Practical notes on large-scale data mining under real limits: storage and compute choices, checks, benchmarks, and reusable tools. Notes also cover pipeline patterns, problem framing, twin builds, automation, and LLM systems when they help.
Step-by-step methods that end in a clear choice.
Case NotesBenchmarks, failures, fixes, and measured outcomes.
ToolsChecklists, scripts, and templates for repeated work.
Click a step. Each one ends in a real page you can use right now.
Read the promise and choose a route based on the main constraint (speed, cost, correctness, automation).
Pick one guide and follow it end-to-end. Keep notes on what changed and what was checked.
Use a checklist on a real dataset. The goal is a repeatable method, not a one-off fix.
Case notes show what failed, what changed, and what improved, with an evidence block at the end.
Scope explains what is covered, what is avoided, and how pages are organised.
If a problem is active, send details in a clean format (data size, limits, checks, and a sample).
Tip: start with one constraint. Measure before/after. Save the checks.