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Snowflake, BigQuery & Databricks · Choosing and Comparing

Snowflake vs BigQuery vs Databricks

Easywarehouses-53
comparisonsnowflakebigquerydatabricksplatform-choice

Question

How would you compare Snowflake, BigQuery and Databricks?

Solution

All three run analytics on cloud storage with separated compute, but they started from different places. Snowflake is a SQL-first data warehouse, BigQuery is Google's serverless warehouse, and Databricks is a Spark-based lakehouse platform built on open files.

Snowflake

You work mostly in SQL. Compute is organised as virtual warehouses that you size and suspend, and cost is credits per second of running time. Strong points are ease of use, workload isolation, data sharing between accounts, and a broad ecosystem of BI and data tools. It runs on AWS, Azure and GCP.

BigQuery

Fully serverless. There are no clusters to size. You pay per bytes scanned, or buy slots. It is deeply integrated with the rest of Google Cloud, including Pub/Sub, Dataflow, Vertex AI and Looker. Strong points are simplicity at large scale, and built-in streaming and ML functions. It is tied to GCP, apart from Omni for reading other clouds.

Databricks

Built on Apache Spark and open formats (Delta, Iceberg, Parquet) in your own cloud storage. It is strong for data engineering with code, streaming, and machine learning, and has notebooks, jobs and Unity Catalog for governance. It also has Databricks SQL, a warehouse-style SQL engine. It runs on AWS, Azure and GCP.

They are converging

  • Snowflake added Python (Snowpark), ML features, Iceberg tables and streaming ingestion.
  • Databricks added a serverless SQL warehouse, governance and BI-friendly features.
  • BigQuery added Spark procedures, notebooks, BigLake and Iceberg support.
  • All three add AI features and support open table formats.

How to choose

There is rarely a universal winner. Consider:

  • The cloud you already use and where the data lives.
  • Skills: a SQL-heavy analyst team is quick on Snowflake or BigQuery. A team that writes lots of Python and Spark, or does ML, may prefer Databricks.
  • Workloads: BI and SQL analytics, heavy ELT, streaming, or ML training.
  • Cost model: per-second compute you manage, per-byte scanning, or DBUs plus VMs. The cost depends on workload shape.
  • Governance, sharing and compliance needs.

In an interview, avoid declaring a favourite. Say what each is best at, then ask what the requirements are.

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