Practical notes on building reliable software by Arunkumar Ganesan

Influence Is Overrated. Credibility Is What Actually Moves Work

Why individual contributors earn trust through evidence, timing, and decision quality instead of title or meeting visibility.

Vector Data Architecture: Similarity Search and AI Retrieval

How embeddings, metadata, vector indexes, and retrieval services support semantic search and AI applications.

Derived Data Architecture: Metrics, Features, and Reusable Results

How calculated metrics, aggregates, ML features, and materialized views are built, tested, served, and reused.

Metadata Architecture: Make Data Findable and Trustworthy

How catalogs, lineage, owners, schemas, quality signals, and freshness data make the data estate usable.

Reference Data Architecture: Small Data That Keeps Systems Aligned

How country codes, currencies, tax categories, statuses, and policy lists are governed, versioned, searched, and analyzed.

Master Data Architecture: Golden Records for Core Entities

How customer, product, supplier, employee, and location master data is matched, governed, published, and analyzed.

Graph Data Architecture: Relationships You Can Query

How relationship data supports fraud detection, recommendations, permissions, and connected search.

Geospatial Data Architecture: Location, Routes, and Spatial Search

How coordinates, zones, routes, polygons, and distance queries become reliable operational and analytical systems.

Event Stream Data Architecture: From Logs to Real Time Decisions

How event streams power ETL, search projections, analytics, alerts, and operational decisions.