Page 3 - SQL

The ANSI SQL standard defines less than many developers assume, and the major database systems rarely implement it in full. This article shows which parts of SQL are actually standardized, where PostgreSQL, MySQL, Oracle, and SQL Server use their own dialect extensions, and how to recognize whether a query is portable or not.

Stored procedures polarize like few other topics in the database world: fewer network round trips and fine-grained access control on one side, hard to test code without simple versioning on the other. An objective assessment shows in which situations stored procedures are actually the right tool and where application code remains the better choice.

A slow query is rarely a coincidence, it almost always has one of a handful of recurring causes. This checklist walks developers step by step through query optimization, from the WHERE clause through missing indexes to the N+1 problem, so that most cases get solved before a DBA even needs to be consulted.

Volume encryption only protects data against a stolen hard drive, not against a compromised database account reading data normally through SQL. Column-level data encryption closes exactly this gap, but demands careful decisions around key management, searchability, and indexing that blanket volume encryption never has to make.

Building a cross tabulation only after exporting to Excel gives up both performance and reproducibility. With CASE WHEN, FILTER, and, depending on the database, native PIVOT commands, pivot tables can be produced directly in SQL, consistent on every run and without a manual post-processing step.

Data integrity that only gets manually verified when needed is already violated before anyone looks. Automated checks built from constraints, checksums and statistical anomaly detection surface inconsistencies long before they turn into wrong reports or bad business decisions.

If you have only solved reporting problems with GROUP BY so far, you always lose the individual rows in the process. Window functions solve exactly this problem: they compute sums, rankings and averages over a defined window of rows without collapsing the result to one row per group. This article explains the OVER clause, PARTITION BY and ORDER BY step by step with real examples.

A flawed database schema costs a multiple of the fix that would have been possible at the design stage. Database schema design with AI review uses Claude to spot normalization mistakes, missing constraints and inefficient index strategies before the first migration runs in production.

Real reporting dashboards rarely need a single window function, they need a combination of CTEs, aggregation and several nested window functions. Cohort retention tables and period-over-period growth rates can be calculated entirely in SQL, structured maintainably with WITH clauses, without raw data ever having to leave the database for the calculation.

Discount campaigns, contract terms, or pricing tiers rarely apply forever, only for a defined period. Effective dating captures exactly that with two columns per row, a start and an end point of validity, controlled by business logic rather than set automatically by the database. The real challenge rarely lies in the columns themselves, but in reliably preventing two validity periods for the same entity from overlapping. This article walks through practical constraint strategies for that problem and works through two full practical examples.