The Short Version #
For most Amazon sellers and agencies, BigQuery is the pragmatic default — it's serverless, pairs natively with Looker Studio, and the S3-to-BigQuery path for Marketing Stream data is well-trodden. Snowflake makes sense when you're already on it, you're multi-cloud, or you have heavy, sustained analytical workloads. Neither is wrong; the tiebreaker is usually your existing stack.
Why BigQuery Fits Most Sellers #
- Serverless — no clusters to size or keep warm; you query and go.
- Native Looker Studio — free, capable dashboards straight on top of your tables, easy for non-technical staff.
- Easy Amazon ingestion — the BigQuery Data Transfer Service and simple S3→GCS→BQ paths move Marketing Stream and report data in with little glue code.
- Pay-per-query — with modest, bursty seller workloads you often pay very little.
Concrete setup: Storing Marketing Stream data in BigQuery (S3 → GCP) shows the whole transfer.
Where Snowflake Pulls Ahead #
- Multi-cloud — runs on AWS, GCP, or Azure; useful if you're not committed to Google.
- Separated compute and storage — spin up independent warehouses for different teams or workloads without contention.
- Sustained heavy analytics — for large, constant query volume, dedicated compute can be more predictable than pure pay-per-scan.
- Amazon has a documented path — there's an official Marketing Stream → Snowflake integration if that's your stack.
The Cost Models, Compared #
This is where the two genuinely differ:
- BigQuery — on-demand pricing charges per terabyte scanned, plus cheap storage. Great for bursty, unpredictable workloads; you pay for what you query. Partition and cluster your tables so you're not scanning everything every time.
- Snowflake — you pay for compute time (credits) while a warehouse runs, plus storage. Predictable for steady heavy use; auto-suspend keeps idle costs down.
For a seller running a handful of dashboards and periodic pulls, BigQuery's pay-per-scan usually lands cheaper. For an agency running constant, heavy queries across many clients, Snowflake's dedicated compute can win.
How to Choose #
A quick decision checklist:
- Already on GCP / using Looker Studio? → BigQuery.
- Already on Snowflake, or committed to multi-cloud? → Snowflake.
- Bursty, modest workloads and want minimal ops? → BigQuery.
- Constant heavy analytical load across many accounts? → evaluate Snowflake.
The worst choice is agonizing over it. Both are excellent; pick the one that matches the stack you already run and move on to modeling the data well.
Want the pipeline built either way? I set up Amazon data warehouses on BigQuery and Snowflake with the ingestion, modeling, and dashboards handled.
FAQ #
Which is cheaper for Amazon data, BigQuery or Snowflake?
For most sellers with bursty workloads, BigQuery's pay-per-scan model is cheaper. For constant, heavy query volume (e.g., an agency across many clients), Snowflake's dedicated compute can be more predictable and competitive.
Can I get Marketing Stream data into both?
Yes. Marketing Stream lands in your S3; from there you can route to BigQuery (via GCS/transfer) or to Snowflake (Amazon documents an official path). Both are well-supported.
Do I need a data engineer to run either?
Not to start. BigQuery + Looker Studio is approachable for a technical operator. Heavy modeling and multi-source pipelines are where bringing in help pays off.
Which works best with Looker Studio?
BigQuery — the integration is native and free, which is a big reason it's the default for Amazon sellers who want dashboards non-technical staff can read.
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I set this up for sellers and agencies every week. Book a free 30-minute audit of your Amazon data & PPC setup — you'll leave with a plan either way, whether we work together or not.