Series · 6 posts, in reading order

Data Engineering SQL

A practice bank for the SQL data engineering runs on, from DDL to whole pipelines, with every answer run on real sample data.

Nov 3, 2026·40 min read·Data Engineering

Data Engineering SQL · Part 1

SQL for Data Engineers: Foundations, from DDL to the NULL Traps

A practice bank for data engineering SQL, part 1: defining and evolving tables, safe casting, idempotent upserts, deduplication and the NULL traps that silently lose rows. Every answer is run on real sample data.

Data EngineeringSQLPractice

Nov 3, 2026·66 min read·Data Engineering

Data Engineering SQL · Part 2

SQL for Data Engineers: Building a Dimensional Warehouse

Part 2 of a data engineering SQL practice bank: a retail star schema built question by question, with grain, SCD Types 1 to 3, point-in-time joins, race-free watermarks, partition reloads, inferred members, bridge tables, snapshots and quality gates. Every answer is run on real sample data.

Data EngineeringSQLPracticeData Modeling

Nov 3, 2026·53 min read·Data Engineering

Data Engineering SQL · Part 3

SQL for Data Engineers: Medallion Layers and CDC

Part 3 of a data engineering SQL practice bank: a banking medallion pipeline, from Bronze landing to Silver CDC apply and history to Gold recomputes, covering redelivery, log ordering, soft deletes, late data, window-frame traps, data contracts, reconciliation and a refresh that is safe to retry. Every answer is run on real sample data.

Data EngineeringSQLPracticeCDC

Nov 3, 2026·54 min read·Data Engineering

Data Engineering SQL · Part 4

SQL for Data Engineers: Product Analytics Pipelines

Part 4 of a data engineering SQL practice bank: a SaaS clickstream turned into sessions, ordered funnels, cohorts, active users, attribution, MRR movement, churn labels and percentiles, kept correct under duplicates and late events. Every answer is run on real sample data.

Data EngineeringSQLPracticeAnalytics

Nov 3, 2026·43 min read·Data Engineering

Data Engineering SQL · Part 5

SQL for Data Engineers: Hard Problems Across Industries

Part 5 of a data engineering SQL practice bank: stand-alone problems from healthcare, IoT, ride-share, telecom, ad tech, logistics, energy, markets and master data, covering interval merging, sweep lines, tiered rating, range and as-of joins, interpolation and survivorship. Every answer is run on real sample data.

Data EngineeringSQLPractice

Nov 3, 2026·32 min read·Data Engineering

Data Engineering SQL · Part 6

SQL for Data Engineers: Capstones, Whole Pipelines in SQL

Part 6 of a data engineering SQL practice bank: six end-to-end capstones, from loading a warehouse in dependency order and a universal SCD2 loader to a late-data-safe incremental pipeline, point-in-time history, a metadata-driven quality gate and a growth mart at the right grain. Every answer is run on real sample data.

Data EngineeringSQLPractice