Data Ingestion Patterns: When to Use Push, Pull, and Poll (With Real Examples)

In the realm of data engineering, selecting the right data ingestion pattern—push, pull, or poll—is crucial for building reliable and maintainable pipelines, as demonstrated using real Dagster code examples. Ingestion is often an afterthought in data engineering, leading to challenges when source systems change, requiring robust and scalable solutions. Push-based ingestion, initiated by the source system, is effective for real-time data delivery but offers less control over timing and volume. Pull-based ingestion, controlled by the data platform, provides scheduling flexibility but depends on source systems exposing APIs. Polling-based ingestion combines aspects of both, checking for new data frequently, but it involves complex state management. Modern data platforms ingest from a variety of sources, necessitating consistent patterns to avoid issues like inconsistent error handling and data quality problems. This guide emphasizes the importance of idempotency, schema management, observability, and error handling to avoid technical debt and operational headaches. While building custom solutions can be a valuable learning exercise, the guide suggests leveraging managed solutions like Fivetran and open-source ones like Sling to focus on high-value engineering work.

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