As every business today is striving to achieve challenging goals through data-driven practices, data management is emerging as a crucial business enabler. The first and
most crucial step in managing a vast amount of data is choosing the right data platform—a decision that directly affects scalability, cost, performance, and analytics.
Talking about the new data platform, two major platforms are dominating discussions today - Databricks and Snowflake. While Snowflake excels with its ease and elastic scaling as a cloud-native data warehouse, Databricks is popular for being a combined platform for big data, AI, and machine learning workloads.
This comparison explores the big differences, performance, cost, and use cases to help you make the right choice based on the needs of your business.

Snowflake Architecture vs. Databricks Architecture - Understanding the Core

Snowflake operates on a shared-data, multi-cluster architecture that separates storage and computing cleanly. This separation enables organizations to scale storage and computing power separately without degrading performance. Different from on- premises data warehouses, the cloud-native architecture of Snowflake removes the
maintenance requirement, enabling organizations to concentrate on data insight and not infrastructure management. It also supports structured and semi-structured data natively, making it possible to run advanced SQL-based analytics. Auto-suspend and auto-scaling capabilities in the platform provide cost efficiency and performance balance, a necessary feature for scaling businesses.
Databricks, built on Apache Spark, takes things in another direction with its lakehouse architecture—a hybrid that blends the flexibility of the data lake with the performance of the data warehouse. This architecture allows organizations to work with raw and processed datasets in a single platform. Databricks also combines Delta Lake to offer ACID transactions, data versioning, and schema enforcement, making the data lakes more reliable, traditionally considered unstructured, and hard to handle. Databricks is the go-to platform for AI, machine learning, and real-time big data analytics with support for a variety of languages, including Python, Scala, R, etc.

Choosing Between Snowflake and Databricks for Enterprise Data

Let us get going with the comparisons on a deeper level – performance, analytics, cost &
security.

Performance & Scalability: Which One Handles Your Workload Better?

Snowflake Performance

Snowflake is designed to handle analytical and business intelligence workloads at scale. Its automatic scaling of compute clusters ensures that workloads are distributed efficiently, maintaining high performance during peak demand.

Key performance-enhancing features include:

• Columnar storage for fast querying.
• Result caching to avoid redundant computations.
• Materialized views for precomputed query responses.
• Adaptive query enhancement for complex queries.

In essence, if your organization runs frequent SQL queries for dashboards, reports, and analytics, Snowflake ensures it executes fast and reliably.

Databricks Performance

Databricks is optimized for large-scale data engineering, AI, and real-time analytics. It leverages in-memory Spark processing to handle massive datasets and complex
computations efficiently.
Delta Lake adds further performance improvements:

• Auto-compaction and indexing for optimized storage.
• Caching and parallel processing for high-speed data pipelines.
• Optimized writes and updates with ACID guarantees.

For organizations working on machine learning pipelines, AI model training, and real-time data streams, Databricks is built to scale and perform without breaking a sweat.

Data Processing & Analytics: Structured vs. AI Workloads

Snowflake: Focused on SQL and BI

Snowflake is built with BI reporting and structured analytics. It can support complex SQL queries, joins, and aggregations and is best for use by BI teams and analysts. It
helps in recovering historical data with its Time Travel feature, which is perfect for auditing and compliance.
However, Snowflake lacks native AI/ML capabilities. While it integrates with external tools for machine learning, it isn't designed for real-time analytics or AI-driven data pipelines.

Databricks: AI, ML, and Big Data Powerhouse

Databricks are designed to consume, process, and analyze massive volumes of structured, semi-structured, and unstructured data. It's enhanced for AI/ML workloads with capabilities including MLflow, offering end-to-end model lifecycle management.
Databricks' Spark Streaming supports real-time data processing to help organizations react to new information in real time—a requirement for finance, e-commerce, and IoT industries.

Cost Efficiency: How They Charge You

Snowflake Pricing Model

Snowflake runs on the consumption-based model, with storage and computing charged separately. Some of the cost-saving features are:

• Pay-per-second billing (only for active computing).
• Auto-suspend and resume to avoid idle charges.
• Multiple editions (Standard, Enterprise, Business Critical) based on feature
  needs.

Snowflake can be cost-effective for data warehousing and BI workloads but may become expensive for continuous big data pipelines.

Databricks Pricing Model

Databricks uses Databricks Units for pricing based on cluster type, configuration, and workload. At the same time, flexible and scalable costs can soar if clusters are poorly optimized.
However, reserved pricing plans offer savings for enterprises with predictable high usage. Databricks are typically more economical for AI, big data, and real-time
processing, provided clusters are managed efficiently.

Security & Compliance: Enterprise-Grade Protection

Snowflake Security

Snowflake offers robust security out-of-the-box:

• Role-Based Access Control.
• End-to-end AES-256 encryption.
• Multi-factor authentication.
• Compliance with SOC 2 Type II, HIPAA, GDPR, and FedRAMP.

The Zero Copy Cloning feature allows secure data duplication without physical replication, minimizing risk.

Databricks Security

Databricks provides enterprise-grade IAM and fine-grained access controls with:

• Data encryption at rest and in transit.
• Collaborative workspaces with tight governance.
• Compliance with SOC 2, GDPR, and HIPAA.
• Best for healthcare and finance sectors, Databricks ensures teams collaborate securely.

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Which One Should You Choose?

Your choice boils down to what your enterprise prioritizes:

• Choose Snowflake if your focus is on structured data analytics, reporting, and BI. Its simplicity, SQL-based interface, and scalable cloud data warehouse make it
  the perfect fit for business intelligence and compliance-heavy industries.
• Choose Databricks if you need AI/ML, real-time analytics, and large data engineering. If your team includes data scientists, ML engineers, and AI practitioners, Databricks' lakehouse method will handle the complexity and size of modern data workflows.

Parkar's Take: Helping You Choose Wisely

At Parkar, we have expertise in assisting companies make intelligent technology decisions. From designing a powerful data warehouse to rolling out Artificial intelligence-based applications, we assist you in aligning platform options with your business goals, budget, and future growth requirements.
Ready to transform your data ecosystem?
Connect with Parkar today for a tailored strategy that prepares you for success.

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