Alex Kimball
Brand & Content at Tacnode
Alex Kimball leads brand and content at Tacnode. He previously worked at Cockroach Labs, where he helped explain distributed SQL to engineers evaluating distributed SQL databases. He writes about data infrastructure positioning, the Context Lake category, and how engineering teams evaluate real-time data platforms. His work bridges the gap between deep technical architecture and the practical problems infrastructure buyers face.
Posts by Alex (21)
![From Context Engineering to Context Infrastructure](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1 1"%3E%3C/svg%3E)
From Context Engineering to Context Infrastructure
Context engineering has become the defining discipline of AI agent development. But the conversation is missing a layer. Techniques for structuring context are well-understood. The infrastructure that makes context complete, consistent, and current at decision time is not.
Alex Kimball|Mar 19, 2026ETL Pipelines: What They Are, How They Work, and When to Eliminate Them
ETL pipelines extract data from source systems, transform it into a usable format, and load it into a destination. This guide covers how ETL pipelines work, common architectures, tools, failure modes, and when streaming and CDC approaches eliminate the need for batch ETL entirely.
Alex Kimball|Mar 13, 2026Medallion Architecture: Bronze, Silver and Gold Layers in Modern Lakehouses
Medallion architecture organizes your lakehouse into bronze, silver and gold layers with progressive data refinement. Learn how each layer works, when to apply the pattern, and where it breaks down for real-time decisioning.
Alex Kimball|Mar 11, 2026Streaming Database: What It Is, How It Works, and When You Need One
A streaming database replaces the batch query model with continuous computation — materialized views maintained incrementally as data arrives. Here's how they work, when they help, and where they run out of runway.
Alex Kimball|Mar 5, 2026What Is Real-Time Artificial Intelligence? Architecture, Use Cases, and Data Streaming
Real-time artificial intelligence processes live data and makes decisions in milliseconds. Learn the architecture, use cases across industries, and how real-time data streaming powers AI that acts on current reality.
Alex Kimball|Feb 25, 2026What Is Data Quality? The Complete Guide to Data Quality [2026]
Data quality measures whether your data is accurate, complete, consistent, fresh, valid, and unique enough to support the decisions you're making with it. This guide covers the six core dimensions of data quality, how to measure them, common issues that degrade quality, and why data quality matters more than ever for AI and machine learning systems.
Alex Kimball|Feb 22, 2026What Is a Data Contract? The Complete Guide to Data Contracts [2026]
A data contract defines the structure, format, and quality expectations for data exchanged between systems. Learn how to create, implement, and enforce data contracts across your data platform.
Alex Kimball|Feb 13, 2026Stale Data: Causes, Detection, and How to Set Freshness SLAs
Stale data silently breaks models, dashboards, and automated decisions. This guide covers what causes data staleness across batch and streaming pipelines, how to detect it, and how to set freshness SLAs by use case.
Alex Kimball|Feb 12, 2026OpenClaw Proves Agents Work — But Exposes the Context Gap
OpenClaw proves AI agents can manage your life. But as agents scale from personal assistants to enterprise systems, they hit a wall: the context they need is scattered across systems, stale, or inconsistent. Here's what infrastructure is missing.
Alex Kimball|Feb 3, 2026Time Travel Queries: Undo Deletes, Debug Issues, Audit Changes (With SQL)
Someone deleted critical rows. A bad update corrupted data. You need to see exactly what your system saw at decision time. Time travel queries let you rewind any table to any timestamp. Here's how it works.
Alex Kimball|Jan 28, 20265 Industries Where Stale Data Costs Real Money
Fraud detection on 10-minute-old data? You already approved the transaction. Dynamic pricing on yesterday's inventory? You're selling what you don't have. Five industries where data freshness directly determines revenue.
Alex Kimball|Jan 28, 2026Data Freshness vs Latency: Why Fast Queries Still Return Stale Results
Your dashboard loads in 50ms — but shows 2-hour-old data. Latency and freshness are different metrics, and most teams only track one. Here's the four-quadrant framework for understanding which combination you're in.
Alex Kimball|Jan 25, 2026Why Analytical Queries Slow Down (And What to Do About It)
Root causes explained (and solved.)
Alex Kimball|Jan 9, 2026What Is a Feature Store? Architecture, Benefits, and How It Prevents Training-Serving Skew
A feature store is the infrastructure layer that manages, stores, and serves ML features for both training and real-time inference. It prevents training-serving skew by ensuring your model sees the exact same features in production that it trained on. Here's the architecture and what to evaluate.
Alex Kimball|Dec 18, 2025Data Freshness Explained: Why Low Latency Doesn't Mean Current Data
Your query returns in 50ms — but the underlying data is 2 hours old. Data freshness measures how current your data is at the moment a system acts on it. Here's how it differs from latency, the key metrics, and why it matters for AI.
Alex Kimball|Dec 15, 2025The AI Agent Stack in 2026: 6 Layers Your Architecture Needs
Context substrate, semantic retrieval, reasoning, tooling, orchestration, observability — a practical, vendor-agnostic breakdown of the six layers production agent systems require.
Alex Kimball|Dec 6, 2025Context Drift: Why Your AI Agent Loops Until Timeout
Observe, decide, act — then observe stale data and decide wrong again. The loop burns tokens until timeout. Context drift is the failure mode nobody warns you about in agent frameworks. Here's how to detect it and prevent it in production.
Alex Kimball|Dec 4, 2025Live Context: The Key That Unlocks Real-Time AI
Live context is the missing layer that makes real-time AI actually work. Learn why freshness, shared state, and semantic awareness matter for AI agents.
Alex Kimball|Dec 3, 2025Context Lake vs. Data Lake: Key Differences Explained
Why the shift from analysis to action demands a new architecture.
Alex Kimball|Jan 21, 2026Do You Need a Feature Store? 5 Signs You Can't Ship Without One
Some ML teams adopt a feature store too early. Others wait too long and can't ship real-time models. Here are the 5 pain signals that mean it's time—and what to look for when you evaluate platforms.
Alex Kimball|Jan 19, 2026Feature Store Comparison: Feast vs Tecton vs Databricks [2026]
Every feature store looks the same on paper. This guide goes deeper: how pipelines actually differ under load, what separates Feast from Tecton from Databricks Feature Store and Vertex AI, and the 7 architecture criteria ML engineers should evaluate.
Alex Kimball|Jan 22, 2026