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Explore data pipelines with Python using Dagster, featuring insights from Pedram Navid. Learn about building efficient data pipelines, orchestrating automation, optimizing deployments with Posit Connect, backfills, partitioning, and popular data tools like DBT, DuckDB, Apache Arrow, and Pollers.
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Quick takeaways
- Dagster simplifies data pipeline creation by focusing on assets and resources for a declarative approach to data orchestration.
- Dagster's asset-based approach allows for easy modeling and execution of pipelines with tracking of asset metadata over time for performance optimization.
Deep dives
Data Pipelines with Daxter
Daxter, a valuable tool for creating data pipelines with Python, was discussed in this podcast episode. Pedram Navied from Daxter Labs highlighted the significance of data pipelines in processing, filtering, and transforming external data for businesses. Using Daxter simplifies building pipelines by focusing on assets and resources, allowing for a more declarative and efficient approach to data orchestration.
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