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Insights on AI infrastructure, Decision Coherence, and building systems for the machine-driven era.
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61 articles
Redis Alternatives: 9 Options for Caching, Real-Time Data, and Decision Workloads
Redis changed its license. The ecosystem fractured. Here are 9 alternatives — from drop-in forks to architecturally different systems — organized by what you're actually trying to solve.
Alex Kimball|Mar 25, 2026
Apache Doris vs ClickHouse: Choosing a Real-Time Analytics Database
Apache Doris and ClickHouse are both columnar OLAP databases built for real-time analytics. Here's how they compare on architecture, joins, real-time ingestion, and consistency — and where both hit the same structural limit for decision workloads.
Alex Kimball|Mar 24, 2026![Incremental Materialized View: How to Keep Derived State Fresh in Real Time](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1 1"%3E%3C/svg%3E)
Incremental Materialized View: How to Keep Derived State Fresh in Real Time
Standard materialized views recompute everything on a schedule. Incremental materialized views apply only the delta — continuously, as data changes. Here's how they work across Postgres, ClickHouse, Databricks, and streaming databases, what each approach can and can't do, and where they all hit the same structural limit.
Alex Kimball|Mar 24, 2026CQRS 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 Stowe|Mar 20, 2026OLTP vs OLAP: The False Choice for the Agentic Era
Every architecture guide frames OLTP vs OLAP as a choice: optimize for transactions or optimize for analytics. But automated decision systems — fraud checks, credit approvals, agent actions — need both transactional consistency and analytical power at the same moment. The Composition Impossibility Theorem proves you can't stitch separate OLTP and OLAP systems together to get there. Here's what comes after the tradeoff.
Xiaowei Jiang|Mar 17, 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, 2026Ready to get started?
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