Orchestrating unstructured data pipeline with Dagster and dlt.

dlt is an open-source Python library designed to streamline the process of loading unstructured data into structured datasets by providing automated schema inference and evolution. It supports scalable data pipeline building, allowing for deployments on micro workers or highly parallel setups, and offers features such as state management for incremental data extraction. This functionality is demonstrated through a project that ingests GitHub issue data into BigQuery using dlt, with orchestration handled by Dagster. Dagster enables the transformation of data pipelines into assets and resources, enhancing the robustness of the pipeline through features such as schema evolution monitoring and the use of configurable resources. The project also showcases how to orchestrate MongoDB verified sources using Dagster, employing the @multi_asset feature to separate data loading for each collection, which improves debugging and independence. The integration of dlt and Dagster allows for the rapid development and testing of data pipelines before production deployment.

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