Snowflake vs Databricks: Data Platform Showdown

Snowflake and Databricks are converging — Snowflake is adding data engineering and ML, Databricks is adding SQL analytics. But their DNA is different, and that DNA shapes where each platform excels. Choosing between them isn’t about which is “better” — it’s about which one aligns with your primary workload, team skills, and data strategy.

This guide provides an honest, workload-by-workload comparison so you can make the right platform decision for your organization.

Architecture DNA

Aspect	Snowflake	Databricks
Origin	Cloud data warehouse (SQL-first)	Apache Spark + Delta Lake (engineering-first)
Core strength	SQL analytics, ease of use, BI integration	Data engineering, ML, streaming
Storage format	Proprietary (micro-partitions)	Open (Delta Lake / Parquet / Iceberg)
Compute	Virtual warehouses (T-shirt sizing, auto-suspend)	Clusters (configurable node types and counts)
Data sharing	Snowflake Marketplace (native, zero-copy)	Delta Sharing (open protocol)
Lock-in risk	Medium (proprietary format, expensive egress)	Low (open formats, exportable)
Learning curve	Low (SQL knowledge sufficient)	Medium (Python/Spark required for full value)

What “Open Formats” Mean in Practice

Databricks stores data as Parquet files on your cloud storage (S3, ADLS, GCS). If you leave Databricks, your data stays. Snowflake stores data in a proprietary format — leaving requires exporting everything, which is slow and expensive at scale.

Workload Comparison

SQL Analytics & BI

Feature	Snowflake	Databricks SQL
SQL compatibility	Excellent (ANSI SQL, extensive functions)	Good (growing rapidly, some gaps)
BI tool integration	Native (Tableau, Power BI, Looker, dbt)	Good (JDBC/ODBC, improving)
Concurrency	Excellent (multi-cluster warehouses, auto-scale)	Good (SQL warehouses, serverless)
Semi-structured data	Native VARIANT type (best-in-class)	Good (JSON functions)
Time travel	Up to 90 days (Enterprise+)	Up to 30 days (Delta Lake)
Query optimization	Automatic clustering, search optimization	Photon engine, Z-ordering
Winner	✅ Snowflake

Data Engineering & ETL

Feature	Snowflake	Databricks
Streaming support	Snowpipe (micro-batch, ~1 min latency)	Structured Streaming (true streaming, sub-second)
Language support	SQL, Snowpark (Python/Java/Scala)	Python, SQL, Scala, R, Java
Notebook experience	Snowsight (basic, improving)	Excellent (collaborative, Git-integrated)
Orchestration	Tasks + Streams (basic DAGs)	Workflows (full DAG orchestration, retry, dependencies)
Complex transformations	SQL-focused (Snowpark adds Python)	Spark DataFrames (more flexible for complex logic)
Data quality	Basic constraints	Built-in expectations, Delta Live Tables
Winner		✅ Databricks

Machine Learning

Feature	Snowflake	Databricks
ML training	Snowpark ML (limited algorithms)	MLflow + Spark ML (comprehensive, production-grade)
Deep learning	Limited (no native GPU support)	Native GPU clusters (A100, H100)
Feature store	Basic	Built-in Feature Store (online + offline)
Model serving	Snowflake ML (preview/limited)	Model Serving (production endpoints, auto-scaling)
LLM support	Cortex AI (managed, easy)	Foundation Model APIs + fine-tuning (more flexible)
Experiment tracking	None native	MLflow (industry standard)
Winner		✅ Databricks

Pricing Comparison

Snowflake

Cost = Compute (credits/hour) + Storage ($/TB/month)

Compute pricing per credit:
- Standard:          $2/credit
- Enterprise:        $3/credit
- Business Critical: $4/credit

Warehouse sizes (credits/hour):
- XS:  1 credit/hr    → $2-4/hr
- S:   2 credits/hr   → $4-8/hr
- M:   4 credits/hr   → $8-16/hr
- L:   8 credits/hr   → $16-32/hr
- XL:  16 credits/hr  → $32-64/hr
- 4XL: 128 credits/hr → $256-512/hr

Storage: $23-40/TB/month (depending on cloud/region)
Auto-suspend: Warehouse pauses after idle period (saves $$$)

Databricks

Cost = DBU (Databricks Units) + Cloud Compute + Storage

DBU pricing by workload:
- Jobs Compute:        $0.10-0.30/DBU  (cheapest, for scheduled jobs)
- SQL Compute:         $0.22-0.55/DBU  (for analytics queries)
- All-Purpose Compute: $0.40-0.65/DBU  (for interactive development)

Cloud compute: Underlying VM cost (AWS/Azure/GCP pricing)
Storage: Cloud-native pricing (S3: ~$23/TB, ADLS: ~$21/TB)
Serverless SQL: Pay per query (no idle costs)

Cost Comparison: 10TB Warehouse, 8 Hours/Day Analytics

Component	Snowflake (Enterprise)	Databricks SQL
Compute (monthly)	~$5,000	~$4,500
Storage (monthly)	$300	$230 (open format, cheaper tiers)
Total	~$5,300	~$4,730

Note: Actual costs vary significantly based on query complexity, concurrency, instance types, and cloud region.

Hidden Cost Factors

Factor	Snowflake	Databricks
Auto-suspend savings	✅ Excellent (built-in, granular)	⚠️ Manual (cluster auto-terminate)
Idle cluster cost	Zero (auto-suspend)	Can be expensive if not configured
Data egress	$$$ (proprietary format export)	Low (open format, your storage)
Serverless option	Yes (but premium pricing)	Yes (SQL Serverless, cheaper idle)
Enterprise features	Per-edition pricing jump	Unity Catalog included in Premium

Governance & Security

Feature	Snowflake	Databricks
Access control	Role-based (RBAC)	Unity Catalog (ABAC + RBAC)
Data masking	Dynamic data masking (Enterprise+)	Column-level masking
Row-level security	Row access policies	Row-level filters
Audit logging	Access History (Enterprise+)	Unity Catalog audit logs
Data lineage	Built-in (Enterprise+)	Unity Catalog lineage
Compliance	SOC 2, HIPAA, PCI, FedRAMP	SOC 2, HIPAA, PCI

Decision Framework

Primary workload is SQL analytics / BI?
├── Yes → Snowflake (best SQL experience, BI integration)
└── No
    ├── Heavy data engineering (Spark, streaming)?
    │   └── Databricks (native Spark, true streaming)
    ├── ML/AI is a primary use case?
    │   └── Databricks (MLflow, GPU clusters, feature store)
    └── Need open data formats (no vendor lock-in)?
        └── Databricks (Delta Lake, Parquet, your storage)

Team is SQL-only (no Python/Spark)?
└── Snowflake (gentler learning curve)

Already invested in one platform?
└── Expand before switching (migration is expensive and risky)

Budget is the primary constraint?
└── Compare total cost including hidden factors (egress, idle, features)

Decision by Company Profile

Company Type	Recommendation	Why
BI-heavy enterprise (analysts >> engineers)	Snowflake	SQL-first, easy onboarding, BI integration
Data engineering shop (many pipelines)	Databricks	Spark, notebooks, streaming, orchestration
ML/AI company	Databricks	GPU, MLflow, feature store, model serving
Startup (small team, SQL focus)	Snowflake	Simpler, faster time-to-value
Multi-cloud strategy	Databricks	Open formats, less lock-in
Both BI + engineering heavy	Both (seriously)	Snowflake for BI, Databricks for engineering

The Lakehouse Convergence

Both platforms are converging on the “lakehouse” architecture — combining data lake flexibility with data warehouse performance.

Feature	Snowflake (adding)	Databricks (adding)
Data engineering	Snowpark, Dynamic Tables	✅ Native (Spark, Delta Live Tables)
SQL analytics	✅ Native (best-in-class)	Databricks SQL (Photon engine, improving)
ML/AI	Cortex, Snowpark ML	✅ Native (MLflow, GPU, model serving)
Governance	Horizon (new)	Unity Catalog (mature)
Open format support	Iceberg support (external tables)	✅ Delta Lake native + Iceberg/Hudi
Real-time streaming	Snowpipe Streaming	✅ Structured Streaming

Migration Considerations

Factor	Snowflake → Databricks	Databricks → Snowflake
Data migration	Export CSV/Parquet → load to Delta	Parquet → COPY INTO (straightforward)
SQL migration	Minor syntax differences	More significant (Spark SQL → Snowflake SQL)
Pipeline migration	Rewrite to Spark/Python	Rewrite to SQL/Snowpark
Timeline	2-6 months for major workloads	2-6 months for major workloads
Risk	Medium (format change, SQL differences)	Medium (tool change, learning curve)

Checklist

:::note[Source] This guide is derived from operational intelligence at Garnet Grid Consulting. For data platform consulting, visit garnetgrid.com. :::