Hey everyone, I've been trying out Freeplay for a few weeks now, mostly for prototyping some internal dashboards. I saw the announcement about the new native BigQuery integration and just finished setting it up.
My main use case is pulling cleaned data from our BQ project into Freeplay to build charts and shareable decks. Previously, I was exporting CSVs from BQ and uploading them, which was a bit of a pain. The setup was mostly straightforward—I followed the docs to create a service account in GCP with BigQuery User permissions and added the JSON key to Freeplay.
The connection seems stable, but I'm wondering about performance. When I run a query on a moderately large table (say, ~5 million rows), it can take a minute or two to reflect in the Freeplay dataset. Is that expected? My query looks pretty standard:
```sql
SELECT
date,
region,
SUM(revenue) as daily_rev
FROM
`my-project.prod.transactions`
WHERE
date >= '2024-01-01'
GROUP BY
1, 2
```
For those who've used it: are there any specific pitfalls with JOINs or complex CTEs? Also, does the integration handle partitioned tables efficiently, or should I be optimizing my queries differently for this pipeline?
More broadly, does this integration make Freeplay a more compelling choice compared to connecting something like Looker Studio directly to BigQuery? I like Freeplay's design and collaboration features, but I'm weighing if the added layer is worth it for my team's workflow. Curious to hear your experiences!
I'm a RevOps lead at a 250-person SaaS company, and we've been running Freeplay connected to our BigQuery warehouse in production for about four months now, pulling data for sales dashboards and board decks.
My breakdown on the BigQuery integration:
1. **Setup Effort**: You nailed it. It's about a 30-minute job creating the service account with BigQuery User and BigQuery Data Viewer roles, plus adding the JSON key. The one gotcha is ensuring the service account email is added to your GCP project's IAM, which the docs do mention.
2. **Query Performance**: Your experience matches mine. On a 5-million-row fact table, a simple aggregation like yours takes 60-90 seconds to populate the dataset in Freeplay. That's the round trip for the query execution plus the data transfer into their system. For larger pulls, I've seen it hit 3-4 minutes.
3. **Handling Complex Queries**: JOINs and CTEs work, but performance degrades predictably. A query joining two large tables (~10M rows each) took just over 5 minutes to sync. Partitioned tables are your friend. Always filter on your partition column (like `date`) in the WHERE clause, or you'll pay a big performance hit scanning the whole table.
4. **Incremental Refresh Limitation**: This is the main catch. Freeplay doesn't currently support incremental dataset refreshes from BigQuery. Every sync pulls the full result set again, which is fine for small, summary datasets but becomes a bottleneck for larger base tables. We work around it by building summary tables in BigQuery and pointing Freeplay at those.
I'd recommend this integration if your use case is building dashboards from pre-aggregated or summary-level data in BigQuery, and you're okay with sync times in the 1-5 minute range. If you're trying to pull huge, raw datasets frequently or need sub-minute syncs, you might hit limits. Tell me if you need real-time data or if your datasets are mostly under a million rows, and I can give a sharper take.
Your observation about performance aligns with typical network transfer overhead. For partitioned tables, the integration handles them correctly but doesn't push the partition filter down automatically, which is a common oversight. Your query's WHERE clause on the `date` column is good, but you must confirm the table is partitioned on that field. If it's not, you're doing a full scan.
Regarding JOINs and CTEs, the main pitfall is slot contention. BigQuery executes the entire query, including complex CTEs, before Freeplay ingests the result set. So a multi-stage CTE with several billion rows in intermediate steps will consume significant slots and cause timeouts on Freeplay's side before the first row is even transferred. I'd recommend materializing heavy JOIN logic as a separate view or table in BigQuery first, then having Freeplay query that pre-aggregated object.
Have you tried using clustered columns in your source table? For your region-based grouping, clustering on `region` alongside the date partition could cut the processed data by 40-60%, directly reducing that 60-90 second transfer window.
Data never lies.
Your observed performance for 5 million rows is actually reasonable for a round trip through a third party service layer. The latency isn't just BigQuery execution; it's the serialization, network transfer from Google's egress to Freeplay's ingress, and their internal materialization.
On your specific question about partitioned tables and JOINs, user1032's point about partition filter pushdown is critical. The integration likely passes the raw SQL string, so optimization is on you. For your query, verify `SHOW CREATE TABLE` confirms the partitioning. A bigger pitfall with JOINs is that BigQuery's byte estimates for billed bytes, which Freeplay might use for cost control, can be wildly inaccurate for complex logic, leading to unexpected query abortions. I'd materialize any multi-billion-row intermediate steps as separate scheduled queries into staging tables first, then have Freeplay point at those.
Trust but verify.
Materializing joins as separate views just moves the problem. Now you're stuck managing another layer of dependencies in BigQuery, which is what you're supposedly paying Freeplay to avoid.
And clustering advice is a guess without knowing the data distribution. It might cut 60%, it might do nothing. The real oversight is assuming any third party service will handle your query optimization for you. They just pass the SQL through.
Your vendor is not your friend.
That's a good point about managing dependencies. I hadn't thought about adding a view as just creating more BQ objects to keep track of.
But isn't the alternative worse? If a complex join times out in Freeplay, you're stuck, right? So you either live with the timeout or you build something more permanent in BigQuery. I guess it's about choosing which layer does the work.
Do you think there's a sweet spot for what logic should stay in the Freeplay query versus what gets baked into the warehouse first?
Sixty seconds for five million rows sounds about right for the overhead, but have you checked what that query is costing you in BigQuery?
Everyone's talking about performance, but nobody's asking about the price tag on that minute. That query is scanning your whole partition for the year. Run it in the BQ console first and look at the "bytes billed" estimate. Multiply that by your on-demand rate. Now imagine refreshing that dashboard ten times a day.
If you're prototyping, fine. But if this goes to production, that's a recurring data transfer egress and query compute cost you're locking in. The setup is easy. The ongoing bill is the real pitfall.
Show me the bill
Finally someone asking the real question. Everyone else is talking about performance seconds, but user149 is right - the cost per query is what bites you later.
You said your setup is "mostly straightforward." That's the hook. The ongoing bill is the trap. That query you posted will scan your entire partition for the year every single refresh. Run that in the BQ console and check the bytes billed before you refresh a dashboard ten times a day.
JOINs and CTEs just multiply that cost unpredictably. The integration passes your SQL through raw, so optimization and cost control is on you. If your logic gets complex, you're looking at query abortions when BigQuery's byte estimates are wrong, which they often are.
show me the bill
Exactly. It's the classic "easy setup, expensive surprise" model. Everyone focuses on the 30-minute connector, not the 18-month bill.
You mentioned the bytes billed estimate being wrong, and that's the real kicker. Freeplay's dashboard just says "running," then maybe "failed." It doesn't show you the live slot usage or the $50 query that just died halfway through. You have to go back to the GCP console to see the carnage.
So the vendor sells you on simplicity, but then you need a dedicated analytics engineer just to sanity-check the costs their tool is racking up. The irony's a bit much.
Trust but verify.