How to switch from Snowflake to BigQuery

What it takes to switch from Snowflake to BigQuery

Switching from Snowflake to BigQuery means rewriting SQL for a different dialect, moving your data out through Snowflake's unload commands, and rebuilding your pricing model from credits to either pay-per-scan or reserved slots. Google's own BigQuery Migration Service handles the SQL translation and data transfer, and it's free to use beyond normal storage costs. The real work is deciding whether your workload actually fits BigQuery's pricing better than Snowflake's, because for some teams it won't.

This is a real switch, not a lateral move. Snowflake and BigQuery both separate storage and compute, but they price it differently and they run on different clouds. Read the pricing gap first, because that's usually what forces the decision either way.

The pricing model is the real difference

Snowflake bills compute in credits consumed per second by your virtual warehouses, plus storage per TB per month, and it doesn't publish per-credit dollar rates. Rates vary by edition (Standard, Enterprise, Business Critical, VPS), cloud provider, and region, so you need Snowflake's calculator or a sales quote to know your real number. There's no free tier, only a 30-day trial with $400 in credits.

BigQuery bills the same two things, storage and compute, but publishes actual numbers on its pricing page. On-demand queries cost $6.25 per TiB of data scanned, and the first 1 TiB scanned each month is free. Storage runs about $0.023 per GiB per month for active data and about $0.016 for data untouched 90+ days, with the first 10 GiB free. If your query volume is steady and high, BigQuery Editions let you buy dedicated slot capacity instead: Standard starts at $0.04 per slot-hour, Enterprise at $0.06, and Enterprise Plus at $0.10, each cheaper with a 1- or 3-year commitment.

The practical difference: BigQuery's on-demand model charges for what a query actually scans, with no warehouse sitting idle and billing while nobody queries it. Snowflake charges for warehouse uptime by the second, so a warehouse that isn't set to auto-suspend keeps burning credits. Teams with spiky, unpredictable query patterns often see a real drop in bill moving to on-demand BigQuery. Teams running constant, heavy query volume may find slot-based BigQuery Editions cost about the same as a well-tuned Snowflake warehouse, since both are still usage-billed once you commit.

If you want to see how BigQuery stacks up against the rest of the field before committing, the Snowflake alternatives hub ranks it alongside Databricks, Redshift, Dremio, and Starburst on the same criteria.

What actually needs to move

Three things change when you switch: your SQL, your pipelines, and your access model.

SQL dialect. Snowflake SQL and BigQuery's GoogleSQL are both ANSI-based but diverge on functions, semi-structured data handling, and scripting syntax. Every stored procedure, view, and scheduled query written against Snowflake needs a rewrite or a translation pass. Google runs a BigQuery Migration Service for exactly this: a batch translator, an interactive translator, and an API that convert Snowflake SQL into GoogleSQL, plus a data validation tool to confirm the migrated tables match the source. It's free beyond the storage cost of the files you feed it.

Getting data out. Snowflake exports data with the COPY INTO <location> command, unloading tables to CSV, JSON, or Parquet, staged in Amazon S3, Google Cloud Storage, Azure, or a Snowflake internal stage. For a BigQuery move, unloading to Google Cloud Storage as Parquet is the more direct path, since Parquet preserves numeric precision that CSV and JSON unloads truncate to about 15 digits. From GCS, BigQuery's native load jobs or the Data Transfer Service pick the data up.

Pipelines and BI tools. Anything pointed at Snowflake, dbt models, Fivetran syncs, Airflow DAGs, Tableau or Looker connections, needs a new connection string and often a rewritten model layer if it leans on Snowflake-specific SQL. BigQuery has its own native pull from Google Analytics 4 and Google Ads through the Data Transfer Service, which can replace some pipeline steps outright if those are sources you already use.

None of this is a weekend project. Budget for a parallel-run period where both warehouses receive data and queries get validated against both, before you cut over reporting and cancel the Snowflake contract.

When to stay on Snowflake

Don't switch if the reason is only "BigQuery's pricing page is easier to read." That's real, but it's not enough on its own. Stay on Snowflake if you're running Apache Iceberg tables natively across governance editions, using Cortex AI functions and Time Travel together in one platform, or if you need to run the same warehouse across AWS, Azure, and GCP because you're genuinely multi-cloud. Snowflake still covers all three clouds; BigQuery only runs on GCP, so switching gives up that portability for good.

Also stay if your team already tunes Snowflake warehouses well, auto-suspend is on, sizes match load, and your bill is predictable. The switch cost, in engineering time and dialect rewrite risk, usually isn't worth it just to chase a lower theoretical rate you might not actually hit once you learn BigQuery's own cost traps, like an unfiltered query scanning a full table.

Who should actually make the move

Make the switch if you're already deep in the Google ecosystem, Google Analytics 4, Google Ads, or Google Cloud infrastructure generally, since native connectors remove pipeline work Snowflake can't match. Also make it if your query pattern is genuinely spiky: on-demand BigQuery bills nothing when nobody's querying, where an always-on Snowflake warehouse keeps a bill running. And make it if you want published, self-serve pricing you can check against your own numbers instead of a quote-only rate card.

Whatever you pick, verify the published rates yourself before committing. Both vendors' pages are dated and usage-based, and your actual bill depends entirely on how your team writes queries and sizes compute, not the sticker rate.

FAQ

Does Google provide a tool to migrate SQL from Snowflake to BigQuery? Yes. The BigQuery Migration Service includes a batch translator, an interactive translator, and an API that convert Snowflake SQL into GoogleSQL, along with a data validation tool to check migrated tables against the source. The service itself is free; you pay standard storage fees for the input and output files.

How do I get data out of Snowflake to load into BigQuery? Snowflake unloads data with the COPY INTO <location> command, exporting to CSV, JSON, or Parquet, staged in Amazon S3, Google Cloud Storage, Azure, or a Snowflake internal stage. For a BigQuery migration, unloading to Google Cloud Storage as Parquet avoids the numeric precision truncation that CSV and JSON unloads have.

Is BigQuery cheaper than Snowflake? It depends on your workload. BigQuery publishes actual rates, $6.25 per TiB scanned on-demand with the first 1 TiB free monthly, or $0.04-$0.10 per slot-hour on Editions. Snowflake doesn't publish per-credit rates at all, so a direct comparison needs your own usage numbers run through both a Snowflake quote and the BigQuery pricing calculator. Spiky, unpredictable workloads tend to cost less on BigQuery's on-demand model; steady high-volume workloads may land close to the same once both platforms are tuned.

Can I run BigQuery and Snowflake side by side during a migration? Yes, and it's the safer approach. Keep both warehouses receiving data during a parallel-run period, validate query results and dashboards against both, and only cut over reporting and cancel the Snowflake contract once BigQuery's numbers are confirmed to match.