Databricks vs Snowflake: Which Is Better for Data Science?

Every modern company wants to call itself “data-driven,” but the real challenge begins when the data keeps growing, users keep scaling, and business questions don’t stop.

A bank wants fraud detection in real time.

A streaming platform wants smarter recommendations.

A retail brand wants instant sales dashboards across hundreds of stores.

To power all this, two platforms dominate the conversation today: Databricks and Snowflake.

They’re both strong, both popular, and both appear in job descriptions everywhere. They are designed for different purposes.

This guide breaks down the differences using real examples, easy comparisons, and practical benchmarks so you can confidently choose the right platform for your data science needs.

The competitive dynamic between Databricks and Snowflake has evolved from a simple rivalry to one of frequent co-adoption, with over 60% of Databricks customers also using Snowflake (Source: ETR/Analyst Surveys). 

Understanding Databricks: Core Capabilities for Data Science

Databricks is a unified lakehouse platform that combines data engineering, analytics, and AI/ML capabilities. Built on Apache Spark, Databricks simplifies processing large-scale datasets while supporting complex ML workflows.

Key Features of Databricks

• Delta Lake: Ensures data reliability, ACID transactions, and version control for streaming and batch data.
• MLflow: Tracks machine learning experiments, models, and deployment pipelines.
• Collaborative Notebooks: Supports Python, R, SQL, and Scala, allowing teams to collaborate in real-time.
• Real-time Streaming: Enables AI and ML on live data streams from IoT devices, logs, or events.
• Auto-scaling Clusters: Automatically adjusts resources for workload demand.

Use Cases for Databricks:

• Predictive analytics and recommendation engines
• Real-time anomaly detection
• Large-scale feature engineering for ML models
• Training deep learning models on distributed datasets

Databricks excels when machine learning and AI workloads are the primary focus, especially when dealing with unstructured or semi-structured data.

The global “data lakehouse” market the space in which lakehouse‑style platforms (like Databricks) and modern data‑platform architectures compete was estimated at USD 11.35 billion in 2024, and is forecast to grow to USD 74.00 billion by 2033, at a CAGR of ~23.2%. (Source: Grand View Research)

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Understanding Snowflake: Core Capabilities for Data Science

Snowflake is a cloud-native data warehouse optimized for analytics, SQL queries, and business intelligence. Snowflake’s architecture separates compute and storage, providing scalable performance for structured data analytics.

Key Features of Snowflake

• Virtual Warehouses: Independent compute clusters enable multiple workloads without resource contention.
• Snowpark: Allows developers to run Python, Java, and Scala code inside Snowflake for data transformations and ML workflows.
• Cortex: Provides AI/ML capabilities, including integrations with large language models (LLMs).
• Secure Data Sharing: Enables organizations to share datasets without moving or copying data.
• Time Travel & Zero-Copy Cloning: Maintain historical data snapshots and clone datasets efficiently.

Use Cases for Snowflake:

• SQL-based analytics and reporting
• Business intelligence dashboards
• Feature engineering with in-database transformations
• Secure multi-organization data collaboration

Snowflake is ideal for analytics-heavy workloads and structured datasets, providing speed and governance without the complexity of ML infrastructure.

As of 2024, Snowflake reportedly had 10,618 customers, including 800+ of the Forbes Global 2000 companies, and processed 4.2 billion daily queries.

Databricks vs Snowflake: Architecture Differences

The underlying architecture of Databricks and Snowflake defines how they handle data and workloads.

Insight: Databricks’ lakehouse architecture provides flexibility for ML pipelines and unstructured data, while Snowflake’s warehouse architecture offers consistent, fast query performance for structured datasets.

In a strategic white‑paper comparing top platforms, estimated market‑share (not just mindshare): Databricks ~15.18%, Snowflake ~20.21%.

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Data Science Workflow Comparison

Databricks

• End-to-end ML lifecycle: data ingestion → cleaning → training → deployment
• Distributed processing for large-scale datasets
• Real-time streaming for live insights
• Integration with TensorFlow, PyTorch, and scikit-learn

Snowflake

• In-database ML using Snowpark
• Supports feature engineering and predictive analytics
• Integrates with external MLOps platforms
• Optimized for batch processing rather than real-time streaming

Key Difference: Databricks is a better choice for machine learning-heavy workflows, whereas Snowflake is suited for analytics-heavy and BI workflows.

Performance and Scalability of Databricks & Snowflake

Databricks: Handles high-volume distributed data processing. Ideal for AI/ML workloads, streaming, and unstructured datasets. Auto-scaling clusters ensure optimal resource utilization.

Snowflake: Provides fast query execution on structured datasets. It separates compute and storage, allowing multiple teams to run concurrent workloads without slowing down.

Use Case Comparison:

• Large ML model training → Databricks
• Complex analytics queries across multiple tables → Snowflake

Practical Benchmarks:

• ML Model Training (1TB data) → Databricks completes in ~3 hours; Snowflake would require export + external compute (~6–10 hours).
• Complex Multi-Table Analytics (50M rows) → Snowflake averages 2.5s per query; Databricks ~8–10s on similar cluster.

Pricing Comparison of Databricks & Snowflake

Databricks:

• Pay-per-cluster compute and storage
• Auto-scaling reduces idle resource costs
• Ideal for ML-heavy workloads with intermittent resource usage

Snowflake:

• Credit-based consumption pricing (compute + storage)
• Efficient for frequent SQL queries and BI dashboards
• Cost-effective for analytics-heavy organizations

Takeaway: ML-heavy teams may prefer Databricks despite higher complexity, while analytics-focused teams may benefit from Snowflake’s predictable pricing.

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Tools & Integrations for Databricks and Snowflake

Both platforms integrate with a wide range of BI, ML, and cloud tools:

• BI Tools: Tableau, Power BI, Looker
• ML Frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost
• Cloud Ecosystems: AWS, Azure, GCP
• Data Engineering Tools: Apache Airflow, dbt, Spark

Advantage: Databricks offers native ML tooling for experiment tracking and distributed processing, while Snowflake focuses on in-database transformations and secure data sharing.

Governance, Security & Data Sharing of Databricks and Snowflake

Databricks:

• Unity Catalog for data lineage, governance, and access control
• Supports role-based access and compliance standards

Snowflake:

• Strong governance suite, including data masking and secure sharing
• Enables multi-org collaboration without moving data

Both platforms provide enterprise-grade security, but Snowflake excels in data sharing and compliance for structured datasets.

Strengths and Weaknesses of Databricks and Snowflake

Real-world Examples Using Databricks

Here’s a detailed list of real-world examples of companies using Databricks, showing practical use cases for data science, AI, and analytics:

Bayer - Consumer‑Health Division

Bayer uses Databricks as its “Data Intelligence Platform” to build reusable data assets and scalable data products. This supports large‑scale analytics, ad‑hoc queries, and AI/ML workflows across the organization.

Barclays - Fraud / FinCrime Monitoring

Barclays reportedly leverages the Databricks lakehouse for its financial‑crime monitoring frameworks, benefiting from both data flexibility and reliability (ACID transactions, scalable processing) to analyze transactional data at scale.

Rivian (Electric Vehicle Manufacturer)

Rivian uses Databricks to drive several capabilities: cybersecurity (real‑time and scheduled event detection), vehicle‑performance monitoring, predictive maintenance (energy/charge profiling), and data‑driven diagnostics enabling a scalable, AI‑powered backend for their vehicles.

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Real-world Examples Using Snowflake

Here’s a clear, detailed list of real-world examples using Snowflake, highlighting practical applications and outcomes:

Honeysuckle Health - Healthcare / Data‑Sharing Use Case

Honeysuckle moved large datasets into Snowflake where their analytics team does transformations and reporting. They now use Snowflake both as a secure data lake/warehouse and as a feature store for data science enabling secure, multi‑organization data sharing and analytics without deploying separate infrastructure.

Pfizer - Large‑Scale Analytics

With Snowflake (via Snowpark), Pfizer reportedly improved analytics performance (processing/data workflows) and reduced total cost of ownership, making large scale analytics and reporting more efficient.

Petco - Retail/Consumer Goods

Petco moved analytics workloads to Snowflake; as a result, they achieved ~50% faster data processing and their data‑science teams gained ~20% efficiency boost, demonstrating improvements in both performance and team productivity.

Final Verdict: Which Is Better for Data Science?

Databricks is the preferred choice for AI/ML workloads, distributed computing, and unstructured data analysis.

Snowflake excels in analytics-heavy workflows, secure data sharing, and SQL-based reporting.

Ultimately, the choice depends on your team’s focus, budget, and data type. Many enterprises use both platforms together: Snowflake for analytics and Databricks for ML pipelines, achieving a hybrid solution.

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