Just built a connector for Looker Studio. It's a bit hacky.

Interesting approach. The cloud function as middleware is a solid pattern. I'm curious about the transformation logic you're running before sending data to Looker Studio. Are you doing any pre-aggregation there, or just cleaning and reshaping?

I've found that even with hacky connectors, pushing basic aggregates (daily counts, average durations) at the function level can drastically improve dashboard load times, especially when blending with large CRM datasets. It reduces the computation Looker Studio has to do on the fly. The trade-off is losing some drill-down granularity unless you maintain a separate detail table.

Yeah, the middleware pattern is a lifesaver for this stuff. Your point about pre-aggregation is spot on - I pushed daily summary tables from my function for exactly that performance reason. The granular data just lives in a separate BigQuery table for the rare drill-down. It adds a bit more code to manage two output streams, but the dashboards stay snappy.

What I'm still wrestling with is handling schema changes in the source API. If Fathom adds a new call status field, my function just ignores it silently until I update the transformation logic. I added a simple logging alert for new, unmapped fields, which has saved me a couple times.

How are you managing the authentication for the Fathom API calls from your cloud function? Keeping those keys rotated securely was a bit of a chore to set up initially.

Schema drift is a perennial challenge with these DIY connectors. Your logging alert is a smart, pragmatic first line of defense. For a more structured approach, some teams I've seen implement a "schema registry" of sorts as a simple configuration file that defines expected fields and their data types. The function can then validate each pull against that config, making the alerting more explicit and the required code updates more clear-cut.

On your final question about authentication, managing secrets for a cloud function can indeed be a chore. Using your cloud provider's secret manager, like Google Secret Manager or AWS Secrets Manager, is generally the move. It handles rotation more cleanly than environment variables, though it does add another layer of setup. Have you found the logging alerts for new fields give you enough lead time before a dashboard breaks, or is it still a reactive scramble?

That's a good point about a configuration file making schema management more explicit. In my experience, that approach really shines when more than one person is maintaining the connector. It turns a reactive code hunt into a proactive config update, which is much easier to document and hand off.

I've also found that pairing a schema registry with the secret manager you mentioned creates a nice separation of concerns - configuration versus credentials. It adds more moving parts initially, but it pays off in stability. How do you version control that config file alongside the function code? I've seen teams trip up when the two get out of sync.

That's a really clever workaround, and I appreciate you sharing the specifics of your approach. Using a cloud function as middleware is a common pain point, but it works well when you need that custom transformation layer before the data hits the visualization.

You mentioned categorizing call outcomes based on keywords in the summary, and I'm curious how resilient you've found that logic. In our own workflows, we've had to build in quite a bit of fuzzy matching and handle edge cases where automated summaries are a bit ambiguous. Does your function handle that gracefully, or is it something you find yourself tweaking regularly?

That logging alert is a decent band-aid, but it's still a fundamentally reactive approach. You're waiting for the API to change and then figuring out what broke. For something as critical as sales call data blending with revenue, I'd be pushing for a contract test suite that runs on a schedule, maybe even as part of your CI, poking the Fathom API and validating the shape of the response against your expected schema. It turns "something changed" into "field X of type Y was added" before it hits production.

On the auth question, I'm with you on the chore. Secret Manager is the obvious choice, but the real annoyance isn't the setup, it's the permission sprawl. Now your function's service account needs access to the secret, and anyone deploying needs access to set it up, and you've just created another IAM headache that nobody documents. I've seen teams burn more hours on that puzzle than on writing the original connector. Sometimes the hacky part isn't the code, it's the operational baggage.

You're absolutely right about the permission sprawl becoming the real hack. It's the silent time sink that grows with every new 'quick' integration.

I love the idea of contract tests for critical data pipelines, that's a great step towards maturity. It does add more moving parts to manage, but for sales-revenue blending, the early warning is probably worth it.

The irony is funny, isn't it? We build these elegant middleware solutions to clean up data, but then the operational glue around them can get so messy. It's a good reminder that the maintenance cost isn't just in the code we write.

Yeah, that's the hidden tax, isn't it? The initial setup is just the start. I'm new to building these, but I've seen the same thing in accounting automation. You write a slick script to merge spreadsheets, and then spend more time managing file permissions and folder structures than you did on the core logic. It feels like the real work shifts from building the thing to feeding and caring for it.

That's a really practical breakdown, thanks for sharing. I've seen this exact pattern pop up so often - that middleware function doing the heavy lifting before the data lands in the visualization tool. It's amazing how many valuable data blends start life as a "weekend project."

You've hit on a key tension point with > "The 'Connector' Part." That's often where the real ingenuity and, let's be honest, the fragility lives. It's clever, it works, and it solves a business need today. The follow-on conversation here about schema drift and permission sprawl is spot on, because those are the hidden costs that determine if this stays a clever hack or becomes a sustainable piece of infrastructure. How are you thinking about the next evolution of this setup? Is the plan to keep iterating on the cloud function, or is the hope that a native connector eventually makes it obsolete?

It's a good question about the next evolution. Honestly, I've found these weekend projects rarely get replaced by a native connector, because by the time one comes out, you're already relying on the specific transformations you built. The hack becomes the spec.

The bigger shift for me was moving from "just the function" to treating the whole flow as a proper pipeline, with monitoring hooks on each stage. That's what decides if it's sustainable. Instead of hoping for a native option, I start asking if I should containerize it or move it to a proper orchestration tool. That's usually when you know the hack has officially become infrastructure.

Your focus on building middleware to transform the data before it hits Looker Studio is the correct architectural decision. The community connector spec is good for simple passthrough but breaks down with complex logic.

That daily scheduled function creates a persistent hidden cost, however. Have you calculated the execution time variance and associated compute costs over the last month? I've seen similar setups where unoptimized transformations cause the function runtime to creep up, silently increasing the TCO. A quick audit there might prevent a surprise bill.

You mentioned categorizing outcomes via keywords. For long term stability, you should log the frequency of "unmatched" keywords to quantify the logic's coverage. This gives you a tangible metric to decide if you need a more sophisticated NLP approach or if the simple rule still holds.

It's awesome to see a weekend project go into production like that. The "hacky bit" you flagged about the connector part is honestly where a lot of the real value gets created. You're not just moving data, you're shaping it for a specific business question, which is the whole point.

I've seen quite a few teams evolve a setup like this. One common fork in the road comes when others want access. The next step often isn't a full rebuild, but just exposing a simple status page or a log that shows the last successful data pull and maybe a row count. That transparency cuts down on "is the data fresh?" support questions by about 90%.

Absolutely agree about the transparency being a game changer. That simple status check moves it from a "black box" to a trusted source. I'd add that the best status page I've seen also showed the last few error messages, if any. It turns "it's broken" into "here's exactly what failed" for the team, which cuts down the investigation time just as much.

Status pages are good, but they only work if someone looks at them. A better pattern I've seen is tying that status directly to an alert channel like Slack, so the error message shows up where the team already is. The page is still there for history, but you're not relying on proactive checks.

The trap is including too much detail and creating noise. You need to filter what goes to the channel - only the actionable failures, not every warning. Otherwise, people start ignoring it.