Been migrating our sales team's dashboards from a pricey SaaS to Looker Studio (free tier). We're a k3s shop, so I'm used to building over buying 😅
Assumptions for our case:
* Data sources: PostgreSQL (transactions), Salesforce API, some CSV exports from marketing.
* Need real-time-ish (hourly sync) for pipeline, daily for revenue.
* Team of 10 sales reps, basic filtering by rep/region.
Biggest hurdle was the data blending. Looker Studio's native connectors are okay, but for complex joins, I set up a middleware. Since we're already on k8s, I deployed a lightweight service to pre-aggregate data.
```yaml
# Simplified extract job in our cluster
apiVersion: batch/v1
kind: CronJob
metadata:
name: sales-data-transform
spec:
schedule: "0 * * * *"
jobTemplate:
spec:
template:
spec:
containers:
- name: transformer
image: custom-python-etl:latest
env:
- name: POSTGRES_HOST
valueFrom:
configMapKeyRef:
name: db-config
key: host
```
Then pipe the cleaned data to BigQuery (or even a simple CSV in Cloud Storage) for Looker Studio to consume. Works, but it's definitely a "roll your own" solution. Cost is near zero, but maintenance is a factor.
Curious if anyone else went down this path. How did you handle more complex metrics like weighted pipeline or forecasting? Did you miss any enterprise features from tools like Tableau?
yaml all the things
Interesting approach with the middleware service. I've found that data blending is where Looker Studio either shines or falls apart completely.
For a similar setup, I used a scheduled Zapier task to pull from Salesforce and our PostgreSQL db, merging them in Google Sheets as a staging area before Looker Studio picks it up. It's less elegant than your k8s cron job, but it let our non-technical sales ops person own the pipeline.
How are you handling schema changes in your transformed data? That's bitten me a few times when marketing adds new fields to their CSVs.