Data & Database Workflows (page 15 of 26)
PostgreSQL, SQL, CSV, JSON, Excel, PDF, and conversion pipelines — practical workflows for working with structured data safely.
- SQL ROW_NUMBER, RANK, and DENSE_RANK Guide
A practical guide to SQL ROW_NUMBER, RANK, and DENSE_RANK with clear examples for ranking rows, handling ties, selecting top records per group, and solving real reporting and analytics problems.
- SQL SELECT Statement Explained
A practical guide to the SQL SELECT statement with beginner-friendly explanations, examples, and real query patterns for filtering, sorting, grouping, aliases, expressions, and clean result design.
- SQL Stored Procedures vs Functions
A practical guide to SQL stored procedures vs functions, covering how they differ, how they are used in applications and reporting, where side effects matter, and the design patterns that help technical teams choose the right database logic structure.
- SQL String Functions Guide
A practical guide to SQL string functions covering CONCAT, LENGTH, LOWER, UPPER, TRIM, SUBSTRING, REPLACE, POSITION, LIKE, and the text-cleaning patterns developers, analysts, and data teams use every day.
- SQL Subqueries Explained
A practical guide to SQL subqueries with clear examples for filtering, aggregation, correlated logic, EXISTS, IN, nested queries, and the real-world patterns developers, analysts, and data teams use every day.
- SQL Transactions and ACID Properties
A practical guide to SQL transactions and ACID properties, covering commits, rollbacks, consistency, isolation, concurrency, failure handling, and the real reasons transactions are essential in modern database systems.
- SQL UNION vs UNION ALL
A practical guide to SQL UNION vs UNION ALL with clear examples, performance considerations, duplicate-handling logic, and the real-world query patterns developers, analysts, and data teams use every day.
- SQL Views vs Materialized Views
A practical guide to SQL views vs materialized views, covering virtual query logic, stored results, refresh strategies, performance, analytics workloads, and the design patterns teams use in real database systems.
- SQL vs Excel for Data Analysis
A practical guide to SQL vs Excel for data analysis, covering strengths, limitations, workflows, performance, collaboration, reporting, and how to choose the right tool for your data tasks.
- SQL vs Excel
A practical guide to SQL vs Excel covering scale, workflows, formulas, queries, reporting, collaboration, limits, automation, and when analysts should use one, the other, or both together.