Loading Data from Braze
to Azure Synapse
with dlt
in Python
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This documentation provides a guide on loading data from Braze
to Azure Synapse
using the open-source Python library dlt
. Braze, Inc.
is an American cloud-based software company based in New York City, known for its customer engagement platform used by businesses for multichannel marketing. Azure Synapse Analytics
is a limitless analytics service that combines enterprise data warehousing and Big Data analytics. By leveraging dlt
, users can efficiently transfer data from Braze
to Azure Synapse
, ensuring seamless integration and robust data management. For more information about Braze
, visit here.
dlt
Key Features
- Governance Support:
dlt
pipelines provide robust governance through metadata utilization, schema enforcement, and schema change alerts. Learn more - Schema Enforcement and Curation: Ensure data consistency and quality by enforcing and curating schemas. Read more
- Schema Evolution Alerts: Get notified of schema changes to proactively manage data integrity. Find out more
- Scaling and Finetuning: Scale up and fine-tune pipelines with parallel processing and configurable options. Explore more
- Staging Support: Utilize Azure Blob Storage for staging data before loading into Synapse. Read more
Getting started with your pipeline locally
dlt-init-openapi
0. 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.