SQL

One of the most common operations in a data pipeline is the upsert — insert new records, update existing ones, and optionally delete removed ones, all in a single atomic operation. The SQL MERGE statement handles all three in one query. Understanding it is essential for anyone building incremental load pipelines in Snowflake, BigQuery, Databricks … Read more

After every pipeline run, you need to verify that what landed in your warehouse actually matches what was in the source. Row counts alone are not enough — a pipeline can load the correct number of rows but with wrong values, missing columns, or shifted amounts. Reconciliation SQL catches these problems before your data consumers … Read more

The most dangerous bugs in a data pipeline are not the ones that throw errors — those are easy to find and fix. The dangerous bugs are the ones that silently produce wrong results: queries that run successfully but return incorrect data that flows into your warehouse, corrupts reports, and goes undetected for weeks. This … Read more

Time-series calculations — running totals, moving averages, period-over-period comparisons — are among the most common requirements in data engineering. Analytics teams need them in dashboard tables, finance teams need them for revenue tracking, and ML pipelines use them as features. This tutorial covers the full range of time-series SQL patterns with real data, including how … Read more

Slowly Changing Dimensions (SCD) solve one of the most fundamental questions in data warehousing: when a customer changes their address, do you overwrite the old address or keep it? The answer depends on whether your business needs to report on current state only, or historical state at any point in time. This tutorial walks through … Read more

Common Table Expressions (CTEs) and subqueries often produce identical results, but they are not interchangeable in a data engineering context. The choice between them affects readability, debuggability, and in some databases, query performance. Getting this right matters when you are writing transformations that run millions of rows in production. This tutorial explains the practical differences, … Read more

Loading a full table on every pipeline run is expensive and slow. Once your source tables grow beyond a few million rows, full loads become impractical. Incremental loading — processing only new and changed data since the last run — is the pattern that makes production data pipelines scalable. This tutorial covers three incremental load … Read more

Introduction If you are starting a career in technology—especially in Data Engineering—there is one skill you will repeatedly hear about: SQL. Some beginners ignore it because they think modern tools like Python, AI, or cloud platforms are more important. Others underestimate it because it looks “too simple.” But here is the truth: SQL is the … Read more

Introduction As your database grows, you may start to notice that your SQL queries become slower. Simple queries that once took milliseconds may begin to take seconds—or even longer. This can impact application performance, user experience, and overall system efficiency. One of the most effective ways to solve this problem is SQL indexing. Indexing is … Read more