BestHub
Sign in
Data Bricklaying Diary
Author

Data Bricklaying Diary

Records practices, thoughts, and pitfalls on the data grunt-work journey, sharing content on data platforms, data analysis, data processing, data governance, knowledge graphs, and more. Less theory, more hands‑on, making complex data technologies simple.

165
Articles
0
Likes
383
Views
0
Comments
Recent Articles

Latest from Data Bricklaying Diary

100 recent articles max
Data Bricklaying Diary
Data Bricklaying Diary
Sep 25, 2026 · Backend Development

Beyond Retry: Designing Fault Tolerance from Failure Models to Disaster Recovery

This article explains why retry alone is insufficient for fault tolerance, detailing how to classify failures via fault models, apply appropriate mechanisms like timeouts and circuit breakers, design recovery paths targeting state convergence, define RTO/RPO for disaster recovery, and validate all assumptions through fault injection drills.

IdempotencyRPORTO
0 likes · 23 min read
Beyond Retry: Designing Fault Tolerance from Failure Models to Disaster Recovery
Data Bricklaying Diary
Data Bricklaying Diary
Sep 25, 2026 · Backend Development

From Table-Driven to Ontology-Driven: Why Data Alone Can't Explain Business

This article argues that ontology-driven modeling — defining business concepts like delivery, deferral, and confirmation — enables AI and humans to trace actual order records, distinguish occurred facts from pending requests and causal guesses, and answer why an order was delayed without full system rewrite or event sourcing.

AI explanationTransactional Outboxbusiness modeling
0 likes · 17 min read
From Table-Driven to Ontology-Driven: Why Data Alone Can't Explain Business
Data Bricklaying Diary
Data Bricklaying Diary
Sep 24, 2026 · Operations

Observability Isn't a Log Platform: Connecting Business Goals to Verifiable Runtime Facts

This article explains that observability is not centralized logging but a practice of defining business outcomes (SLIs/SLOs), correlating metrics, logs, and traces via stable business identifiers, designing actionable alerts, structuring dashboards along business flows, separating observability responsibilities, halting automation when evidence is unreliable, and using runtime data to continuously correct architectural assumptions.

SLI/SLOSREalerting
0 likes · 28 min read
Observability Isn't a Log Platform: Connecting Business Goals to Verifiable Runtime Facts
Data Bricklaying Diary
Data Bricklaying Diary
Sep 24, 2026 · Artificial Intelligence

Why Your Knowledge Graph Fails to Explain Business: The Missing Ontology Layer

This article uses an order management example to explain why a knowledge graph alone cannot capture business meaning, defines ontology as explicit computer-processable descriptions of concepts and constraints, distinguishes ontology from knowledge graphs and graph databases, compares OWL and OPM modeling approaches, and provides six validation questions for ontology-driven data governance.

Business OntologyOPMOWL
0 likes · 21 min read
Why Your Knowledge Graph Fails to Explain Business: The Missing Ontology Layer
Data Bricklaying Diary
Data Bricklaying Diary
Sep 23, 2026 · R&D Management

AI Empowers Senior Developers, But How Do Juniors Learn Engineering Judgment?

A veteran developer reflects on how AI tools like Cursor and Claude Code have shifted their workflow from coding to design and verification, boosting confidence, but worries that juniors who only prompt AI will miss the crucial learning process of debugging, understanding system boundaries, and developing judgment—especially when business pressures prioritize short-term efficiency over long-term talent development.

AI-assisted developmentSpecification-Driven Developmentengineering judgment
0 likes · 12 min read
AI Empowers Senior Developers, But How Do Juniors Learn Engineering Judgment?
Data Bricklaying Diary
Data Bricklaying Diary
Sep 23, 2026 · Artificial Intelligence

Beyond Average Samples: Mastering Long-Tail Data & Active Learning for High-Stakes AI

This article presents a framework for high-quality dataset governance that prioritizes long-tail, high-risk samples over volume, distinguishes four data types (long-tail, hard examples, anomalies, unknowns), defines five hard-example gap categories, and structures a seven-step active learning loop prioritized by business risk, coverage gaps, information value, representativeness, and expert cost with independent evaluation validation.

active learningdata qualitydataset governance
0 likes · 30 min read
Beyond Average Samples: Mastering Long-Tail Data & Active Learning for High-Stakes AI
Data Bricklaying Diary
Data Bricklaying Diary
Sep 22, 2026 · Artificial Intelligence

Jev: Fast, Structured Judgments for AI Agents Without LLM Overhead

TypeSafe's Jev provides fast, constrained judgments (Choice, Score, Noul) for high-frequency agent decisions like ticket routing, model selection, and browser actions, cutting latency and cost while requiring rigorous evaluation, fallback, and human oversight because type safety does not guarantee correctness.

AI agentsEvaluationJev
0 likes · 14 min read
Jev: Fast, Structured Judgments for AI Agents Without LLM Overhead
Data Bricklaying Diary
Data Bricklaying Diary
Sep 21, 2026 · Backend Development

Scaling Isn't Just Adding Instances: End-to-End Capacity, Traffic & Cost Design

This article argues that true system scaling requires end-to-end capacity planning across ingress, task processing, dependencies, and recovery—not merely adding instances—and shows how to translate business growth into workload models, manage backpressure, set per-link capacity budgets, validate with realistic load tests, and balance cost against performance.

Auto ScalingCost Optimizationbackend architecture
0 likes · 24 min read
Scaling Isn't Just Adding Instances: End-to-End Capacity, Traffic & Cost Design
Data Bricklaying Diary
Data Bricklaying Diary
Sep 21, 2026 · R&D Management

Ontology Review Exposes Governance Conflicts: Customer & Revenue Disputes

The article explains how ontology reviews surface business governance conflicts over definitions like 'customer' and 'revenue,' and provides a framework to distinguish identity, roles, granularity, facts, and rules; assign confirmation authority by domain, cross-domain, and management levels; implement decisions via versioned decision cards; and validate through concrete test cases rather than forced unification.

business semanticscross-domain governancecustomer master data
0 likes · 22 min read
Ontology Review Exposes Governance Conflicts: Customer & Revenue Disputes