We design new data platforms and improve the foundations already in place. Architecture follows the sources, expected workloads, access requirements, and the people operating the result.
AWS environmentAccess boundary
STORAGE
COMPUTEIN USE
Configuration in codePlan → review → applyStorage, processing, concurrency, freshness, and recovery requirements shape the design. Snowflake, BigQuery, and Databricks serve different environments and workloads. We compare those choices with existing investments and the practical burden of maintaining them, then record the decisions the implementation needs to follow.
Run #024 · Planaws_instance.transform_runner
| instance_type | Current | Proposed |
|---|---|---|
| Size | t3.small | t3.medium |
Plan: 0 to add · 1 to change · 0 to destroy
Run #024 · Review| Environment | dev / data-platform |
|---|---|
| Owner | Platform team |
| IAM role | Existing role retained |
✓ Plan approved · ready to apply
Run #024 · Apply| Compute size | t3.medium |
|---|---|
| Result | 1 resource changed |
| Run record | #024 · saved |
✓ Reviewed configuration applied
Cloud resources need clear environment boundaries, access rules, and operational ownership. On AWS or Azure, Terraform can make resource configuration explicit and changes reviewable. We connect storage and compute choices to the data flows they support, with configuration and handover documentation that the maintaining team can use.
We examine query plans, storage layout, concurrency, and repeated processing against the actual workload. Monitoring connects run health, freshness, and resource usage to named owners. Capacity decisions use expected growth and measured behavior, with cost and maintenance considered alongside speed. Incremental work and appropriate materialization keep processing proportionate to the job.
A warehouse migration changes more than a connection string. We map dependencies, preserve model grain and business definitions, and compare source and target results under agreed checks. The cutover plan names the validation, access changes, recovery options, and owners. Differences are investigated before the team relies on the replacement.
Handover includes architecture decisions, model and pipeline dependencies, validation checks, access responsibilities, and recovery instructions. A platform for a company, scientific lab, or AI research team must support the work people actually do. The result needs to be understandable when a source changes or a new question arrives.
Tell us what you are building, what the existing environment needs to support, and which constraints shape the next step.