Lakehouse in LATAM: Snowflake, Databricks or BigQuery

The three platforms converge but the three remain different. What we see working for mid-sized companies in Colombia, Mexico and Chile.

Three years ago Snowflake was the clean warehouse, Databricks was the ML stack, and BigQuery was for people already on Google Cloud. Today the three chase each other and all end up as lakehouse platforms with serverless compute and a semantic layer. But meaningful differences remain for a mid-sized LATAM company.

Snowflake — when budget is plentiful and the team is small

Operationally the simplest. Storage decoupled from compute, one-click scale, integrates with everything. The price: expensive if you do not optimize warehouses and queries. The learning curve for a traditional DBA is the shortest — pure SQL, no cluster management. For a company with 1–3 data engineers under time pressure, it wins.

Databricks — when the use case includes serious ML

The best option when on top of BI you need feature engineering pipelines, model training and serving. Integrated MLflow, real Unity Catalog, native support for Delta and Iceberg. If the team comes from Spark or wants Python notebooks for analysis, Databricks is native. Cost: more operationally complex, requires understanding clusters.

BigQuery — when you are already on Google Cloud

If your stack is GCP, BigQuery is the default. No cluster management (truly serverless), pricing by bytes scanned (aligned with how data is used), brutal integration with Looker, GA4 and Vertex AI. The trap: cost explodes if nobody controls queries (a SELECT * on a petabyte table costs real money).

What we see in LATAM

For mid-sized companies (50–500 employees) with mixed stacks: Snowflake wins on operational simplicity. For fintechs and ML-focused companies: Databricks. For companies already deep in Google Cloud (Workspace + GA4 + Search Ads): BigQuery without thinking twice. The costly mistake is choosing by hype — we have seen Databricks clients paying 3x what they would on Snowflake because "Databricks is the modern thing".

The equalizer: dbt and Iceberg

What matters is avoiding lock-in. With dbt on top, your business logic lives in versioned SQL and can move between platforms. With Apache Iceberg as the table format, you can read the same data from Snowflake, Databricks or Trino. That reduces the risk of choosing wrong.

How we help at Athrun Data Intelligence

30-min call: we tell you which one fits your current stack and project estimated cost over 12 months. If it helps, we support the migration with a phased plan and per-sprint cost metrics.