BigQuery or ClickHouse for Your First Warehouse?

The Decision Is Really About Three Things

Warehouse choices get argued as technology comparisons and are actually decisions about team shape, query patterns, and cost structure. BigQuery and ClickHouse are both competent systems; they fail differently, and the failure mode that hurts your team is the one to optimise against.

Three questions settle it: how predictable is your query volume, how fresh does the data need to be, and who runs the infrastructure. If the answers are low, daily, and nobody, BigQuery is the answer. If they are high, near-real-time, and there is a data engineer who wants to own it, ClickHouse earns a serious look.

How the Cost Models Diverge

BigQuery charges for bytes scanned by each query, plus storage. That is elegant at low volume: an idle warehouse costs nothing beyond storage, and a team experimenting freely pays a few euro a month. It punishes careless query design at scale: wide scans over unpartitioned tables on a frequent schedule get expensive in a way that shows up on the invoice before it shows up in anyone's workflow.

ClickHouse pricing is the inverse: you provision compute, so cost is predictable and largely independent of query shape. Fast analytical queries against large event tables are its best case. The cost sits in the provisioning: someone manages the servers or the Kubernetes deployment, upgrades happen on your schedule, and a badly sized cluster is money spent continuously whether the queries run or not.

• BigQuery: pay per query, zero ops, cost scales with query discipline.
• ClickHouse: pay per provisioned compute, ops required, cost scales with capacity you choose.

Query Patterns: Where Each One Wins

ClickHouse is built for high-speed aggregation over large event tables: sessionised clickstream, product analytics, operational monitoring. If your primary question is how many users did X in the last seven minutes, sliced forty ways, it is the natural fit and will feel dramatically faster than a row-based engine.

BigQuery wins on flexibility and join-heavy work: attribution models that join sessions to orders to campaign tables, financial reconciliations, ad hoc exploration by analysts who know SQL but not operations. It is also where the rest of the marketing data ecosystem already speaks fluently: Looker Studio, dbt, GA4 exports, and the server-side tagging ecosystem all land in BigQuery first.

The Skills and Operations Reality

For a first warehouse, the scarcest resource is usually not money or performance: it is the attention of the two or three people who could build it. BigQuery removes a category of ongoing work entirely. There is no cluster to size, no upgrade window to plan, and no paging at 3am for a node that filled its disk.

ClickHouse asks for that operational commitment up front and pays it back in query speed and cost predictability once volume justifies it. The teams who regret ClickHouse are almost never the ones who chose it deliberately with an engineer who wanted to own it; they are the ones who were sold on benchmarks and then had nobody to run it.

A Real Decision Shape

On an eCommerce attribution project we ran, the data volume was a mid-size retailer's: months of GA4 server-side events, ad platform spend, and order lines, joined for full-path attribution. The query mix was broad, the consumers were analysts and a Looker Studio board, and the team had no one who wanted to run infrastructure. BigQuery was the obvious home, and the same warehouse later fed the custom attribution model without a migration.

The point is not that BigQuery is always right. It is that at first-warehouse scale, the flexibility and zero-ops profile usually beats the raw query speed that ClickHouse would bring, because the queries that matter at that stage are join-heavy and irregular rather than narrow and constant.

When to Revisit

Revisit the decision when one of three things becomes true: a specific workload is predictable enough and frequent enough that provisioned compute would clearly beat on-demand; sub-second latency on fresh events becomes a product requirement rather than a nice-to-have; and you have the engineer who will own the platform. Until then, the migration risk of switching later is a better trade than the operational risk of adopting ClickHouse early.