Three Ways to Ingest from Postgres

Concepts covered: paFullVsIncremental, paCdc, paIdempotency

Vocabulary becomes useful applied to a single source seen three ways. Take a Postgres operational database with two tables of interest: customers (slowly changing dimension, ~2M rows) and orders (event-shaped, ~500M rows growing at ~10M per day). The downstream destination is Snowflake. The product team wants the customers table fresh within an hour and the orders table fresh within five minutes. Three legitimate strategies exist. Each is correct for some scale and some operational context. Strategy 1: Periodic Full Load + Incremental Pull The first strategy treats the two tables differently. Customers is small enough that a nightly full load (truncate-and-replace) is acceptable; the table reloads every 24 hours and the freshness SLO is met. Orders is too large for full load, so incrementa

About This Interactive Section

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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