Snowflake or ClickHouse?

Different tools. Snowflake suits broad analytical workloads with irregular query patterns and a team that does not want to operate infrastructure. ClickHouse suits high-volume, well-understood query shapes where latency and cost per query matter more.

Who actually does the work?

A certified senior engineer, personally. There is no account manager layer and no junior delivery team. You meet the engineer who will do the work before anything starts.

What access do you need?

As little as possible, and read-only first. Every change goes through code review and Terraform, so there is a full audit trail, and you can revoke access the moment an engagement ends.

Data & Platform

Data Platform Engineering

Analytics stacks tend to grow without an owner. Schemas drift, replication breaks quietly, personal data ends up somewhere it should not be, and compute spend climbs with every new pipeline nobody removed.

Start with the audit

No sales layer, no juniors. You meet the engineer before anything begins.

// What is included

What you get

Single ownership of schema and migrations rather than shared partial ownership
Replication from source systems monitored, with failures that alert
Personal data isolated from the general analytics path by design
Warehouse compute watched and controlled as a standing concern

// How it runs

The sequence

01
Map
What flows where, who owns it, and what it costs to run.
02
Stabilise
Fix replication, schema drift and the data that should not be there.
03
Control
Cost and access managed on an ongoing basis.

// Stack

Snowflake
ClickHouse
Fivetran
AWS
Terraform
PostgreSQL

// Related work

Where this has been done before

Client names under NDA. The numbers are not.

Analytics warehouses under control, PII isolated (NDA)

Single owner for schema and migrations across both warehouses
Managed replication with personal data isolated from the analytics path
Idle warehouses downsized automatically

Warehouse compute brought under control

AWS bill down 46% for a data governance SaaS (NDA)

EC2 and ECS rightsized against real utilization
Eligible workloads moved to Graviton
S3 storage classes and ECR lifecycle policies applied

AWS bill down 46%

All case studies

// Questions

Before you ask

// Also in Data & Platform

Talk to the engineer who would do the work

A 20 minute call. You describe your setup, you get an honest read on whether this helps, and the top risks worth looking at first.

See pricing

Data Platform Engineering

What you get

The sequence

Map

Stabilise

Control

Where this has been done before

Analytics warehouses under control, PII isolated (NDA)

AWS bill down 46% for a data governance SaaS (NDA)

Before you ask

Related services

Microservices Architecture

Platform Engineering

Warehouse Cost Optimization

Talk to the engineer who would do the work