Build a Cloud Comparison Worksheet for a BI or Data Engineering Client

Before comparing warehouse engines, list every current data source: the transactional database, the SaaS tools with export APIs, any existing data lake. If most of it already sits on one platform, that's the starting weight in the worksheet, since moving terabytes of existing data to match a warehouse preference costs real time and money in egress and transfer, not just opinion.

BigQuery on Google Cloud is built around a serverless, pay-per-query model that rewards ad hoc, bursty analytical queries. AWS's Redshift is a provisioned-cluster model, which fits more predictable, steady query loads better, alongside Athena for serverless queries directly against data in S3. Fill in the client's actual query pattern here: is it a handful of analysts running ad hoc questions, or a fixed set of dashboards refreshing on a schedule? The answer points toward a different engine on each platform, not just a different vendor.

Run both platforms' pricing calculators against the client's actual expected query volume and storage size rather than a generic example. Pay-per-query pricing can look cheap in a demo and expensive once real usage patterns include a few unoptimized queries scanning entire tables; provisioned pricing can look expensive in a demo and cheap once utilization is high. The worksheet should show a real number for the client's own workload, not a vendor's example workload.

If the client's BI dashboards feed decisions their own leadership relies on, a broken pipeline after a schema change is a real credibility hit for both you and the client. Keep your own change failure rate low on pipeline changes by testing against a sample of production-like data before a schema or transformation change goes live, regardless of which warehouse it feeds¹.

A consulting engagement ends; the client's own team keeps running what you built. Weigh how comfortable the client's in-house analysts and engineers are with SQL dialects and tooling on each platform, since a beautifully built pipeline the client's team can't maintain becomes your support burden long after the contract ends. This row often outweighs a small cost difference between the two platforms.

Do this exercise with the client's stakeholders present rather than handing them a finished recommendation, since watching the tradeoffs get filled in with their own numbers builds far more buy-in than a slide deck ever does. It also surfaces constraints you didn't know about, like an existing enterprise agreement or a security team's platform preference, before you've built a recommendation around the wrong assumption.

Fill in these rows during the session:

1. Where the client's data already lives, listing every source including transactional databases, SaaS exports and any existing data lake.
2. The client's query pattern: bursty ad hoc analysis suits BigQuery's pay-per-query model, while steady loads suit provisioned Redshift.
3. Total cost from each platform's pricing calculator, using the client's real query volume and storage size.
4. How a broken pipeline after a schema change would affect the dashboards the client's leadership relies on.
5. Who on the client's team can maintain the warehouse after your engagement ends.

The comparison you build at kickoff goes stale as the client's data volume and query patterns actually evolve, often faster than anticipated once the warehouse is in daily use across more teams than originally planned. Revisit the worksheet at the first contract renewal or roughly a year in, with real usage data in place of the original estimates, rather than assuming the original recommendation still holds.

This revisit also gives you a natural, low-pressure moment to flag any warning signs, like a cost trend climbing faster than the client's data growth would explain, before it becomes a bigger problem the client discovers on their own from a surprising invoice.

Name a specific person on the client's side who will own monitoring cost and query performance after your engagement ends, and make sure they've actually been in the room for at least one of the worksheet sessions rather than inheriting a system secondhand. A client-side owner who understood the tradeoffs when they were made asks better questions six months later than one who's simply been handed a finished system and a login.

Leave behind a short, plain-language version of the worksheet itself, not just the technical documentation, so that owner has something to refer back to the next time someone on their team asks why the warehouse works the way it does, without having to track you down months after the contract ends.