Full, Incremental, and Bookmarks

Concepts covered: paFullVsIncremental, paBookmarkPattern

Pull ingestion lives on a spectrum. At one end, every run reads the entire source table. At the other end, every run reads only the rows that changed since the last successful run. The second pattern requires a bookmark of some kind, persisted between runs, that defines what 'since the last run' means. Picking the right point on the spectrum and choosing the right bookmark is the difference between ingestion that scales and ingestion that turns the source database into the bottleneck. When Each Strategy Fits The Bookmark Pattern A bookmark is a small piece of state that records the position the last successful ingestion run reached. The bookmark is persisted in a metadata table the pipeline owns, not in the source. On every run, the pipeline reads the bookmark, queries the source for rows

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This section is part of the Ingestion Patterns: Intermediate lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

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