Long-form essays from the engineers shipping AI inside payers, hospitals, energy operators and proptech platforms. Written for technology leaders who care more about what runs in production than what trended last week.
The silent failure modes that quietly kill data pipelines, cardinality explosions, skew, fan-out joins, and how to detect and prevent them before they blow up cost and latency.
How to build an on-call practice for data engineering: runbooks, alerting, escalation, and the operating model that turns 3 AM pipeline failures into routine recoveries.
How to test data pipelines properly with unit, integration, and contract tests, plus data quality checks, so bad data is caught before it reaches dashboards and models.
A framework for choosing between streaming and micro-batch data processing: latency tiers, cost, complexity, and the controls each production pipeline needs.
How partition pruning, clustering, and file layout turn slow, expensive warehouse queries into fast ones, with the design patterns and controls a production data platform needs.
Why most data catalogs go unused and how to build one people actually rely on: adoption-first design, ownership, lineage, and the workflows that keep it current.
Learn how a semantic layer gives an enterprise one governed definition of revenue and every other metric: architecture, governance, and the controls a production deployment needs.
Lift-and-shift cloud migrations save the initial migration cost and accumulate technical debt that becomes refactoring cost later. The trigger to refactor is recognizable. Here is the practitioner…
Internal Developer Platforms pay off at specific organizational sizes and fail at others. The buildout that works has identifiable phases. Here is the practitioner reference for when to start and…
Real estate underwriting AI has matured for commercial investment decisions. The patterns reflect the data and the analytical workflows. Here is the 2026 reference.
Smart building data architectures have specific properties that distinguish them from generic IoT platforms. The patterns reflect building operational reality. Here is the 2026 reference.
AI in property management has shipped most successfully in maintenance triage and vendor coordination. The patterns reduce operational cost and improve resident experience. Here is the 2026 reference.
Real estate lead scoring AI has to outperform round-robin assignment without creating fair housing problems. The patterns that work in 2026 have specific properties. Here is the reference.
Demand response AI has moved beyond simple peak forecasting. The patterns span day-ahead planning through real-time dispatch. Here is the 2026 reference for what works.
Grid edge computing has matured into a category with specific deployment patterns. The substation is increasingly where AI inference happens. Here is the 2026 reference.
EV charging networks have grown into major grid loads requiring AI for pricing, load balancing, and grid coordination. The patterns have specific properties. Here is the 2026 reference.
Energy storage optimization combines dispatch decisions with degradation management. AI handles both. Here is the 2026 reference for what works.
Smart meter analytics at scale requires specific data platform patterns. The patterns reflect both the data volume and the utility operational requirements. Here is the 2026 reference.
Wildfire and weather AI has become operational reality for utilities in fire-prone regions. The risk models are in production rather than research. Here is the 2026 reference.
AI in energy efficiency programs has matured across targeting, measurement, and verification. The patterns affect program design and operations. Here is the 2026 reference.
Renewable generation forecasting has matured into a production AI category supporting grid operations. The patterns differ by resource type and by use case. Here is the 2026 reference.
AI in outage management spans prediction, restoration optimization, and customer communication. The patterns have matured into operational reality. Here is the 2026 reference.
Energy customer analytics has matured beyond billing into broader customer insight. The patterns affect customer service, program design, and revenue protection. Here is the 2026 reference.
PropTech platforms integrate with many systems: MLS, CRM, payment, accounting, marketing. The integration tax is real. Here is the 2026 reference for managing it.
One long-form essay every other Wednesday. Written by the engineers shipping production AI for our clients, not by a content team. No promotional emails. Unsubscribe in one click.
