How to Decide on Snowflake over BigQuery in PostgreSQL

Galaxy Glossary

Why choose Snowflake over BigQuery?

Choosing Snowflake over BigQuery offers flexible pricing, cross-cloud support, and zero-copy data sharing.

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Description

Example H2

Example H3

Why would a team pick Snowflake instead of BigQuery?

Snowflake decouples storage and compute on any major cloud, enabling cross-region collaboration without vendor lock-in. Pay-per-second warehouses let you pause compute, lowering idle spend. Native data-sharing eliminates ETL overhead when exposing data to partners.

How does Snowflake’s pricing model differ?

BigQuery9s on-demand plan bills per scanned terabyte, creating cost surprises with large joins. Snowflake bills for actual CPU seconds used by a virtual warehouse, so predictable batch workloads cost less.Auto-suspend avoids charges while queries are idle.

What features give Snowflake an edge for developers?

Time Travel keeps historical table versions for up to 90 days, simplifying rollbacks. Zero-copy Cloning spins up dev environments instantly without duplicating data. JavaScript UDFs and Snowpark allow version-controlled, language-based logic.

Can Snowflake query semi-structured data better?

Both engines support JSON, but Snowflake9s VARIANT column and FLATTEN function avoid defining explicit schemas.Developers can combine structured Orders rows with raw product events in a single SQL statement.

Where does BigQuery still shine?

BigQuery excels for ad-hoc petabyte scans with flat-rate pricing, tight GCP integration, and built-in BI Engine caching. Teams fully invested in GCP may value lower operational work.

Practical comparison example

Snowflake9s multi-cluster warehouses handle hundreds of concurrent dashboard queries, while BigQuery queues if slots are exhausted.For SaaS apps with unpredictable spikes, Snowflake delivers lower latency.

Best practices when migrating

Estimate warehouse sizes using query history to avoid over-provisioning.
Leverage Zero-copy Cloning for parallel QA and staging.
Tag resources and set budgets to track cost per team.

What are common mistakes?

See below.

Why How to Decide on Snowflake over BigQuery in PostgreSQL is important

How to Decide on Snowflake over BigQuery in PostgreSQL Example Usage


-- Snowflake: join structured and semi-structured data for KPI
SELECT c.name,
       SUM(oi.quantity * p.price) AS lifetime_value
FROM Customers c
JOIN Orders o ON o.customer_id = c.id
JOIN OrderItems oi ON oi.order_id = o.id
JOIN Products p ON p.id = oi.product_id
WHERE o.order_date >= DATEADD(year, -1, CURRENT_DATE())
GROUP BY c.name
ORDER BY lifetime_value DESC
LIMIT 10;

How to Decide on Snowflake over BigQuery in PostgreSQL Syntax


-- Snowflake warehouse creation
CREATE WAREHOUSE prod_wh
  WITH WAREHOUSE_SIZE = 'XSMALL'
  AUTO_SUSPEND = 60
  AUTO_RESUME = TRUE;

-- Query semi-structured order events
SELECT o.id, e.value:status AS status
FROM Orders o, LATERAL FLATTEN(input => o.events) e
WHERE e.value:status = 'SHIPPED';

-- BigQuery equivalent (standard SQL)
SELECT id,
       JSON_VALUE(event, '$.status') AS status
FROM `project.dataset.Orders`,
UNNEST(events) AS event
WHERE JSON_VALUE(event, '$.status') = 'SHIPPED';

Common Mistakes

Assuming Snowflake9s smallest warehouse is always cheapest. XS warehouses may finish slower, running longer and costing more. Benchmark workloads before locking size.
Migrating without rewriting cost-heavy SELECT *. BigQuery tolerates wide scans, but Snowflake bills per compute time—select only needed columns to reduce CPU seconds.

Frequently Asked Questions (FAQs)

Is Snowflake more expensive than BigQuery?

It depends on workload. Long, predictable jobs are often cheaper in Snowflake thanks to auto-suspend. Unpredictable petabyte scans can be cheaper in BigQuery9s flat-rate plan.

Can I keep data in GCS and query it from Snowflake?

Yes. Use Snowflake External Tables with a GCS stage. This lets you incrementally migrate while keeping a single query layer.

How hard is it to port SQL from BigQuery to Snowflake?

Most standard SQL transfers directly. Adjust date functions (e.g., DATE_ADD vs DATEADD) and replace BigQuery STRUCTs with Snowflake OBJECT or VARIANT.