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.
What GitOps actually changes in day-to-day operations: git as the source of truth, reconciliation, auditability, and the controls a production GitOps setup needs.
What zero-trust networking means for cloud-native systems: identity-based access, microsegmentation, mutual TLS, and the controls to adopt it without breaking everything.
How policy as code enforces security and compliance standards automatically without slowing developers: where to enforce, how to avoid friction, and the controls it needs.
How to configure Kubernetes cluster autoscaling that scales for demand without runaway cost: bounds, policies, and the cost-awareness a production setup needs.
How to right-size Kubernetes with requests and limits grounded in real usage: avoiding over-provisioning and throttling, and the controls a production cluster needs.
Why cost allocation tagging is the unglamorous practice that unlocks cloud cost control: a tagging strategy, enforcement, and the accountability it makes possible.
A practical handbook for setting service level objectives your team can actually hit: choosing SLIs, realistic targets, error budgets, and the operating model behind them.
Why observability belongs in development, not after an incident: instrumenting before you ship, the three pillars, and the practice that makes systems debuggable in production.
Why environment variables fail as secrets management at scale, and how to do it right: centralized storage, dynamic secrets, rotation, and the controls a production system needs.
The real cost of going multi-region is more than duplicated infrastructure: data replication, consistency, operational complexity, and the controls a multi-region system needs.
A practical guide to progressive delivery: canary releases, blue-green deployments, and feature flags, plus the metrics and automation that make rollouts safe.
What golden paths are and how they boost developer productivity: paved defaults, escape hatches, and the platform discipline that makes the right way the easy way.
What a new platform engineering team should build in its first 90 days: sequencing, golden paths, and the foundations that earn adoption instead of resentment.
What a cloud landing zone is and how to get the foundation right the first time: account structure, identity, networking, guardrails, and the controls a production landing zone needs.
A practical look at feature stores: what problems they solve, when you actually need one, the cost of building too early, and how to decide for your ML program.
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…
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.
