Data & Database Workflows (page 2 of 40)
PostgreSQL, SQL, CSV, JSON, Excel, PDF, and conversion pipelines — practical workflows for working with structured data safely.
- Surrogate pairs and emoji in CSV cells: export realities
A practical guide to surrogate pairs and emoji in CSV cells, focused on encoding, truncation, spreadsheet and export quirks, and how to validate files before pipelines break.
- Tab-separated files: when TSV is the safer interchange format
A practical guide to when tab-separated files are safer than CSV, focused on delimiter collisions, quoting behavior, loader support, and the limits teams usually miss.
- Temp tables vs permanent staging: operational tradeoffs
A practical guide to choosing between temp tables and permanent staging tables for CSV pipelines, focused on real operational tradeoffs instead of abstract SQL preferences.
- Time zones in CSV exports: what to store and what to avoid
A practical guide to time zones in CSV exports, focused on storing instants safely, preserving business-local meaning, and avoiding ambiguous timestamps that break pipelines.
- Turning validation errors into user-fixable row reports
A practical guide to turning parser and schema failures into row reports users can actually fix instead of opaque import errors.
- Unicode normalization (NFC/NFD) and duplicate keys
A practical guide to Unicode normalization and duplicate keys in CSV pipelines, focused on NFC vs NFD, canonical equivalence, browser and database behavior, and when to normalize versus preserve original text.
- Upserts from CSV: ON CONFLICT patterns that scale
A practical guide to upserting CSV data at scale, focused on COPY plus staging, one-row-per-key dedupe, ON CONFLICT design, and when MERGE is the better fit.
- URL validation in CSV: tracking parameters and normalization
A practical guide to validating and normalizing URLs in CSV files without breaking attribution, dedupe, or security expectations.
- UTF-8 vs Windows-1252: diagnosing mojibake in CSV
A practical guide to fixing mojibake in CSV imports by identifying the wrong decode path, preserving original bytes, and validating encoding before database load.
- Validating CSV against JSON Schema: a practical mapping
A practical guide to using JSON Schema with CSV data by validating row objects, documenting the mapping layer, and keeping non-JSON-Schema rules explicit.