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:
dltpipelines 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-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.