Snowflake vs. BigQuery vs. Postgres: when each one wins
Postgres, BigQuery and Snowflake are the three most common answers to "where should our analytics data live?" They solve different problems. Here is when each is the right pick for a small or mid-size company, and when you do not need to decide yet.
Pick an analytics database that is too big and you pay for capacity and complexity you do not use. Pick one that is too small and you hit a wall when the data grows. PostgreSQL, Google BigQuery and Snowflake are the three names that come up most often. They are built on different ideas, and each is the right answer for a different kind of company.
The short version
| PostgreSQL | BigQuery | Snowflake | |
|---|---|---|---|
| What it is | General-purpose relational database | Serverless columnar warehouse on Google Cloud | Cloud warehouse with separate storage and compute |
| You manage | The server (or a managed service does) | Almost nothing | Warehouse sizes and schedules |
| Billing model | The machine you run it on | Per data scanned, or reserved capacity | Compute time plus storage |
| Best fit | Your own app data, moderate volume | Bursty queries, Google-centric stack | Many sources, a data team, heavy daily use |
PostgreSQL: the sensible starting point
Postgres is probably already running your application. For many companies under about 100 people, it is enough for analytics too.
Pick Postgres when:
- Most of the data you analyze is your own application's data.
- The tables are in the millions of rows rather than billions, or the questions are simple aggregations.
- Someone on the team already runs it.
How to make it work for analytics: run reports against a read replica so heavy queries do not slow the app, create views that pre-aggregate the common questions (daily revenue, active customers by month), and index the columns you filter and group by, dates above all.
You have outgrown it when reports take minutes because they scan whole tables, the replica cannot keep up, or you are loading data from many outside tools and the join logic has become a project of its own. Postgres stores data row by row, which is ideal for an app and slower for scanning one column across a very large table.
BigQuery: serverless, pay for what you scan
BigQuery is Google's columnar warehouse. There are no servers or clusters to size. You load data and query it, and by default you pay by the amount of data each query scans, plus storage. Reserved capacity is available when usage becomes steady.
Pick BigQuery when:
- You already use Google Cloud, or much of your data is in Google products. GA4 can export raw events to BigQuery, and Google Ads data can be loaded with Google's transfer service.
- Your querying is bursty: busy at month-end, quiet in between.
- You want a warehouse without deciding on capacity up front.
Watch for: queries that scan whole tables. Select only the columns you need and filter on partitioned date columns; cost follows the bytes scanned. When dashboards query it all day, look at reserved capacity instead of on-demand pricing.
Snowflake: built for a data team
Snowflake separates storage from compute. You create virtual warehouses (compute clusters) of the size you need, they run while queries run and can suspend when idle, and several teams can query the same data without competing for resources. It runs on AWS, Azure and Google Cloud, and has a large ecosystem of loading, modeling and BI tools around it.
Pick Snowflake when:
- You have a data team, or are about to hire one, who will work in the warehouse every day.
- You load data from many sources with an ELT tool and model it with something like dbt.
- You need to run on more than one cloud, or share data with partners.
Watch for: compute left running. Set auto-suspend on every warehouse, size them to the workload, and review usage monthly. Costs grow with how much compute you use, and a busy data team uses a lot.
Three example companies
A 12-person SaaS company with a Postgres app database, Stripe and HubSpot. Stay on Postgres with a read replica and a few views. The billing and CRM data can go straight into a BI tool that connects to those apps.
A 40-person e-commerce brand living in Google Analytics, Google Ads and Sheets, with seasonal reporting peaks. BigQuery fits: the GA4 export lands there for event-level analysis, and bursty querying suits on-demand pricing.
A 200-person company with a data team of four, fifteen sources and dbt models. Snowflake (or BigQuery run the same way) is the natural choice. At this point the warehouse is infrastructure, not an experiment.
Do you need one at all?
Often not yet. If your questions are about data in a handful of SaaS tools and one app database, a warehouse adds pipelines to maintain without adding much. Data warehouses: what they are and when you need one lists the signs that you have outgrown the simpler setup.
How this affects your BI tool
Most BI tools assume a warehouse: they expect modeled tables and query them. clariBI can work with or without one.
- Without a warehouse: it connects directly to the business apps (Stripe, HubSpot and other MCP apps, Google Analytics and Google Ads through Google sign-in, API connectors) and to your database. The PostgreSQL integration uses a read-only user; MySQL, SQL Server and Oracle connect the same way. The PostgreSQL walkthrough and the database connection docs cover the setup.
- BigQuery: the BigQuery connector pulls a table or the result of a SQL query you write, so you can aggregate in BigQuery and keep the scanned bytes down. Rows per sync are capped (5,000 by default), another reason to send summaries rather than raw events.
- Snowflake: there is no direct Snowflake connector today. Export a summary table to CSV and upload it, or put the summary in Postgres or BigQuery. The help article on BigQuery and other data stores lists the options.
For most companies under 50 people, the practical answer is: stay on Postgres, point a BI tool at a read replica and the SaaS tools you use, and add a warehouse when the pain of not having one is clearly bigger than the work of running one.