Google BigQuery
Why Google BigQuery
BigQuery is Google Cloud's serverless data warehouse. You load data into it and query terabytes with SQL or Python without managing infrastructure, with machine learning and streaming ingestion built in. It suits companies whose data has outgrown spreadsheets and the reporting inside their individual apps.
What it does
BigQuery is a serverless data warehouse on Google Cloud. You store data in tables, then query it with SQL or Python. There are no servers or clusters to size, because Google runs the infrastructure.
It handles structured and unstructured data, supports open table formats such as Apache Iceberg, Delta and Apache Hudi, and takes in streaming data for continuous analysis. It also has machine learning built in through BigQuery ML, and governance features for data discovery and access. BigQuery Omni lets you query data that sits in AWS and Azure regions.
Why you would need it
The pain starts when a simple question needs data from three places. Revenue lives in your billing tool, leads in your CRM and behaviour in your product analytics. Each tool shows its own slice, and nobody can join them without a messy export.
A warehouse gives you one place to land everything, and SQL gives you a way to ask any question across it. You feel the change the first time you answer a board-level question in minutes instead of days.
Where it fits
BigQuery sits in the middle of a data stack. It is fed by exports from your analytics, ad platforms, CRM and product events, often through a loader or event pipeline. It feeds dashboards and reports, for example in Looker Studio, and it can feed models and agents.
It replaces the big spreadsheet and the folder of CSV exports that someone updates by hand.
What stands out
- A free usage tier. The first 1 TiB of query data processed each month and the first 10 GiB of storage are free.
- A sandbox you can try without a credit card, with tables that expire after 60 days.
- Two compute pricing models: pay for the data your queries scan, or buy capacity in slots for a steadier bill.
- A remote MCP server from Google, so AI assistants can run queries and read metadata.
My take
I'd pick BigQuery when you already use Google Cloud or Google's marketing stack, and your data volume or your question complexity has outgrown a spreadsheet. The free tier lets you start without a procurement conversation.
I'd skip it for a small company with a few thousand rows. A hosted Postgres database or the reporting inside your tools is simpler. What I'd watch is cost control, because on-demand billing follows the bytes your queries scan, and a careless query on a large table is expensive.
Verdict
Pick it when you need a central warehouse and have someone who can write SQL. Skip it when your data fits in a spreadsheet or a small database.
Notes
Your note
Before you choose
Cost depends on how you query
On-demand billing follows the data your queries scan. Select only the columns you need, partition large tables and set the maximum bytes billed on a query so one mistake cannot run up a large bill.
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You need SQL skills and an owner
BigQuery does not model your data for you. Someone has to decide table structure, naming and who may see what. Without an owner it becomes a dumping ground.
The sandbox is for learning
The free sandbox limits storage and expires tables after 60 days. Use it to learn, then link a billing account for anything real.
Smaller alternatives
At small scale, a hosted Postgres database such as Neon or Supabase is often enough. Compare those and other warehouses such as Snowflake before you commit to Google Cloud.