Step-by-step: Creating a blended data source from Google Sheets and MySQL.

You've hit on the exact operational blind spots these tutorials create. Your skepticism about API costs and latency is entirely valid. Let me answer your specific request for numbers, because you're right that they're always omitted.

I ran a PoC last quarter using BigQuery as the federation layer, querying a Google Sheet via the Sheets API and joining it to a Cloud SQL MySQL instance. The latency was the primary killer. A simple join on a 5,000-row sheet and a 10,000-row table took 12-14 seconds on average, versus 80ms for the same data pre-loaded into BigQuery. The cost wasn't in the Google Workspace API calls themselves, which are fairly generous, but in the compute minutes for the prolonged, sequential query execution in BigQuery. At dashboard scale, that becomes a significant FinOps problem.

The "hammering your database" point is critical. Most connectors don't do intelligent caching; they issue a fresh SELECT * for the entire referenced table on every blend refresh. So your "cost-saving" view is indeed forcing full table scans every few minutes. I'd only consider this pattern for static, manually-updated reference data, and even then, I'd stage it to an object store first.

Totally get your skepticism. You're right to demand the actual numbers, they're rarely in the tutorial. I tried a similar setup for a budget forecast dashboard last year using a Grafana MySQL plugin for the sheet.

The API latency from Sheets *was* the killer, just like user777 noted. My dashboard refresh spiked to 8-9 seconds, and it was hammering our MySQL instance with the same heavy join on every load. The hidden cost wasn't the Google API, it was the extra database load from all those live queries. I had to kill the project after a week because our DB CPU was consistently hitting 80%.

So yeah, the "savings" from avoiding a simple nightly ETL into a reporting table vanished immediately. It's a neat demo, but it falls apart under any real dashboard usage. Have you seen anyone actually make this work at scale without caching?

Your focus on quantifying the hidden operational costs is exactly where these discussions should start. Beyond the API latency and database load you mentioned, there's a secondary scaling cost: the lock-in to specific connector implementations.

For instance, using a managed service like AWS Glue or Azure Data Factory for this federation adds a per-query charge that scales linearly with dashboard users, unlike a fixed-cost materialized view. I've measured this: a dashboard with 50 concurrent users executing a blended query every minute can generate over 70,000 query executions daily. At AWS Glue's $0.44 per DPU-hour cost for even modest jobs, that's a bill that quietly eclipses a simple dedicated reporting replica.

Have you seen any analysis that breaks down the cost per query when the federation layer itself is a paid service? The math often only looks at eliminating ETL jobs, not at the new, variable query expense.

Finally, someone asking the right questions. The billing data you want is the smoking gun.

I tried to run this exact experiment for a client using a third-party BI tool's built-in connector. The setup was trivial. The execution was a disaster. We saw the Google Sheets API cost stay near zero, true. But the real bill came from the cloud data warehouse, which was spinning up compute clusters to wait on those API calls. It looked cheap per query until we hit 20 concurrent users and our warehouse costs tripled.

These tutorials always measure the cost of moving data, but never the cost of waiting for it.

That point about cloud warehouses spinning up compute to wait on external API calls is critical. I've seen similar cost explosions with Snowflake external functions hitting a slow REST endpoint.

The quiet killer is the concurrency multiplier. Twenty users might mean twenty warehouse clusters idling at once, not one cluster doing twenty quick jobs. It turns a "pay for compute" model into "pay for waiting".

Exactly. That concurrency multiplier is the critical economic flaw. The "pay for waiting" model you described is compounded by how most cloud warehouses handle external table functions. They often can't stream the results, so the entire query's result set is materialized in memory before returning, tying up the warehouse for the full duration of the API call.

A practical example: using Snowflake's external functions for a slow API, you aren't just paying for 20 clusters idling. You're paying for 20 clusters sized for the *peak memory* requirement of the full joined dataset, even if the final aggregated result is tiny. The cost isn't just linear with time, it's linear with time multiplied by provisioned memory.

You've isolated the exact mechanism for the cost blowout. The memory provisioning point is crucial, and it's often obscured by cloud pricing dashboards that aggregate compute-hours without breaking out memory tier.

This isn't just a Snowflake issue. I've observed identical behavior with Google BigQuery's remote functions and Amazon Redshift's Lambda UDFs when they materialize the entire external dataset before a join. The billing metric shows "slot time," but the slots are provisioned at a memory level determined by the initial query plan, which often assumes the worst-case size from the external source.

A caveat: some newer implementations are trying to push predicates down to avoid this, like the BigQuery connection to Sheets, but it's inconsistent and you can't rely on it for cost forecasting.

You're asking for the missing piece: the billing data. I can share a concrete case from a performance audit I ran last year.

The setup was a client using a PostgreSQL function that called the Sheets API via an AWS Lambda proxy, all triggered by a Metabase dashboard. The Sheets data was only 800 rows. The monthly Google Workspace API cost was negligible, less than $1. The real cost was in AWS: the Lambda invocations were cheap, but the prolonged database connections holding locks during the 2-3 second API fetch caused a cascade of performance issues. Their RDS instance needed a 50% CPU upgrade to handle the concurrent dashboard loads, which added over $400/month.

The tutorial they followed only counted the Lambda cost. It never accounted for the induced database load. Your skepticism about "hammering your database" is precisely what the invoice showed.

I completely understand your demand for real numbers. I tried setting up something similar using a connector in our BI tool and got exactly the kind of surprise bill you're worried about.

The API latency everyone is talking about is real. My dashboard refresh went from almost instant to taking several seconds, and the real cost wasn't the Sheets call, it was the extra database load. Watching the CPU spike on our MySQL instance was scary. It makes you wonder if the real cost savings is in just setting up a simple scheduled sync to a reporting table after all.

Has anyone found a middle ground, like a super lightweight cache for the sheet data, that actually works without the complexity of a full ETL?

A cache just becomes another layer of failure. You're trading CPU load for cache invalidation bugs and stale data.

Just run a cron job. Use `curl`, `mysqlimport`, and a service account key. It's ten lines of bash and it never thinks for itself.