A layer-by-layer architecture for a construction dispute intelligence platform: hashed intake and immutable originals, OCR with page coordinates, a versioned Primavera P6 schedule plane, an event model that joins documents to activities, hybrid retrieval on pgvector, and a generation layer bounded by human confirmation. Written around what each layer prevents.
A module's real cost is not writing it, it's upgrading it. Five signals that separate reusable Terraform modules worth adopting from the ones your team will pin at an old version and never touch again, plus the state-address failure that makes version bumps dangerous.
Both platforms look identical on day one: a schema file, a typed client, a deploy that works. The difference shows up months later, in the queries you cannot afford and the layer you cannot leave. A practical comparison of data models, billing shape, auth stickiness and exit cost.
Podman is the better-designed container engine and Docker is the better-supported one. A working engineer's comparison: the architectural difference that drives everything, where each one wins and loses, the four things that genuinely break in a migration (compose, the Docker socket, privileged ports, restart on boot), and a decision procedure to run against your own setup.
Everyone argues this one on data volume, and volume is the argument that matters least: CRM data is small enough that both architectures handle it comfortably. What actually decides data lake vs data warehouse for CRM analytics is how much point-in-time history you need, how fast the schema churns, what shape your queries are, and who is going to maintain the thing. Includes a decision procedure you can run in an afternoon.
Agentforce and AWS wire together in four standard patterns, and every one of them has a point where Salesforce's guarantees stop and yours start. This traces a single request across each boundary it crosses, covers the Trust Layer default most write-ups get wrong (LLM data masking is disabled for agents), and sets out what changes the moment a callout lands in your own account: retention, audit trail, and user identity that does not travel.
Most Shopify dashboards built on AWS work perfectly for about three weeks, then quietly drift away from the numbers in the Shopify admin. Here is why that happens, and how to design the ingestion, storage and query layers so your totals still reconcile six months in.
In a SaaS analytics product, the Redshift bill tracks how often queries arrive, not how much data they touch. Here is how the meter actually works, why connection pools bill you while nobody is using the product, how to attribute spend to a tenant, and which isolation choices quietly cost more than they save.
Rendering a dashboard inside your app is the easy part. Tenant isolation, session cost and query mode are what break. A practical walkthrough of the four decisions behind embedded analytics on AWS, the API constraints that lock you in, and the errors you will actually see.
Semantic search finds the most persuasive document, not the earliest one. Here is how to architect an AI construction claims and dispute intelligence platform on AWS so retrieval respects the contractual clock, schedule data stays out of the vector index, every answer resolves to a page, and privileged material never shares a retrieval path with project records.