Snowflake is a cloud-based data platform primarily used for data warehousing, data lakes, data engineering, data science, and data sharing.
It’s designed to handle massive volumes of data in a scalable, secure, and efficient way.
Unlike traditional data warehouses, Snowflake is fully managed and cloud-native, running on Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).
Key Features of Snowflake:
- Separation of Storage and Compute:
- You can scale compute (processing power) and storage independently, improving performance and cost-efficiency.
- You can scale compute (processing power) and storage independently, improving performance and cost-efficiency.
- Multi-Cluster Architecture:
- Handles concurrent workloads without performance degradation by automatically scaling compute clusters.
- Handles concurrent workloads without performance degradation by automatically scaling compute clusters.
- SQL-Based:
- Uses standard SQL for querying, which makes it easy for analysts and developers to use without learning a new language.
- Uses standard SQL for querying, which makes it easy for analysts and developers to use without learning a new language.
- Support for Semi-Structured Data:
- Easily handles JSON, Avro, Parquet, and XML with automatic parsing and querying.
- Easily handles JSON, Avro, Parquet, and XML with automatic parsing and querying.
- Time Travel and Fail-Safe:
- Lets you view and restore historical data (up to 90 days, depending on your edition).
- Lets you view and restore historical data (up to 90 days, depending on your edition).
- Secure Data Sharing:
- Allows seamless and secure sharing of data across different Snowflake accounts or external parties without duplication.
- Allows seamless and secure sharing of data across different Snowflake accounts or external parties without duplication.
- Fully Managed:
- No infrastructure to manage—Snowflake handles maintenance, scaling, backups, and tuning automatically.
- No infrastructure to manage—Snowflake handles maintenance, scaling, backups, and tuning automatically.
- High Performance:
- Uses columnar storage and optimizations for faster queries and analytics.
Common Use Cases:
- Enterprise Data Warehousing.
- Data Lakes.
- Real-Time Analytics.
- Business Intelligence (BI).
- Machine Learning and AI.
- Data Sharing and Monetization.
Pricing:
Snowflake uses a pay-as-you-go pricing model based on:
- Storage: Charged per terabyte stored per month.
- Compute: Charged based on how long virtual warehouses (compute clusters) are running.
- Cloud Services: A small cost for metadata management and optimization.
Who owns snowflake?
Snowflake Inc. is a publicly traded company listed on the New York Stock Exchange (NYSE) under the ticker symbol SNOW.
Company Overview:
- Founded: 2012
- Founders: Benoit Dageville, Thierry Cruanes (both former Oracle engineers), and Marcin Żukowski.
- Headquarters: Bozeman, Montana, USA (formerly headquartered in San Mateo, California)
Ownership:
As a public company, Snowflake is owned by its shareholders, which include:
- Institutional Investors (e.g., Vanguard, BlackRock, Fidelity).
- Retail Investors (individual stockholders).
- Company Executives and Founders.
- Former Strategic Investors (such as Salesforce Ventures and Berkshire Hathaway, which participated in its IPO in 2020).
Notable IPO:
- Snowflake went public on September 16, 2020, in one of the biggest software IPOs in history.
- Berkshire Hathaway, led by Warren Buffett, made headlines by investing $735 million in Snowflake at the IPO—one of Buffett’s rare investments in a tech IPO.
How does Snowflake compare to similar applications like Amazon Redshift or Google BigQuery?
Here is a comparative overview of Snowflake vs. Amazon Redshift vs. Google BigQuery, three major players in the cloud data warehousing space.
Each has its strengths depending on your priorities like performance, cost, integration, and flexibility.
High-Level Comparison
| Feature | Snowflake | Amazon Redshift | Google BigQuery |
| Provider | Independent (multi-cloud) | AWS | Google Cloud |
| Deployment | Cloud-native on AWS, Azure, GCP | AWS only | Google Cloud only |
| Architecture | Decoupled compute & storage | Closely coupled (now supports RA3 decoupling) | Fully serverless (storage + compute decoupled) |
| Query Language | Standard SQL | Standard SQL + PostgreSQL syntax | Standard SQL (with extensions) |
| Performance Scaling | Auto/multi-cluster scaling | Manual scaling, concurrency scaling available | Fully serverless, scales automatically |
| Pricing Model | Pay-per-second (compute), storage | Hourly or per-second compute, storage | Pay-per-query (bytes processed), storage |
| Ease of Use | Very user-friendly | More admin-heavy | Very simple (zero infrastructure) |
| Semi-Structured Data | First-class support (e.g., JSON) | Support via Redshift Spectrum & JSON functions | Excellent (optimized for nested data) |
| Data Sharing | Built-in native sharing | External tools needed | Data sharing via authorized views |
| Security | End-to-end encryption, private link, role-based access | Strong, but more AWS IAM-dependent | Strong, with fine-grained IAM |
| Ecosystem | Integrates with all major BI tools | Deep AWS ecosystem integration | Best with Google Cloud services |
When to Use Each
Snowflake is best if:
- You need multi-cloud or cloud-agnostic support.
- You want scalable, easy-to-use warehousing with minimal admin.
- You have mixed workloads (structured and semi-structured).
- You value fast data sharing between business units or partners.
Amazon Redshift is best if:
- You’re deeply invested in AWS infrastructure.
- You want tight integration with tools like S3, Glue, SageMaker.
- You have in-house expertise in PostgreSQL and need advanced tuning.
- You’re okay with managing more infrastructure for performance gains.
Google BigQuery is best if:
- You’re using Google Cloud Platform (GCP) services heavily.
- You prefer a serverless model—no infrastructure to manage.
- You want to analyze very large datasets (petabyte-scale).
- You need real-time analytics and pay-per-query flexibility.
Summary:
- Snowflake: Easiest to use, flexible, strong multi-cloud and data sharing.
- Redshift: Best for AWS-centric enterprises, customizable but more complex.
- BigQuery: Best for massive-scale analytics, cost-efficient for ad-hoc workloads.