Data & Database Workflows (page 6 of 40)
PostgreSQL, SQL, CSV, JSON, Excel, PDF, and conversion pipelines — practical workflows for working with structured data safely.
- Multipart CSV uploads: validating chunks before merge
A practical guide to validating multipart CSV uploads without confusing network parts with CSV records.
- Naming conventions for nightly CSV drops (files, columns, partitions)
A practical guide to naming nightly CSV drops so file names, columns, and partition fields stay portable across storage systems and SQL engines.
- Null sentinels: empty string vs NULL vs N/A vs "-"
A practical guide to designing a null-sentinel contract for CSV files so empty string, NULL, N/A, and - do not silently change meaning between systems.
- Number formatting in CSV: commas, decimals, and locale chaos
A practical guide to surviving number formatting in CSV when spreadsheets, loaders, and locales disagree about commas, decimals, and thousands separators.
- Open-source CSV libraries in JS: selection criteria
A practical guide to choosing a JavaScript CSV library without confusing browser-first parsing, Node streams, and lightweight in-memory helpers.
- Parallelizing CSV processing: boundaries that respect quotes
A practical guide to parallelizing CSV processing without corrupting rows when fields contain quotes, commas, or embedded newlines.
- Payroll CSV uploads: common column name mismatches
A practical guide to payroll CSV column name mismatches so imports stop failing on headers, mappings, and field semantics.
- PII scanning in CSV columns: regex vs dictionary approaches
A practical guide to choosing between regex and dictionary approaches for PII scanning in CSV columns without breaking row structure or overtrusting simple matches.
- Pipe-delimited vs comma-delimited: regional defaults that bite
A practical guide to why comma-delimited files fail across regional defaults, when pipe-delimited exports help, and how to keep loaders and spreadsheets aligned.
- Polars vs Pandas for CSV: throughput notes for practitioners
A practical guide to Polars vs pandas for CSV workloads, focused on throughput, memory pressure, bad-line handling, and the real decisions practitioners make in production.