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Insights on AI infrastructure, Decision Coherence, and building systems for the machine-driven era.
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77 articles
7 Real-Time Financial Services Use Cases Where a Context Lake Adds Value
Real-time financial services use cases — credit decisioning, card authorization, payments, withdrawal limits — and what breaks when the context each decision reads lags the money moving.
Alex Kimball|Jun 8, 2026
Real-Time Inventory: Why Oversell Happens and How to Prevent It
Overselling isn’t a counting bug — it’s a concurrency problem. When several checkouts read the same availability before any of them decrements it, they all sell the last unit. Here’s the structural fix.
Alex Kimball|Jun 2, 2026![ClickHouse Alternatives (2026): A Workload-First Guide](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1 1"%3E%3C/svg%3E)
ClickHouse Alternatives (2026): A Workload-First Guide
ClickHouse is excellent at what it was designed for — fast analytical queries over large event datasets — but teams hit walls when they push the engine into workloads it wasn’t built for. A workload-first guide: the two kinds of analytical workload people run on ClickHouse, the six pains that bite in production, and the alternatives that fit.
Alex Kimball|May 29, 2026Postgres Schema Design Principles for OLTP, Analytics, and Real-Time Decisions
A practical guide to Postgres schema design principles — database/schema/table hierarchy, normalization, indexing, partitioning, access control, schema evolution — and where the conventional rules need revision for systems that commit decisions inside small validity windows.
Alex Kimball|May 29, 2026The Thundering Herd Problem in Real-Time Decision Systems
The thundering herd problem looks like a load problem, and single-flight or jittered TTLs treat it as one. In a real-time decision system it's a correctness problem — and the real fix is removing the cache, not tuning it.
Xiaowei Jiang|May 22, 2026ACID for Agents: Why Database Consistency Is the Bottleneck for Production AI
AI agents and automated decision systems need ACID consistency on the data they read at decision time — not just the data they write. The industry agrees the agent data layer is broken; it disagrees on why. The converged-database camp says storage fragmentation. We argue it’s computation fragmentation: the derived state decisions depend on — velocity counters, risk scores, features — is maintained in pipelines outside the transactional boundary, so it’s always slightly stale. A Context Lake converges the computation, not just the storage, ingesting via CDC from existing systems without requiring migration.
Boyd Stowe|May 11, 2026Ready to get started?
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