Large-scale data mining
- Ingestion, cleaning, joins, indexing
- Sampling, evaluation, monitoring
- Benchmarks and repeatable runs
About
A practical view of large-scale data mining
This site records work patterns that remain stable under scale: ingestion, schema control, sampling, correctness checks, and cost-aware storage. Focus stays on pipeline behaviour, not domain words.
Scope
Large Data Notes covers large-scale data mining and the system work around it: pipelines, checks, framing, automation, LLM systems, and twin builds. The aim is clear, measured notes that can be used in real work.
Data infrastructure
- Pipelines and failure handling
- Schema design and change control
- Quality checks and simple governance
Problem framing
- Clear problem statements
- Metrics and success rules
- Cost–risk–impact trade-offs
Twins
- Data feeds and state updates
- Spatial layers, tiling, spatial joins
- Validation and update rules
Automation
- Guardrails, fallbacks, monitoring
- Alerting and audit traces
- Safe workflow automation
LLM systems
- Search + Q&A, extraction, routing
- RAG, tool calls, assistants
- Evaluation, safety, cost control
Delivery
- From idea to working system
- Integration and rollout steps
- Quality, privacy, maintenance limits
Reusable tools
- Checklists and templates
- Small utilities and starter kits
- Case note format for proof
What is avoided
- News recycling
- Theory-only posts with no checks
- Vague claims without evidence
How posts are written
- Limits first (time, memory, cost, correctness)
- Checks and failure paths
- Decision and next step at the end
What is covered
Large-scale data mining is the core. Notes also cover the system work around it, when it affects results.
- Pipelines and validation checks
- Problem framing and metrics
- Twins (digital and spatial)
- Automation and monitoring
- LLM systems (RAG, assistants, evaluation)
- Business delivery steps (rollout, maintenance limits)
Guides
Short explanations that end in a decision and a next step.
- formats and storage choices
- sampling and bias control
- chunking and memory safety
- validation habits
Case Notes
Proof pages: concrete runs, constraints, and measured outcomes.
- inputs and constraints
- method and run notes
- checks for correctness
- results and trade-offs
Tools
Reusable blocks that reduce mistakes in the next run.
- pipeline review checklist
- sampling validation checklist
- conversion plan
- case note template
Principles selector
Select what matters right now. This builds a quick reading path suggestion.
stability
correctness
cost
speed
reproducibility
simplicity
Suggested path
Start with the pipeline checklist, then read the 100GB case note, then review CSV vs Parquet.
Selection is stored locally in this browser.
Examples may mention a domain (for instance agriculture) when it helps, but the method remains the same: data behaviour, constraints, and checks. The goal is transferable workflow, not domain talk.
If a pipeline is failing or slow, the fastest starting point is the pipeline review checklist. For scoped support, use the services page or contact.