for schema changes, allowing for proactive governance. Read more.
Getting started with your pipeline locally
dlt-init-openapi0. Prerequisites
dlt and dlt-init-openapi requires Python 3.9 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 and dlt-init-openapi
First you need to install the dlt-init-openapi cli tool.
pip install dlt-init-openapi
The dlt-init-openapi cli is a powerful generator which you can use to turn any OpenAPI spec into a dlt source to ingest data from that api. The quality of the generator source is dependent on how well the API is designed and how accurate the OpenAPI spec you are using is. You may need to make tweaks to the generated code, you can learn more about this here.
# generate pipeline
# NOTE: add_limit adds a global limit, you can remove this later
# NOTE: you will need to select which endpoints to render, you
# can just hit Enter and all will be rendered.
dlt-init-openapi braze --url https://raw.githubusercontent.com/dlt-hub/openapi-specs/main/open_api_specs/Business/braze.yaml --global-limit 2
cd braze_pipeline
# install generated requirements
pip install -r requirements.txt
The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt:
dlt>=0.4.12
You now have the following folder structure in your project:
braze_pipeline/
├── .dlt/
│ ├── config.toml # configs for your pipeline
│ └── secrets.toml # secrets for your pipeline
├── rest_api/ # The rest api verified source
│ └── ...
├── braze/
│ └── __init__.py # TODO: possibly tweak this file
├── braze_pipeline.py # your main pipeline script
├── requirements.txt # dependencies for your pipeline
└── .gitignore # ignore files for git (not required)
1.1. Tweak braze/__init__.py
This file contains the generated configuration of your rest_api. You can continue with the next steps and leave it as is, but you might want to come back here and make adjustments if you need your rest_api source set up in a different way. The generated file for the braze source will look like this:
Click to view full file (605 lines)
2. Configuring your source and destination credentials
dlt-init-openapi will try to detect which authentication mechanism (if any) is used by the API in question and add a placeholder in your secrets.toml.
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
[runtime]
log_level="INFO"
[sources.braze]
# Base URL for the API
base_url = "https://rest.iad-01.braze.com"
generated secrets.toml
[sources.braze]
# secrets for your braze source
# example_api_key = "example value"
2.1. Adjust the generated code to your usecase
At this time, the dlt-init-openapi cli tool will always create pipelines that load to a local duckdb instance. Switching to a different destination is trivial, all you need to do is change the destination parameter in braze_pipeline.py to postgres and supply the credentials as outlined in the destination doc linked below.
3. Running your pipeline for the first time
The dlt cli has also created a main pipeline script for you at braze_pipeline.py, as well as a folder braze 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:
import dlt
from braze import braze_source
if __name__ == "__main__":
pipeline = dlt.pipeline(
pipeline_name="braze_pipeline",
destination='duckdb',
dataset_name="braze_data",
progress="log",
export_schema_path="schemas/export"
)
source = braze_source()
info = pipeline.run(source)
print(info)
Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:
python braze_pipeline.py
4. Inspecting your load result
You can now inspect the state of your pipeline with the dlt cli:
dlt pipeline braze_pipeline info
You can also use streamlit to inspect the contents of your Timescale destination for this:
# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline braze_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 pipeline using GitHub Actions, a CI/CD runner that you can use for free. Follow the steps here.
- Deploy with Airflow and Google Composer: Discover how to deploy a pipeline with Airflow, a popular workflow management tool, and Google Composer, a managed Airflow environment. Detailed instructions can be found here.
- Deploy with Google Cloud Functions: Explore how to deploy a pipeline using Google Cloud Functions, a serverless execution environment. Check out the guide here.
- Other Deployment Options: For more ways to deploy your pipeline, including various cloud services and orchestration tools, visit the comprehensive deployment guide here.
The running in production section will teach you about:
- How to Monitor your pipeline: Learn how to effectively monitor your
dltpipeline to ensure it runs smoothly in production. How to Monitor your pipeline - Set up alerts: Set up alerts to get notified of any issues or changes in your
dltpipeline. Set up alerts - And set up tracing: Implement tracing to track the execution and performance of your
dltpipeline. And set up tracing
Available Sources and Resources
For this verified source the following sources and resources are available
Source Braze
Braze source collects user engagement, email status, data summaries, and broadcast scheduling information.
| Resource Name | Write Disposition | Description |
|---|---|---|
info | append | General information about the Braze account and settings. |
hard_bounce | append | Records of emails that were returned to the sender because they couldn't be delivered. |
unsubscribe | append | Information about users who have unsubscribed from email communications. |
data_summary | append | Summary of data metrics related to user engagement and campaign performance. |
scheduled_broadcast | append | Details about scheduled broadcast messages and their statuses. |
statu | append | Status updates and logs related to various operations within Braze. |
detail | append | Detailed logs and information about specific user interactions and events. |
get | append | API calls and responses for retrieving specific data from Braze. |
data_series | append | Time-series data related to user engagement, campaign performance, and other metrics. |
list | append | Lists of users, segments, or other groupings used within Braze for targeted marketing efforts. |
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