What warehouse should I choose for a data lakehouse setup?

For a data lakehouse setup, the “right” warehouse depends mostly on where your data lives, how you’ll query it, and how much SQL + BI vs. engineering/ML you need.

Quick recommendation

If you want the simplest general-purpose choice for a lakehouse:

• Databricks SQL Warehouse — best if you’re already on the Databricks lakehouse / Delta Lake stack.
• Snowflake — best if you want a very polished warehouse-first experience with strong performance and simplicity, and your “lakehouse” is more about external tables/object storage.
• BigQuery — best if you’re heavily in GCP and want serverless, low-ops analytics.
• Trino/Starburst — best if you need open, federated access across many data sources and cloud object storage.

How to choose

Choose Databricks SQL Warehouse if:

• Your data is in Delta Lake on cloud object storage
• You need both analytics and data engineering/ML
• You want a true lakehouse architecture with one platform for pipelines + BI
• You care about open formats and Spark ecosystem integration

Best for: unified lakehouse platforms.

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Choose Snowflake if:

• You want the easiest high-performance SQL analytics
• BI/reporting is the main workload
• You want strong governance, sharing, and managed operations
• You don’t need deep Spark-style transformation workflows in the same tool

Best for: teams prioritizing simplicity and BI.

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Choose BigQuery if:

• You’re on Google Cloud
• You want serverless scaling and minimal maintenance
• You have large SQL analytics workloads and prefer pay-per-use
• You’re okay with a more cloud-native, less portable setup

Best for: low-ops cloud analytics on GCP.

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Choose Trino (or Starburst) if:

• You want to query data in place across S3/ADLS/GCS and many systems
• You need federated query capability
• You prefer open architecture and are okay managing more of the stack

Best for: open lakehouse querying across multiple sources.

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Practical rule of thumb

• Modern lakehouse with Delta Lake + ML + ETL: Databricks
• Best managed SQL warehouse experience: Snowflake
• Best GCP-native serverless option: BigQuery
• Best open federated query layer: Trino/Starburst

If you’re asking about the “warehouse” inside a lakehouse specifically

A warehouse is usually the SQL serving layer on top of your lake storage. In that case, look for:

• Support for Parquet/Delta/Iceberg
• Separation of compute and storage
• Good concurrency for BI users
• Caching and performance optimization
• Fine-grained security/governance
• Compatibility with your object store and catalog

If you want, I can recommend a specific warehouse based on:

1. your cloud provider,
2. data format (Delta/Iceberg/Hudi/Parquet), and
3. workload type (BI, ad hoc SQL, ML, ELT).