Snowflake vs Databricks vs BigQuery
| Aspect | Snowflake | Databricks | BigQuery |
|---|
| Primary Use | SQL-first analytics, BI | Spark + ML + lakehouse | Serverless analytics on GCP |
| Storage | Proprietary compressed columnar | Delta Lake on S3/ADLS | Capacitor (columnar) |
| Compute | Virtual warehouses | Spark clusters | Serverless slots |
| ML Support | Snowpark + ML functions | MLflow + Spark MLlib | BigQuery ML |
| Pricing | Per-second + storage | DBU + cloud VM | Per-query (on-demand) or slot-based |
| Best For | BI, SQL analytics, regulated | ML, GenAI, lakehouse | Ad-hoc analytics, GCP shops |
Choose Snowflake when SQL and BI are the priority; Databricks when ML/AI is the priority; BigQuery when you're GCP-native and want serverless simplicity.
Snowflake Salary Impact (India)
| Experience | Without SnowPro (LPA) | With SnowPro (LPA) | Premium |
|---|
| Fresher | ₹8–14 | ₹10–17 | +15–20% |
| Mid (3–6 years) | ₹18–32 | ₹22–42 | +20–30% |
| Senior | ₹40–70 | ₹48–85 | +15–20% |
Common Snowflake Real-World Example — A Snowflake Workflow at a Fintech
Here is how a mid-size fintech uses Snowflake in production:
- Data ingestion: Fivetran loads Stripe, Salesforce, and HubSpot data into raw schema.
- Transform with dbt: dbt models run on Snowflake compute. Mart models in the analytics schema serve BI tools.
- BI serving: Looker connects directly to Snowflake via the JDBC driver. Snowflake's result cache makes dashboards snappy.
- ML workloads: Snowpark runs Python ML notebooks on Snowflake compute — no need to move data to a separate ML platform.
- Data sharing: Snowflake's Secure Data Sharing shares cleaned datasets with partner banks without copying.
- Cost management: Auto-suspend on all warehouses, resource monitors to cap spend, and tag-based cost allocation per team.
Snowflake's separation of storage and compute makes all of this possible at scale.
Pitfalls
Watch out for these traps:
- Leaving warehouses running: Idle warehouses burn credits. Always enable auto-suspend after 60 seconds.
- Oversizing warehouses: Most queries work fine on a Small or Medium warehouse. XL only for the heaviest aggregations.
- Not clustering large tables: Snowflake's automatic clustering handles most cases, but for very large tables (10+ TB), manual clustering or search optimisation may help.
- Storing PII without masking: Use Snowflake's dynamic data masking policies for PII columns. Don't rely on application code alone.
- Neglecting Time Travel: Snowflake retains historical data (1–90 days depending on edition). Use it for quick recovery, but don't rely on it forever.
Quick Reference — Cheatsheet
- Storage and compute scale independently — the killer feature.
- Multiple warehouses can access the same data without contention.
- Native JSON, Avro, Parquet support via VARIANT type.
- Auto-suspend + auto-resume cuts cost 60–80% for intermittent workloads.
- SnowPro Core is the entry certification — Advanced certifications earn 20–30% premium.
Frequently Asked Questions
What is Snowflake?
A fully managed cloud data warehouse on AWS/Azure/GCP. Separates storage from compute. SQL-first. Supports structured + semi-structured data.
How is Snowflake different from a traditional warehouse?
Traditional warehouses need hardware provisioning. Snowflake is fully managed, scales infinitely, and bills per second.
What is Snowflake's unique architecture?
Multi-cluster shared-data. Storage and compute are separated. Multiple compute clusters can access the same data without contention.
Does Snowflake support semi-structured data?
Yes — JSON, Avro, Parquet, ORC. Query semi-structured data with SQL using VARIANT type and dot notation.
How much does Snowflake cost?
Compute ($2–4/credit) + storage ($23–40/TB-month). Small warehouse ~$2–4/hour.
Is Snowflake better than Databricks or BigQuery?
Snowflake: SQL/BI. Databricks: ML/AI. BigQuery: GCP-native serverless.
Fazlur Rahman
