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Boyd Stowe

Boyd Stowe

Solutions Engineering at Tacnode

Boyd Stowe leads solutions engineering at Tacnode. With two decades of experience helping enterprises adopt new database paradigms, he previously worked at Couchbase and IBM. He writes about real-time data architecture, production deployment patterns, and the practical considerations of migrating from composed stacks to unified platforms.

Solutions ArchitectureEnterprise Data SystemsDatabase Migration
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Posts by Boyd (16)

Agent Coordination: How Multi-Agent AI Systems Work Together
AI & Machine Learning

Agent Coordination: How Multi-Agent AI Systems Work Together

Agent coordination is what determines whether multiple AI agents produce coherent results or expensive chaos. Here's how coordination strategies, communication protocols, and fault tolerance actually work — and what breaks in production.

Boyd StoweBoyd Stowe|Mar 5, 2026
Foreign Data Wrappers: S3, Iceberg & Delta Lake
Data Engineering

Foreign Data Wrappers: S3, Iceberg & Delta Lake

Foreign data wrappers let you query Parquet on S3, Iceberg tables, and Delta Lake catalogs with standard SQL and zero data movement. Complete setup guide.

Boyd StoweBoyd Stowe|Feb 26, 2026
Enterprise Integration Patterns for Streaming and AI Architectures [2026]
Data Engineering

Enterprise Integration Patterns for Streaming and AI Architectures [2026]

Publish-subscribe, content-based routing, CDC, event sourcing — the patterns haven't changed, but the architectures have. How each enterprise integration pattern applies in modern streaming, event-driven, and AI agent systems.

Boyd StoweBoyd Stowe|Feb 22, 2026
Vector Quantization: Compress Vectors 4–32x Without Losing Accuracy
AI Engineering

Vector Quantization: Compress Vectors 4–32x Without Losing Accuracy

Scalar, product, and binary quantization — how each compression method works, when to use them, and practical SQL examples for cutting vector memory 4–32x while preserving search quality.

Boyd StoweBoyd Stowe|Feb 20, 2026
LLM Agents: 4 Components That Separate POC From Production
AI Engineering

LLM Agents: 4 Components That Separate POC From Production

LLM agents plan, act, remember, and coordinate. Most die after the demo. This guide breaks down the 4 core components every LLM agent needs, the types you'll encounter in production, and the infrastructure gaps that kill real deployments.

Boyd StoweBoyd Stowe|Feb 19, 2026
Full-Text Search in PostgreSQL: A Complete Guide
Data Engineering

Full-Text Search in PostgreSQL: A Complete Guide

Learn how PostgreSQL full-text search works: tsvector, tsquery, GIN indexes, relevance ranking, and fuzzy matching — with production-ready SQL examples.

Boyd StoweBoyd Stowe|Feb 18, 2026
Similarity Search: What It Is, How It Works, and Why Most Teams Implement It Wrong
AI & Machine Learning

Similarity Search: What It Is, How It Works, and Why Most Teams Implement It Wrong

Similarity search ranks by vector proximity, not exact keywords. Learn how embeddings, ANN indexes, and hybrid search work — and why bolting on a separate vector database creates an infrastructure trap most teams don't see coming.

Boyd StoweBoyd Stowe|Feb 17, 2026
What Is Data Observability? The Complete Guide [2026]
Data Engineering

What Is Data Observability? The Complete Guide [2026]

Data observability monitors data health across your pipelines. Learn what data observability means, how it differs from data quality, the pillars of data observability, and why reactive monitoring isn't enough.

Boyd StoweBoyd Stowe|Feb 14, 2026
Multi-Agent Architecture: 8 Coordination Patterns That Actually Work [2026]
AI Infrastructure

Multi-Agent Architecture: 8 Coordination Patterns That Actually Work [2026]

When AI agents conflict, you get duplicate orders, race conditions, and angry customers. Here are 8 production-tested coordination patterns — from simple locks to distributed consensus — with code examples for each.

Boyd StoweBoyd Stowe|Jan 28, 2026
Feature Freshness Explained: Why Model Accuracy Drops in Production
Real-Time Data Engineering

Feature Freshness Explained: Why Model Accuracy Drops in Production

Your model scored 94% in training. In production it's drifting toward 80%. The features you trained on don't match what the model sees at inference. Here's how to measure feature freshness, detect drift, and close the gap.

Boyd StoweBoyd Stowe|Jan 15, 2026
Primary Key Design: Best Practices for Performance and Scale
Architecture & Scaling

Primary Key Design: Best Practices for Performance and Scale

Your analytical queries are slow—and it's probably not indexing. I've seen query latency double without any code changes, just from a bad primary key choice. Here are the 3 patterns that actually work at scale.

Boyd StoweBoyd Stowe|Dec 29, 2025
Code Like a Mammal
Real-Time Data Engineering

Code Like a Mammal

Evolve to stay a step ahead.

Boyd StoweBoyd Stowe|Oct 15, 2025
Context Lake in Practice: Detecting Fraud with Live-context LLMs
Real-Time Data Engineering

Context Lake in Practice: Detecting Fraud with Live-context LLMs

Securing systems where milliseconds mean millions.

Boyd StoweBoyd Stowe|Sep 9, 2025
Stateful vs Stateless AI Agents: A Practical Comparison
AI & Machine Learning

Stateful vs Stateless AI Agents: A Practical Comparison

Stateful agents retain context across requests. Stateless agents scale but forget. This guide covers the 5 failure modes teams hit in production, when to use each pattern, and the hybrid architectures that actually work at scale.

Boyd StoweBoyd Stowe|Jan 6, 2026
CQRS for AI Agents: Why Eventual Consistency Breaks Autonomous Systems
Data Engineering

CQRS for AI Agents: Why Eventual Consistency Breaks Autonomous Systems

CQRS separates reads from writes. But when AI agents become the read-side consumer, eventual consistency becomes a correctness problem. Here's what changes.

Boyd StoweBoyd Stowe|Mar 20, 2026
Snowflake Dynamic Tables vs Materialized Views: When Each Works, When Neither Does
Data Engineering

Snowflake Dynamic Tables vs Materialized Views: When Each Works, When Neither Does

Snowflake Dynamic Tables and Materialized Views both promise fresh derived state without manual pipelines. This guide covers how each refresh mechanism actually works, when to choose Dynamic Tables over Materialized Views, refresh mode and target lag tradeoffs, monitoring and compute costs, and where both fall short for real-time decisioning workloads.

Boyd StoweBoyd Stowe|Apr 20, 2026