As organizations strive to become more data-driven in a rapidly changing digital landscape, the Snowflake Data Cloud emerges as a pivotal solution. For those seeking an all-encompassing data management solution, the Snowflake Data Platform likely rings a bell.

Snowflake stands out as an acclaimed data warehouse choice, praised for its simplicity, robust capabilities, and abundant advantages. It is ideal for businesses, especially newcomers to data warehousing.

Snowflake Data Cloud redefines how organizations store, process, and derive insights from their data. It is a robust, cloud-built data platform that is delivered as a service.

It stands out in the realm of data management by seamlessly integrating various components that collectively empower organizations to harness the full potential of their data.

As a cloud-native solution, Snowflake operates on major cloud platforms, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).

This blog highlights all you need to know about Snowflake, along with its key aspects, starting with a fundamental understanding.

What is Snowflake Data Cloud?

Snowflake, a data platform, enables firms to store and analyze customer data comprehensively. It goes beyond conventional analytics tools, proving beneficial for enterprises grappling with the task of consolidating vast data volumes in a centralized repository.

Snowflake transcends the conventional database role, positioning itself as a comprehensive end-to-end solution. It seamlessly integrates essential components such as data ingestion, processing, security, governance, and collaboration.

Snowflake serves as a holistic solution for managing large-scale data processing requirements, providing a complete infrastructure for businesses to operate seamlessly.

What Makes Up the Snowflake Platform?

The Snowflake Data Cloud platform comprises a hybrid architecture, blending traditional shared-disk and shared-nothing database structures. It incorporates a central data repository akin to shared-disk models, accessible from all compute nodes.

Yet, it employs MPP (massively parallel processing) compute clusters for query processing, with each node storing a localized subset of the complete dataset.

This distinctive approach combines the simplicity of shared-disk architectures with the performance and scalability advantages of shared-nothing architectures.

How Does Snowflake Data Cloud Work?

Snowflake Data Cloud works through an advanced self-managed service, revolutionizing data storage, processing, and analytics. Unlike traditional solutions, Snowflake offers enhanced speed, ease of use, and unparalleled flexibility.

Diverging from existing database technologies and “big data” platforms like Hadoop, Snowflake adopts a pioneering approach. It melds a novel SQL query engine with a cloud-native architecture, eliminating reliance on pre-existing frameworks.

For users, Snowflake seamlessly delivers the functionalities of an enterprise analytic database while introducing extra features and distinctive capabilities.


Experience Seamless Data Management

Unlock the power of Snowflake Data Cloud for unparalleled data management. Experience seamless scalability, performance, and security like never before.

What Components Make up the Snowflake Data Cloud?

Breaking down its architecture, Snowflake Data Cloud features three pivotal layers:

1. Database Storage

Upon ingestion into Snowflake, data undergoes a transformation, being reorganized into an internal format optimized for efficiency, compression, and columnar storage. This refined data is then securely stored in cloud storage.

Snowflake takes on the comprehensive management of various storage facets, including organization, file size, structure, compression, metadata, statistics, and more.

Importantly, the data objects remain hidden and inaccessible directly to customers; access is exclusively granted through SQL queries executed within the Snowflake environment.

2. Query Processing

Within the processing layer, query execution takes place, orchestrated by Snowflake’s deployment of “virtual warehouses.” These warehouses, serving as MPP (massively parallel processing) compute clusters, consist of multiple compute nodes allotted by Snowflake through a chosen cloud provider.

Crucially, each virtual warehouse operates autonomously, devoid of resource-sharing with other clusters. This isolation ensures that the performance of one virtual warehouse remains unaffected by the activities of others, enhancing efficiency and avoiding performance bottlenecks.

3. Cloud Services

At the heart of Snowflake, the cloud services layer serves as a consolidated hub for coordinating activities seamlessly. This layer interconnects all Snowflake components, orchestrating a smooth flow of user requests from login initiation to query dispatch.

Operational on compute instances equipped by Snowflake through the chosen cloud provider, the cloud services layer encompasses critical services such as:

  1. Authentication: Verify and ensure secure access to Snowflake.
  2. Infrastructure Management: Overseeing the underlying architecture and resources.
  3. Metadata Management: Handling data descriptors, properties, and relationships.
  4. Query Parsing and Optimization: Analyzing and refining queries for optimal execution.
  5. Access Control: Governing permissions and user access within the Snowflake environment.

What Are the Benefits of Using Snowflake Cloud Data?

Snowflake’s Cloud Data Platform redefines the capabilities of a cloud data warehouse, empowering organizations to make strategic decisions and become data-driven entities.

The platform provides a solid foundation for future initiatives, fostering growth and advancement within the organization. Snowflake offers the following benefits:

1. Cloud-Built Robust Platform:

Snowflake Data Warehouse is a resilient cloud-based platform delivered as a service, ensuring reliability and accessibility. Leveraging cloud architecture, Snowflake eliminates the complexities and constraints associated with traditional data warehouses.

2. Cross-Cloud Deployment Capabilities:

Supports a multi-cloud strategy, providing deployment capabilities across major cloud platforms, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Enables organizations to avoid vendor lock-in and leverage diverse cloud services offered by different providers.

3. Separate Workload Clusters:

Facilitates workload separation, allowing organizations to operate different clusters against the same data. Ensures consistency and integrity by accommodating diverse workloads, such as data science, analytics, reporting, and IT operations.

4. Virtually Unlimited Query Concurrency:

Scalability is a cornerstone, providing virtually unlimited query concurrency to handle simultaneous operations during peak demand. Ensures optimal performance even in scenarios with high query loads.

5. High-Performance Queries on JSON:

Efficiently runs JSON queries, allowing organizations to gain advanced insights and a holistic view of their data. Enhances analytical capabilities, especially in scenarios where unstructured or semi-structured data is prevalent.

6. Performance Across Structured Data Types:

Offers high-performance queries not only on JSON data but across a broad range of structured data types. Enhances flexibility in querying diverse datasets, providing a comprehensive analytical perspective.

7. Elastic Scaling:

Allows organizations to seamlessly scale up or down, both vertically and horizontally, to adapt resources to changing demands. Enables elasticity without disrupting ongoing queries, ensuring cost efficiencies by optimizing resource usage.

8. Automatic Database Management:

Delivered as a service, Snowflake eliminates the need for manual database management efforts. Saves time and resources by automating routine tasks, allowing organizations to focus on strategic initiatives rather than infrastructure management.

9. Efficient Resource Utilization:

Eliminates tedious tasks such as parameter settings, distribution, key management, tuning, and vacuuming. Frees up data engineering resources to concentrate on higher-value projects, maximizing the efficiency of valuable talent.

10. Complete SQL Database and Data Warehouse:

Snowflake is a robust ANSI SQL-compliant platform, ensuring compatibility with enterprise data warehouse applications. Provides support for multi-statement transactions, catering to diverse data processing needs.

11. Built-In High Availability and Data Protection:

Retains and recovers data for up to 90 days, ensuring high availability and protecting against unforeseen events, human errors, node failures, and malicious attacks.

12. Cost-Effective Data Storage:

Offers budget-friendly compressed data storage without expensive premium storage. Ensures cost efficiency while providing ample storage capabilities for organizational data.

How is Snowflake Pricing Calculated?

Snowflake’s pricing model revolves around data storage and compute resource consumption, providing flexibility and transparency in billing based on actual usage. Costs are accrued daily, and the final monthly billing reflects the cumulative consumption of data storage and compute resources. Here’s how it works:

1. Compute Costs:

Snowflake credits serve as the unit of measure for computing usage and are consumed for query processing and cloud services within the Snowflake environment.

2. Storage Costs:

Monthly billing is determined by the amount of data storage utilized, measured in compressed terabytes per month.

Snowflake Credits

Snowflake credits are the designated unit of measure used to cover the consumption of resources within the Snowflake platform. Credits are exclusively consumed when customers actively utilize resources.

This includes scenarios such as the operation of virtual warehouses, the execution of functions by the cloud services layer, or the utilization of serverless features. The primary function of Snowflake credits is to serve as a means of payment for the dynamic utilization of resources, aligning costs with actual usage.

Credits are attributed to various activities within Snowflake, ensuring that customers are billed based on their resource consumption and promoting transparency and efficiency in cost management.

How is Credit Usage Calculated?

The credit usage is influenced by the quantity of virtual warehouses employed within the Snowflake environment. Credits are charged based on the duration for which each virtual warehouse operates. The longer a virtual warehouse runs, the higher the credit consumption.

The size of each virtual warehouse, denoting the compute resources per cluster it possesses, directly impacts credit usage. More giant warehouses with more substantial computational capabilities generally incur higher credit costs.

The credit usage calculation involves referencing a table that outlines the credit cost associated with different virtual warehouse sizes. Each size corresponds to specific compute resources allocated per cluster.

Credit usage is finely tuned to the compute resources allocated to each virtual warehouse, ensuring a proportional billing structure based on the computational capacity utilized.

Snowflake’s cost-effectiveness is evident as it allows seamless scaling, adjusting resources based on demand without duplicating compute clusters to manage concurrency.

For example, a complex query running for an hour on an X-Small warehouse consumes one credit. If the same query is executed on a Medium warehouse and completed in 15 minutes, the credit consumption remains at one.

Snowflake’s consumption-based model empowers users to optimize performance without incurring unnecessary costs.

Moreover, Snowflake’s Virtual Warehouses feature an auto-suspend functionality, allowing users to configure suspension after as little as 60 seconds of inactivity—minimizing costs during periods of non-utilization.

Additional Costs

While Snowflake’s pricing is transparent, users should be aware of additional costs associated with certain features. These additional costs ensure the flexibility and scalability of Snowflake’s features, allowing users to optimize their usage based on specific needs and performance requirements.

Cloud Services

Snowflake’s infrastructure, serving as the managed cloud service layer, handles account-level and session operations. DDL commands and account log access incur compute resources but are charged only when exceeding ten percent of the total compute credits. Use the WAREHOUSE_METERING_HISTORY view to monitor cloud service computing usage.

Serverless Computing

Snowflake offers five serverless features utilizing managed compute resources and incurring credits:

  • Snowpipe: A serverless, automated service for ingesting streaming data, eliminating the need for virtual warehouses. Snowpipe and Snowpark costs are covered by Snowflake credits, with an additional fixed credit per file.
  • Database Replication: Allows data replication across regions and cloud platforms without virtual warehouses. Credits cover replication costs, and additional charges may apply for storage and data transfer.
  • Materialized Views Maintenance: This tool automates the synchronization of materialized views with base tables without requiring virtual warehouses, utilizing Snowflake-managed resources.
  • Automatic Clustering: Optimizes clustering state for tables and materialized views, utilizing compute resources and billing on a per-second basis with credits.
  • Search Optimization Service (SOS): Available in the Enterprise Edition or higher, it speeds up point lookup queries in large tables. It is paid with Snowflake credits and billed on a per-second basis, aiming to enhance performance and reduce compute resource usage.

Pricing Options

Snowflake provides two primary usage-based pricing plans, offering flexibility and adaptability to cater to diverse business needs. These pricing models, namely On Demand and Capacity Pricing, are detailed in the company’s official pricing guide.

Snowflake’s purchasing options offer organizations the freedom to choose between On Demand and Capacity Pricing, allowing them to align their payment structure with their specific requirements and usage patterns.

Each option caters to different scenarios, ensuring that businesses can optimize costs and leverage the advantages of Snowflake’s cloud data platform effectively.

On-Demand Pricing

On-Demand Pricing is a flexible and self-serve model offered by Snowflake, where users are charged a fixed rate for the consumption and storage of data within the Snowflake platform.

The billing occurs in arrears at the end of each month, providing users with the ease of selecting a preferred cloud region for deploying their Snowflake instance.

  • Charging Model: Fixed rate for consumption and storage.
  • Billing: Occurs in arrears at the end of each month.
  • Selection: Users can choose a preferred cloud region for deploying their Snowflake instance and opt for one of three Snowflake Editions.
  • Flexibility: Self-serve model, allowing ease of initiation with Snowflake.
  • Ease of Use: The easiest and most flexible way to start using Snowflake.
  • Consideration: Typically, it is not the most cost-effective option by default.

Capacity Pricing

Capacity Pricing is a strategic and cost-effective approach for organizations committed to implementing Snowflake as their cloud data platform. In this model, organizations pre-purchase a specific capacity, signifying a dollar commitment to Snowflake.

Capacity Pricing stands out as a cost-effective option compared to On-Demand Pricing, primarily because it involves a set contract that locks the organization into a predefined commitment. 

  • Nature: Pre-purchased capacity based on a specific dollar commitment to Snowflake.
  • Planning: Suited for organizations committed to implementing Snowflake as a cloud data platform.
  • Requirement: Detailed understanding of end-use cases for estimating monthly credit consumption and storage costs.
  • Estimation: Snowflake or Snowflake partners can assist in providing accurate estimations.
  • Advantages: Generally more cost-effective and attractive compared to On Demand Pricing.
  • Lock-In: Involves a set contract, providing stability and predictability.
  • Pricing Determination: The price for credits is established at the time of the order, considering the total committed compute in the purchase.

Snowflake Editions

Snowflake Data Cloud provides four distinct editions, each tailored to specific business needs, and three of them are available under On Demand Pricing.

These editions differ in features, catering to diverse requirements and offering unique capabilities. Below is an overview of the key aspects of each edition:

1. Standard Edition:

The Standard Edition in Snowflake is the foundational subscription tier that provides access to essential features of the Snowflake Data Cloud platform.

This edition is designed to offer core data warehousing capabilities with key security features.

Standard Edition includes fundamental features that constitute the core functionality of Snowflake as a cloud data warehouse. While Standard Edition offers essential features, there may be some limitations in terms of advanced functionalities or additional capabilities available in higher-tier editions.

These limitations are often associated with more complex use cases, extended time travel periods, or specific features tailored for advanced analytics. Standard Edition is a cost-effective option that is suitable for organizations with basic data warehousing needs.

It is often chosen by businesses that are starting with Snowflake or have straightforward data processing requirements. Moreover, the standard edition offers the following features:

  • Complete SQL Data Warehouse.
  • Secure Data Sharing across regions/clouds.
  • Premier Support 24×365.
  • 1 day of time travel.
  • Always use enterprise-grade encryption in transit and at rest.
  • Customer-dedicated virtual warehouses.
  • Federated authentication.
  • Database and Share replication.
  • External Functions.
  • Snowsight analytics UI.
  • Create your own Data Exchange.
  • Data Marketplace access.

Suited For

Organizations that require fundamental data warehousing and secure data sharing capabilities.

2. Enterprise Edition:

The Enterprise Edition in Snowflake builds upon the foundation of the Standard Edition, offering an enhanced set of features and capabilities to address the more advanced needs of organizations.

Enterprise Edition is tailored for businesses with more sophisticated analytics needs, requiring advanced security measures, extended historical data access, and additional controls for managing workloads efficiently.

Enterprise Edition includes Standard features plus the following:

  • Multi-cluster warehouse.
  • Up to 90 days of Time Travel.
  • Annual rekey of all encrypted data.
  • Materialized Views.
  • Search Optimization Service.
  • Dynamic Data Masking.
  • External Data Tokenization.

Suited For

Businesses with advanced analytics needs and longer time travel requirements.

3. Business Critical:

The Business Critical Edition stands out as the comprehensive and advanced tier for hosting Snowflake in a public cloud region, particularly outside a Virtual Private Cloud (non-VPC) setting.

This edition is strategically designed for scenarios where data and the associated applications are deemed business-critical.

The Business Critical Edition, which caters to enterprises and industries where data resilience, regulatory compliance, and uninterrupted access are paramount.

By offering extended support for regional compliance and failover capabilities, it addresses the unique challenges of hosting and managing business-critical data in a public cloud environment.

Business Critical Edition includes Enterprise Edition features plus the following:

  • HIPAA Support.
  • PCI Compliance.
  • Data encryption is everywhere.
  • Tri-Secret Secure using customer-managed keys.
  • AWS PrivateLink support.
  • Account replication, failover, and failback for business continuity.
  • External Functions – AWS API Gateway Private Endpoints support.
  • Client Redirect.
  • Virtual Private Snowflake.

Suited For

Organizations with critical compliance requirements and enhanced security needs.

4. Virtual Private Snowflake

Virtual Private Snowflake (VPS) represents the pinnacle of dedicated and secure instances within the Snowflake ecosystem, situated within an AWS Virtual Private Cloud (VPC). This edition introduces several distinctive features and advantages, making it the most secure version of Snowflake.

Virtual Private Snowflake caters to organizations with the highest requirements for data security and isolation. By offering a dedicated instance within a secure AWS VPC and introducing features like an isolated metadata store, VPS ensures a robust balance between performance and security, making it an optimal choice for industries where safeguarding sensitive data is of utmost importance.

Virtual Private Snowflake includes Business Critical features plus the following:

  • Customer-dedicated virtual servers are available wherever the encryption key is in memory.
  • Customer-dedicated metadata store.

Suited For

Enterprises need the highest level of customization, control, and security.


Transform Your Analytics

Elevate your analytics game with Snowflake Data Cloud. Say goodbye to traditional data silos and hello to unified analytics infrastructure. Unleash the true potential of your data with Snowflake.


Snowflake Data Cloud stands out as an exceptional data platform, particularly for data teams seeking to streamline operations without delving into the intricacies of ongoing maintenance, query optimization, or resource provisioning.

Its cloud-native architecture, coupled with a comprehensive suite of features, offers a robust solution for organizations navigating the complexities of modern data management. However, Snowflake’s advantages come with a nuanced pricing structure.

A thorough understanding of Snowflake’s pricing details is crucial for optimizing workloads, maximizing compute and storage efficiency, and managing costs effectively.

While the platform empowers businesses to harness the full potential of their data, strategic planning and a keen awareness of pricing details ensure that the benefits of Snowflake are realized without undue financial implications.

  • Frequently Asked Questions

    Q1. Is Snowflake a Data Warehouse or ETL??

    Snowflake serves as a data warehouse with built-in capabilities for data loading and transformation, offering an end-to-end solution for organizations. While it can replace or complement certain aspects of ETL processes, its primary identity remains that of a cloud data warehouse.

    Q2. Is Snowflake the Same as AWS?

    Snowflake is not the same as Amazon Web Services (AWS). Snowflake is a cloud-based data platform and data warehouse that operates on various cloud infrastructures, including AWS, Microsoft Azure, and Google Cloud Platform (GCP). Snowflake provides a separate and distinct service for data storage, processing, and analytics.

    Q3. Is Snowflake a data warehouse or ETL?

    Snowflake is primarily recognized as a cloud-based data warehouse rather than an Extract, Transform, Load (ETL) tool. While Snowflake does offer some features related to data loading and transformation, its core functionality lies in providing a cloud-native data warehousing solution.