DEV Community Kicks Off Meme Monday With Community-Sourced Cover Image

·2 views

DEV Community has launched its recurring Meme Monday feature, a lighthearted weekly tradition on the platform. The cover image for this edition was sourced from the previous thread's contributions. DEV emphasizes that it operates as an inclusive space for all participants. To maintain respectful engagement, moderators will downvote any humor deemed to be in poor taste.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

SQL Views vs Temp Tables: Key Performance and Use-Case Differences Explained

Data analyst Michael Nocito published a technical comparison of SQL views and temporary tables on DEV Community in August 2026. A view stores only the query text and re-executes it on every read, while a temporary table stores the actual result rows after a single execution. Benchmarks on a 400,000-row SQLite orders table showed that reading a view took 467.6 ms per query, versus effectively zero milliseconds for the equivalent temp table. Over ten consecutive reads, the view accumulated nearly 4,700 ms in total execution time compared to just 0.2 ms for the temp table. The article concludes that temp tables are preferable when the same expensive aggregate is queried multiple times within a session, while views suit scenarios where up-to-date data is required on each read.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

SQL Subqueries vs CTEs: Why Named Steps Are Easier to Debug

Data analyst Michael Nocito published a technical guide on August 8, 2026, explaining the practical difference between SQL subqueries and Common Table Expressions (CTEs). Both approaches produce identical query logic and results, but CTEs allow developers to inspect intermediate outputs at each step, something nested subqueries do not permit. The guide demonstrates this using a 16-row orders dataset loaded in DuckDB, with queries that transfer directly to PostgreSQL, Redshift, and Snowflake. Nocito outlines four positions where subqueries can appear in SQL and notes that most errors stem from returning the wrong data shape for a given position. His core advice is to lift the innermost subquery into a named CTE and run a row count against it to verify what the rest of the query is actually working with.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Lisbon Agency Replaced 12 Client Tools With One Custom-Built Workspace

SharpHaw, a small growth-focused agency based in Lisbon, built an internal platform called SharpOS to replace the fragmented stack of roughly a dozen tools typically used to manage client engagements. Founder and senior engineer behind the agency found that clients were often confused about what work was being done, since they only had access to one or two tools out of many in use. Rather than adopting a client portal — which the team viewed as a secondary layer where work gets copied rather than created — they built a single workspace where the actual work happens and clients can follow along in real time. SharpOS consolidates functions ranging from project boards and audits to automations, analytics, and a media center, serving clients across 19 European countries. The agency notes that cost savings were not the primary motivation; the core goal was making the work continuously visible to clients without requiring them to ask for updates.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How SQL Window Frames Control Running Totals, Explained with Real Data

Data analyst Michael Nocito published a practical SQL guide on August 8, 2026, explaining how window frame clauses determine the outcome of running total calculations. The tutorial demonstrates that omitting the ROWS BETWEEN clause causes SQL to use a default frame, which can produce incorrect cumulative figures when column values are repeated. Using a dataset of 16 orders totalling 9,890 in revenue loaded into DuckDB, Nocito walks through the exact syntax — SUM(revenue) OVER (ORDER BY month ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) — to build accurate cumulative columns. The guide also covers partitioned running totals, moving averages, and a verification method to confirm results against the table's grand total. The SQL dialect used is close to PostgreSQL, making the examples directly transferable to Postgres, Redshift, and Snowflake.

0 comments Read more at DEV Community