[Volume.11 Cloud Data Platform Revolution - Snowflake Inc.]

Paul
Sep 15
14 min read

Snowflake Continual is the Operational AI layer for the Modern Data Stack

Snowflake Inc. - Pioneer of the AI Data Cloud revolutionizing the data warehouse paradigm with cloud-native architecture. Why is Snowflake capturing global enterprise attention?

Snowflake has implemented the philosophy that data should not simply be stored, but should be utilized and shared in real-time across entire organizations. While traditional on-premises data warehouses struggled with hardware constraints and high operational costs, Snowflake achieved democratization of data analytics through cloud-native architecture, providing unlimited scalability and consumption-based pricing models.

For a true data platform, performance and cost efficiency are paramount. Traditional data warehouses faced inefficient resource usage and high operational costs due to their coupled storage and compute architecture. However, Snowflake's innovative disaggregated architecture enables independent scaling of each layer, implementing an optimized cost structure based on actual usage.

Data Lakes, Data Warehouses, and Lakehouses: Evolution of Modern Data Architecture

Limitations and Challenges of Data Warehouses

Data warehouses have been the core infrastructure supporting enterprise business intelligence decision-making for over 30 years. Data warehouses aggregate data from multiple sources into a single repository and organize it into relational database infrastructure to support data analytics and business intelligence applications.

Key characteristics of data warehouses:

Schema-on-Write approach: Applies consistent schema when data is stored to ensure data consistency
Structured data focus: Optimized primarily for structured data based on relational database systems
ETL processes: Cleanses and transforms data through Extract, Transform, Load processes
High-performance SQL queries: Supports fast analytical queries through structured relational schemas

However, data warehouses don't work well with unstructured or semi-structured data due to strict schema requirements, which becomes a significant constraint for AI and ML use cases.

Emergence and Flexibility of Data Lakes

Data lakes emerged with the big data era and advances in distributed computing technology. Data lakes can store all forms of raw data, including structured, unstructured, and semi-structured data, at low cost.

Key characteristics of data lakes:

Schema-on-Read approach: Applies schema when reading data to ensure flexibility
Support for various data formats: Stores all structured, semi-structured, and unstructured data in raw format
Scalability and cost efficiency: Low-cost scalable storage based on cloud object storage
Machine learning and data science optimization: Supports advanced analytics and ML model development on raw data

However, data lakes don't support transactions, don't guarantee data quality, and lack of consistency/isolation makes it nearly impossible to mix append/read operations with batch/streaming jobs.

Lakehouses: Combining the Best of Both Worlds

Lakehouses integrate the core functionalities of data lakes and data warehouses into a single data management solution. Like data lakes, they can store all forms of data at low cost, while like warehouses, they support fast queries and optimized analytics.

Innovative features of lakehouses:

ACID transactions: Data reliability through metadata layers like Delta Lake and Apache Iceberg
Unified batch/streaming: Real-time streaming I/O support eliminates need for message buses like Kafka
Time Travel: Time travel functionality to previous table versions
Schema evolution: Schema enforcement and evolution support

Snowflake's Innovative Approach: Beyond Data Warehouses to AI Data Cloud

Snowflake's Customer Proposition Strategy

Snowflake proposes innovative value to customers through a hybrid architecture that overcomes the limitations of traditional data warehouses and data lakes:

1. Unified Data Platform Proposition

Snowflake evolved from a cloud data warehousing solution to a multi-cloud data platform providing various use cases including data engineering, data lakes, data warehousing, data science, data applications, and data sharing.

2. Innovation in Consumption-Based Business Model

Snowflake's business model is primarily consumption-based. The company charges customers based on the amount of computing resources used, measured in Snowflake Credits. This model provides customers with flexible and scalable data solutions that can adapt to their needs.

3. Differentiation Through 3-Layer Disaggregated Architecture

Snowflake's unique architecture consists of three core layers: centralized storage for consistent data storage, multi-cluster compute enabling various use cases, and cloud services providing user-friendly experiences.

4. Superiority of Cloud-Native Design

Snowflake was engineered from the ground up to leverage the inherent advantages of cloud computing such as elasticity and distributed processing, unlike older systems retrofitted for cloud environments. This architectural choice is fundamental to its scalability and performance claims.

Snowflake's Architectural Innovation: Proprietary Query Engine

Important Technical Differentiation: Snowflake does not use Apache Spark as its core engine. Instead, it developed a completely proprietary vectorized SQL query engine. The Snowflake data platform is not built on existing database technology or "big data" software platforms such as Hadoop, but uses an innovative architecture natively designed for the cloud.

Snowpark Connect: Innovative Implementation of Spark Compatibility

Snowpark Connect for Apache Spark, introduced by Snowflake in 2025, is not a replacement for Spark but an innovative approach providing Spark API compatibility:

Leveraging Spark Connect Protocol: Executes Spark code in Snowflake warehouses through client-server separation architecture
Actual Execution by Snowflake Engine: Converts Spark query plans to execute on Snowflake's vectorized query engine
No Spark Cluster Required: Eliminates traditional Spark cluster management overhead

This is fundamentally different from Databricks' approach of using Apache Spark as the actual core engine.

Snowflake's Structural Innovation

Snowflake proposes the following structural solutions to customers:

Problem Recognition and Solution Presentation: Key problems Snowflake solves: Data formats incompatible for use in other contexts, inability to handle large volumes of data making queries time and resource-intensive especially for big data, inability to address and support as many use cases as possible within organizations, high data management costs, and difficulty of use.

Core Value Propositions:

Instant Elasticity: Real-time resource scaling based on workloads
Ease of Use: Fully managed service without complex infrastructure management
Performance Optimization: Automatic query optimization and parallel processing
Cost Efficiency: Billing based on actual usage reducing TCO
Data Volume Handling: Nearly unlimited data storage and processing capabilities

Research Data and Company Status

Research Date: September 15, 2025

Company Name: Snowflake Inc.

Website: https://www.snowflake.com/

Domain: Cloud Data Warehouse, AI Data Cloud, Data Sharing Platform

Core Solutions: AI Data Cloud, Data Warehousing, Data Engineering, Data Science, Data Applications

Key Technologies: Multi-cloud Architecture, Disaggregated Storage-Compute, Auto-scaling, Data Sharing

Headquarters: Bozeman, Montana, USA (Operations: San Mateo, California)

Founded: 2012

Employees: ~7,000 (as of 2025)

Listed: NYSE (SNOW)

CEO: Sridhar Ramaswamy

Market Cap: Approximately $45-50B (September 2025)

Key Partners: AWS, Microsoft Azure, Google Cloud Platform, Tableau, Power BI, Looker

Competitors: Databricks, Google BigQuery, Amazon Redshift, Microsoft Azure Synapse Analytics

Snowflake vs Databricks: Two Giants of Cloud Data Platforms

Evolution of Competitive Landscape

Snowflake and Databricks have many similarities in data storage and querying, but important differences exist in core functionality and target use cases. Databricks started with data engineering and data science at its core and successfully expanded to cloud data warehousing, while Snowflake is moving in the opposite direction.

Architectural and Design Philosophy Differences

Databricks' Approach

Databricks architecture started with data engineering and data science at its core, using Apache Spark as the actual core engine. It integrated industry-leading capabilities such as the Apache Spark framework, MLflow, and Time Travel, and successfully solved seamless interoperability issues across various data processing engines through significant investment in the Delta Lake project.

Snowflake's Approach

Snowflake developed a proprietary vectorized query engine to build a cloud-native data warehouse. It does not use Apache Spark as its core engine, but instead uses a completely new SQL query engine with an innovative architecture natively designed for the cloud.

Key Differences:

Databricks: Platform operating on Apache Spark engine
Snowflake: Uses proprietary vectorized query engine while providing Spark API compatibility through Snowpark Connect

Technical Innovation of Snowpark Connect

Snowpark Connect for Apache Spark, introduced in 2025, demonstrates how Snowflake integrates with the Spark ecosystem. Rather than adopting Spark as a core engine, it leverages the Spark Connect protocol to provide Spark API compatibility while actual execution is handled by Snowflake's proprietary engine.

Databricks' Core Strengths:

Lakehouse Architecture: Unified analytics platform based on Apache Spark
Machine Learning Specialization: MLflow, AutoML, real-time model tracking capabilities
Multi-language Support: Python, Scala, R, SQL, Java support
Open Source Foundation: Delta Lake, Apache Spark open source ecosystem

Snowflake's Core Strengths:

SQL-First Approach: Optimized for SQL-based analytics
Fully Managed Service: Eliminates infrastructure management burden
Native Data Sharing: Built-in data sharing and marketplace
Multi-cloud Compatibility: Data replication and access across AWS, Azure, GCP

Use Case-Specific Differentiation Strategies

If your focus is on data science, machine learning, and complex engineering workflows, Databricks is the better fit. If your needs revolve around structured data, SQL analytics, and business intelligence, Snowflake offers a simpler, more cost-effective solution.

Performance and Scalability Comparison

Snowflake's Performance Advantages: Snowflake's architecture is designed for scalability. It uses shared-disk and shared-nothing architecture with separated storage and compute resources. This disaggregated design allows independent scaling of these resources as data and query loads change.

Databricks' Performance Advantages: If workloads involve complex data processing, machine learning, or real-time analytics, Databricks may offer superior performance.

Cost Model Differences

Snowflake charges customers separately for storage based on usage levels regarding warehouse size and time required. In contrast, Databricks is less expensive for data storage as it provides clients with different storage environments configurable for specific purposes.

Ecosystem and Integration

Snowflake's Ecosystem: Snowflake integrates seamlessly with Tableau, Power BI, Looker, Qlik, and other SQL-friendly BI tools.

Databricks' Ecosystem: Databricks supports Git, CI/CD, notebooks, and IDEs like VS Code.

Revolutionary Growth Trajectory and Financial Status

2025 Financial Performance

Snowflake continues to record high growth. Q4 FY2025 revenue was $986.8 million, representing 27% year-over-year growth. Product revenue was $943.3 million, representing 28% year-over-year growth.

Key Business Metrics (as of January 31, 2025):

Net Revenue Retention Rate: 126%
Customers with $1M+ Annual Product Revenue: 580 (27% YoY growth)
Forbes Global 2000 Customers: 745 (5% YoY growth)
Remaining Performance Obligations: $6.9 billion (33% YoY growth)

Analysis from Data Analytics Software Developer Perspective

From a data analytics software developer perspective, what's particularly impressive is that Snowflake's annual revenue reached $3.63 billion in FY2025, up 29.21% from $2.81 billion in 2024. This significantly exceeds the average growth rate in the enterprise software industry.

Particularly noteworthy is that Snowflake's consumption-based model is actually connected to customer value. As the company improves features that make the platform more efficient, customers can accomplish the same workloads with fewer resources. This creates a virtuous cycle where technological innovation directly translates to customer satisfaction.

Future Growth Strategy

AI and Data Science Expansion: More than 6,100 accounts use Snowflake's AI weekly. Customers love that the platform is easy to use, connected to enable fluid access to data wherever it sits, and trusted by companies of all sizes and industries.

Strategic Partnerships and Market Expansion

Cloud Ecosystem Integration

Snowflake pursues a multi-cloud strategy through deep integration with major cloud providers. This architecture is built across three major public clouds (AWS, Azure, GCP) with 31 regional deployments worldwide, allowing organizations to optimize for the best features without relying on a single provider.

Data Marketplace Ecosystem

Providers delivering data, apps, and AI products on Snowflake are unlocking greater market potential in the AI Data Cloud. Startups are closing opportunities in large enterprise accounts and regulated markets, while established providers are accelerating evaluations and completing deals faster across new and existing customers.

Marketplace Capacity Drawdown Program Innovation: The Marketplace Capacity Drawdown Program provides customers the opportunity to pay for products with a portion of their committed Snowflake spend, removing the need for additional budget approval for Marketplace purchases.

Challenges and Outlook

Market Challenges

Intensifying Competition: Fierce competition with Databricks, Google BigQuery, Amazon Redshift, etc.

Technical Challenges:

Relative weakness compared to Databricks in AI/ML workloads
Need to improve unstructured data processing capabilities
Strengthening real-time streaming data processing features

Market Outlook

Evolution to AI Data Cloud: CEO Sridhar Ramaswamy stated, "Today, Snowflake is the most consequential data and AI company in the world. More than 11,000 customers are already betting their business on our easy-to-use, efficient, and trusted platform."

Expected Growth Areas:

Cortex AI: Data querying through natural language and leveraging built-in large language models
Data Applications: Data-driven applications built directly within the Snowflake environment
Cross-Cloud Data Sharing: Seamless data collaboration in multi-cloud environments

Innovation Value from Developer Perspective

As a data analytics software developer, Snowflake's approach is very intriguing. The

disaggregated architecture is a successful application of modern cloud-native application design best practices to the data warehouse domain.

Snowflake's auto-scaling and optimization features enable developers to focus on data analytics logic and business value creation rather than infrastructure management. This is similar to the value container orchestration provides in microservices architecture, but implemented in the data domain.

Furthermore, the Zero-ETL concept and native data sharing functionality simplify complex data pipelines, enabling real-time data-driven decision making. This provides innovative value particularly in modern application architectures where data consistency and real-time analytics between microservices are important.

Frontend-Backend-Algorithm Integration Perspective

As a data analytics software developer responsible for frontend, backend, and algorithms, Snowflake provides the following integrated value:

Frontend Integration: Through Snowflake's REST API and SQL interface, you can build data visualization dashboards directly in modern frontend frameworks like React and Vue.js. Particularly through Streamlit integration, you can rapidly prototype Python-based data apps, facilitating smooth collaboration between data scientists and developers.

Backend Optimization: Snowflake's automatic query optimization and caching mechanisms significantly improve backend API response times. Additionally, virtual warehouse auto-scaling ensures stable service during traffic spikes, reducing backend infrastructure complexity.

Algorithm Acceleration: Through Snowflake's Snowpark, you can execute machine learning algorithms written in Python and Scala directly where the data resides. This reduces data movement costs and significantly improves algorithm performance on large datasets.

Strategic Opportunities and Challenges in the Korean Market

Characteristics of the Korean Enterprise Market

The Korean market provides very strategic opportunities for Snowflake. Korean large enterprises are accelerating digital transformation and are particularly active in adopting data-driven decision making and AI.

Key Opportunity Factors:

Regulatory Environment Changes: Increased demand for data governance due to strengthened personal information protection laws
Cloud-First Policy: Government's Digital New Deal and cloud transformation policies
Global Company Entry to Korea: Need for integrated data platforms by multinational corporations

Korean Market Entry Strategy

Financial and Manufacturing Industry-Specific Solutions: Can provide data solutions specialized for Korea's major industries - finance and manufacturing. Particularly with increasing demands for ESG reporting and risk management, Snowflake's governance features can serve as competitive advantages.

K-Cloud Ecosystem Integration: Can pursue hybrid multi-cloud strategies through integration with domestic cloud services like Naver Cloud Platform and KT Cloud.

Technical Innovation and Future Roadmap

Cortex AI: Democratization of Generative AI and Revolutionary User Experience

Snowflake's Cortex AI is fundamentally transforming the paradigm of data analytics. As of 2025, the AI capabilities provided by Snowflake go beyond simple query tools to create entirely new data interaction experiences.

1. Cortex Analyst - Conversing with Data in Natural Language

Cortex Analyst is a fully-managed, LLM-powered Snowflake Cortex feature that enables users to ask questions in natural language and receive direct answers based on structured data in Snowflake.

Revolutionary Experience:

-- Instead of this complex SQL...
SELECT region, product_category, 
       SUM(revenue) as total_revenue,
       AVG(customer_satisfaction) as avg_satisfaction
FROM sales_data 
WHERE date >= '2024-01-01'
GROUP BY region, product_category
HAVING SUM(revenue) > 1000000;

-- You can ask in natural language like this:
"What are the regional and product category-wise revenues and customer satisfaction 
for areas that exceeded $1 million in sales last year?"

No SQL Knowledge Required: Business users can analyze data without writing complex queries
Real-time Answers: Immediate insights without creating complex dashboards or reports
SQL Transparency: Ability to see what queries were executed

2. Cortex Search - Revolution in Hybrid RAG Search

Cortex Search combines the strengths of vector search, keyword search, and semantic reranking into a single search interface. Internal research shows that this approach yields higher quality search results across a variety of RAG-oriented search workloads than vector search or keyword search alone, with over 12% improvement.

Actual User Experience:

Document Conversations: Ask questions and get answers as if conversing with PDF and Word documents
Hybrid Search: Simultaneous use of semantic similarity and keyword matching
200-300ms Response Time: Ultra-fast search even on large volumes of text
Native Vector Database: VECTOR data type support without separate vector stores

3. Cortex LLM Functions - Implementing AI with SQL

Snowflake Cortex gives instant access to industry-leading large language models trained by researchers at companies like Anthropic, Mistral, Reka, Meta, and Google.

Key AI Functions:

-- Summarize customer reviews in natural language
SELECT product_id, 
       AI_SUMMARIZE(review_text) as summary
FROM customer_reviews 
GROUP BY product_id;

-- Real-time sentiment analysis
SELECT product_name,
       AI_SENTIMENT(customer_review) as sentiment_score,
       AI_EXTRACT('key complaints', customer_review) as key_issues
FROM product_reviews
WHERE review_date >= CURRENT_DATE - 7;

-- Extract information from images
SELECT AI_EXTRACT('Extract brand name', image_data) 
FROM product_images;

-- Transcribe and analyze audio files
SELECT AI_TRANSCRIBE(audio_file),
       AI_SENTIMENT(AI_TRANSCRIBE(audio_file)) as call_sentiment
FROM meeting_recordings;

4. Multimodal AI Processing

Using the new FILE data type to seamlessly integrate text, images, and audio within Snowflake tables, with new AI-powered operators that work with familiar SQL syntax.

Innovative Features:

AI_COMPLETE: Generate text or image completions
AI_EXTRACT: Extract information from documents
AI_PARSE_DOCUMENT: Extract text from PDFs and images (OCR)
AI_TRANSCRIBE: Transcribe audio files
Document AI: Document processing using Arctic-TILT model

5. Cortex AISQL - Transforming SQL into AI Query Language

Cortex AISQL enables building scalable AI pipelines across all multimodal data using familiar SQL commands. This allows developers with existing SQL knowledge to immediately leverage AI capabilities without learning separate AI frameworks.

6. Real Business Use Cases

Customer Service Innovation:

-- Automatically classify and prioritize customer support tickets
SELECT ticket_id,
       AI_CLASSIFY_TEXT(ticket_content, 
         ['urgent', 'normal', 'info_request']) as priority,
       AI_EXTRACT('customer emotion', ticket_content) as customer_mood,
       AI_SUMMARIZE(ticket_content) as summary
FROM support_tickets
WHERE created_date = CURRENT_DATE;

Marketing Insights:

-- Social media mention analysis
SELECT brand_name,
       AI_SENTIMENT(mention_text) as sentiment,
       AI_EXTRACT('key keywords', mention_text) as keywords,
       COUNT(*) as mention_count
FROM social_media_mentions
GROUP BY brand_name, AI_SENTIMENT(mention_text);

Document-based QA Systems:

Upload company policy documents, manuals, contracts
Ask natural language questions like "What are the vacation policy regulations for annual leave?"
Receive accurate answers with references to relevant document sections

These AI capabilities provide revolutionary experiences that enable enterprises to immediately benefit from AI without building complex AI infrastructure. The key differentiator is that data never leaves Snowflake, maintaining security and governance while leveraging cutting-edge AI functionality.

Native Application Platform

Snowflake is evolving from a data warehouse to an application platform. Through

Snowflake Native Apps, data providers can deploy and monetize applications directly within the Snowflake environment.

This is an innovative approach different from traditional SaaS models. Third-party application functionality can be utilized without moving customer data externally, satisfying both security and performance requirements.

Differentiated Competitive Advantages Against Databricks

Simplicity of User Experience

The biggest difference between Snowflake and Databricks is their philosophy of user experience. Snowflake enables analysts familiar with SQL to perform powerful data analytics without additional learning through "abstraction of complexity."

In contrast, Databricks emphasizes "flexibility and control," providing more customization options for data engineers and data scientists.

Business Model Differences

Snowflake's Revenue Model:

Product revenue accounts for 94% of total revenue ($1.93 billion in 2023)
Professional services account for 6% (approximately $127 million)
Consumption-based billing directly linked to customer usage

Market Share Comparison:

Snowflake: 18.33% (current industry market share)
Databricks: 8.67%

This shows Snowflake has broader acceptance in the enterprise market.

Technical Approach Differences

Snowflake's Technical Strategy:

Always-On Encryption: Enhanced security compared to Databricks' encryption at rest
Fully Managed Service: Complete elimination of infrastructure management burden
SQL-Centric Optimization: Seamless integration with existing BI tools

Databricks' Technical Strategy:

Open Source Ecosystem: Based on open source like Apache Spark and Delta Lake
Developer-Friendly: Git, CI/CD, notebook-based development environment
Machine Learning Specialization: Professional tools like MLflow and Feature Store

Future Outlook and Strategic Direction

Market Opportunities and Growth Drivers

Data Infrastructure for the AI Era: With the rapid expansion of generative AI and machine learning, the importance of data is growing. Snowflake is materializing its vision of "AI Data Cloud" by leveraging this trend.

Expected Growth Areas:

Vector Database Integration: Support for large language models and RAG (Retrieval-Augmented Generation)
Real-time Streaming: Enhanced real-time data processing through native integration with Apache Kafka
Edge Computing Integration: Cloud integration solutions for IoT data

Challenges and Response Strategies

Technical Challenges:

Unstructured Data Processing: Expanding native support for images, videos, and audio data
Real-time Processing Performance: Expansion from batch processing focus to streaming processing
Cost Optimization: Improving cost efficiency in large-scale data processing

Market Response: To respond to Databricks' lakehouse offensive, Snowflake is actively participating in the open standards ecosystem through Iceberg support and the Polaris Catalog open source project.

Key Insights for Investors and Corporate Decision Makers

Financial Health and Growth Sustainability

Revenue Growth Rate Analysis:

2025: 29.21% growth ($3.63 billion)
2024: 35.86% growth ($2.81 billion)
2023: 69.41% growth ($2.07 billion)

While growth rates are gradually slowing, they still maintain healthy growth significantly above enterprise software industry averages.

Qualitative Improvement of Customer Base:

Customers spending $1M+ annually: 580 (27% increase)
Forbes Global 2000 customers: 745
Net Revenue Retention Rate: 126% (indicator of customer satisfaction and scalability)

Sustainability of Competitive Advantages

Network Effects: Snowflake claims positive network effects where more customers joining the platform increases data exchange and collaboration, enhancing overall platform value.

Technical Moat:

Multi-cloud Architecture: Minimizes vendor lock-in risks
Automatic Optimization: Performance management without manual tuning
Data Governance: Enterprise-grade security and compliance

Conclusion: Core Infrastructure of the Data Economy Era

Snowflake is evolving from a simple data warehouse company to core infrastructure of the data economy. Through cloud-native architecture, consumption-based business models, and AI integration, it has built a platform that comprehensively addresses modern enterprise data needs.

In competition with Databricks, Snowflake is securing a broader user base through differentiated value of simplicity and ease of use. Particularly for business analysts and traditional BI teams familiar with SQL, Snowflake is the more suitable choice.

In the future AI era, data is not the new oil but the new electricity. Snowflake serves as a power plant that efficiently produces, stores, and distributes this electricity, becoming essential infrastructure in the process of all enterprises transforming into data-driven organizations.

As a data analytics software developer, I evaluate Snowflake's innovation as realizing a greater vision of data democratization beyond mere technical excellence. Making it possible for anyone to harness the power of data without complex data engineering knowledge—this is the essence of innovation Snowflake pursues.

This evolution of data platforms is changing the architecture of applications we build, opening an era where all software becomes inherently data-driven. Snowflake is creating new standards for the data economy at the center of this transformation.