The data warehouse is the center of gravity for your analytics stack. Every tool upstream feeds it. Every tool downstream queries it. The choice you make here shapes your transformation patterns, your query costs, and how long your BI tools take to load. Most teams pick Snowflake by default. That's not wrong — but it's often $40K/yr in spend on infrastructure that a $500/mo alternative would handle fine.
There are three dominant patterns today: Pure cloud data warehouses (Snowflake, BigQuery, Redshift) separate storage from compute and charge per query or per second. Lakehouse platforms (Databricks) combine the flexibility of a data lake with warehouse-style query performance, often at better economics for large-scale ML workloads. Specialized OLAP engines (ClickHouse, Firebolt, SingleStore) optimize for specific query patterns — sub-second lookups, event analytics, or real-time ingestion. Each makes different tradeoffs on cost, SQL compatibility, concurrency, and operational overhead.
For most teams under 100GB of data: BigQuery's on-demand pricing will be cheaper than Snowflake. For teams building ML pipelines alongside analytics: Databricks lakehouse unifies the workflows. For teams running user-facing analytics or high-concurrency dashboards: ClickHouse or Firebolt outperform any general-purpose warehouse by an order of magnitude. The warehouse is not a commodity decision — the wrong choice will cost you 3x on query bills or 6 months of migration work.
Snowflake and BigQuery are purpose-built for SQL analytics. Databricks is purpose-built for data engineering and ML. If your primary use case is BI reporting, the warehouse wins. If it's feature engineering and model training alongside analytics, the lakehouse wins. Most teams that moved to Databricks from Snowflake didn't regret it — most teams that did it prematurely did.
Snowflake's credit-based model is easy to budget but can surprise you. BigQuery's on-demand is cheap at low scale but expensive at high query volume. Reserved capacity changes the math — but only if your workloads are predictable enough to justify the commitment.
Snowflake, BigQuery, and Redshift are fully managed but opaque. DuckDB, ClickHouse, and Apache Iceberg-based stacks give you portability and control. The managed tax is real. So is the operational overhead of running your own infrastructure.
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