Snowflake Architecture Explained – Layers, Components & Benefits | Brolly Academy

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Snowflake Architecture Diagram
Snowflake Architecture diagram Explaining
Snowflake Architecture diagram,
Snowflake is a cloud-based data warehouse that is easy to set up, scalable, and flexible.
It enables businesses to securely manage, process, and share data in real time.
Snowflake integrates with popular analytics tools, supporting various data needs like data warehousing and data science.
Its strong data governance features ensure compliance, enabling businesses to make fast, informed decisions.
Snowflake is ideal for efficient data management and seamless collaboration.
What is a Snowflake Architecture diagram?
Snowflake Architecture refers to the unique design of the Snowflake Data Platform, a cloud-based data warehouse solution that is
built for handling large-scale data storage, processing, and analysis.
Unlike traditional data warehouses, which often have rigid and complex architectures, Snowflake’s architecture is designed to be
simple, scalable, and highly flexible, making it ideal for modern data-driven businesses.
The platform is built on top of cloud infrastructure (available on AWS, Microsoft Azure, and Google Cloud), providing users with the
ability to separate storage, compute, and services.
This separation enables users to scale each component independently based on their needs, which makes Snowflake an ideal
choice for businesses that require both elasticity and performance at a cost-effective price.
Snowflake's architecture Diagram is built around three core layers:
Storage Layer: This is where all the data is stored. Snowflake uses a centralized storage model to store structured and semi-
structured data (such as JSON, Avro, and Parquet) in a single location.
Compute Layer: This layer consists of virtual warehouses that handle the computational workload. These warehouses can scale up
or down based on demand, ensuring optimal performance.
Cloud Services Layer: The cloud services layer manages everything else – including metadata, query optimization, security, and
infrastructure management. It enables Snowflake’s ability to perform seamless data processing and supports multi-user
collaboration.
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What sets Snowflake apart from other data platforms is its ability to provide multi-cluster shared data architecture.
This allows users to scale compute resources dynamically, ensuring that different workloads (such as data loading, querying, and
analytics) do not interfere with each other.
Furthermore, Snowflake’s architecture supports both structured and semi-structured data, which makes it a versatile solution for
organizations working with diverse datasets.
Snowflake’s architecture is designed to offer high availability, elastic scalability, low maintenance, and cost efficiency, all while
enabling seamless integration across the cloud and providing easy access to data for various analytics and business intelligence
tools.
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2. What is the Snowflake Architecture Diagram?
The Snowflake Architecture Diagram is a visual representation of how the various components of Snowflake’s architecture interact
with each other.
It provides a clear, structured view of how data is managed, processed, and accessed within the Snowflake platform.
Understanding the Snowflake architecture diagram helps businesses and technical teams grasp the full capabilities of Snowflake
and optimize its use for their data needs.
The diagram typically breaks down the architecture into three main layers: Storage, Compute, and Cloud Services, and shows how
each layer connects and communicates with others.
These components, although separate, work together seamlessly to deliver a highly scalable and efficient data platform.
1. Storage Layer:
The Storage Layer is where all data resides in Snowflake. This includes both structured data (like relational tables) and semi-
structured data (such as JSON, Parquet, or Avro). In the diagram, this layer typically appears as a central data repository that serves
as the foundation for the data processing pipeline.
2. Compute Layer:
The Compute Layer consists of virtual warehouses, each responsible for performing compute-intensive tasks such as querying,
data transformation, and analytics. These virtual warehouses are shown in the diagram as independent entities that scale

dynamically depending on the workload. The Snowflake Architecture diagram highlights how compute resources can be elastically
scaled to meet the demands of varying workloads, providing cost efficiency and performance optimization.
3. Cloud Services Layer:
The Cloud Services Layer acts as the brain of the Snowflake architecture. It includes key functionalities such as metadata
management, query parsing, access control, security, and optimization. The cloud services layer is responsible for managing and
orchestrating tasks across the compute and storage layers. In the Snowflake Architecture diagram, this layer often appears as a
central service that connects and coordinates the other two layers, ensuring data is processed efficiently.
One of the defining features of the Snowflake architecture Diagram, which is often depicted in the diagram, is its multi-cluster shared
data architecture. This allows Snowflake to separate compute resources from storage, enabling each component to scale
independently. This flexibility is a key factor in Snowflake’s ability to handle large volumes of data while ensuring high performance
and low costs.
A Snowflake architecture diagram may also include additional details such as:
Data Sharing: Showing how data is securely shared between accounts and platforms.
Services for Security and Governance: Illustrating Snowflake’s robust security framework, including data encryption, access control,
and compliance mechanisms.
3. How Does Snowflake Architecture Work?
Snowflake architecture is designed to address the challenges of modern data management by offering an optimized solution for
data storage, processing, and analysis.
The platform’s unique multi-cluster shared data architecture allows for easy scaling, high performance, and seamless data
integration.
1. Storage Layer
The Storage Layer is at the heart of Snowflake’s architecture. Data in Snowflake is stored in a central repository, which is designed to
scale horizontally as the volume of data grows.
Data Organization: Snowflake uses micro-partitions, a form of data segmentation that allows for highly efficient querying and
storage. Each micro-partition is stored in columnar format, enabling fast read and write operations. This architecture is well-suited
for structured, semi-structured, and unstructured data.

Data Compression and Encryption: Snowflake automatically compresses and encrypts data as it is loaded, reducing storage costs
and ensuring security. The data is stored in the cloud provider’s storage infrastructure (e.g., AWS S3, Azure Blob Storage, or Google
Cloud Storage).
2. Compute Layer
The Compute Layer is where the processing of data takes place. In Snowflake, the compute layer is composed of virtual warehouses,
which are independent compute clusters that can scale dynamically. Each virtual warehouse handles specific tasks like data
querying, loading, or transformation. These warehouses are completely isolated, meaning that workloads do not interfere with each
other, even when running simultaneously.
align and Concurrency: The compute layer can scale up to meet high demand or scale down when not needed, optimizing
performance and costs. Snowflake architecture Diagram supports multi-cluster virtual warehouses, which allows multiple clusters
to handle heavy workloads without impacting other operations. This scalability ensures that users can perform tasks like querying
or ETL (Extract, Transform, Load) without throttling other operations.
Elasticity: Snowflake automatically adjusts the size and number of virtual warehouses based on workload requirements. This
elasticity ensures that businesses only pay for the compute resources they use, leading to cost savings.
3. Cloud Services Layer
The Cloud Services Layer is the orchestration layer in Snowflake, responsible for managing the overall platform operations. It acts as
the intermediary between the storage and computing layers, ensuring seamless data processing. This layer manages tasks such as:
Query Parsing and Optimization: When a query is executed, the cloud services layer parses it and optimizes it for performance.
Snowflake’s advanced query optimizer ensures that queries are processed quickly and efficiently.
Metadata Management: All data structures, schemas, and other metadata are stored and managed in the cloud services layer.
Snowflake’s automatic metadata handling ensures that data is correctly indexed, enabling fast query performance.
Security and Governance: Snowflake’s cloud services layer also oversees security functions such as access control, data
encryption, and compliance. It ensures that data is protected according to industry standards and user roles are properly defined.
Task and Workflow Management: The cloud services layer also facilitates task scheduling, managing pipelines, and monitoring
ongoing processes within the system.
How It All Comes Together
Snowflake’s architecture is designed to allow each layer (storage, compute, and cloud services) to work independently and scale
automatically. When a user initiates a query or data transformation, the system:
1. Pulls data from the storage layer (which is optimized for both structured and semi-structured data).
2. Processes the data using the compute layer (via a virtual warehouse that can scale according to the task’s complexity).
3. Coordinates the process using the cloud services layer, ensuring that the query runs optimally with no data conflicts or resource
limitations.
Because of this separation, users can independently scale the computing and storage resources, ensuring that high-demand
workloads do not interfere with each other. This architecture is highly cost-effective, as users only pay for the compute resources they
use without worrying about storage costs impacting performance.

4. Key Benefits of Snowflake Architecture
Snowflake architecture Diagram brings several key benefits that make it a standout solution for businesses. These advantages
include:
Scalability: Snowflake’s architecture allows businesses to scale compute and storage resources independently. This means that as
data needs increase or decrease, you can easily adjust resources without any disruption.
Flexibility: Snowflake is highly flexible, supporting a wide range of data types including structured, semi-structured (e.g., JSON, Avro),
and unstructured data. This flexibility allows businesses to manage all their data in a unified platform. Additionally, Snowflake is cloud-
agnostic, which means it works seamlessly with AWS, Google Cloud, and Microsoft Azure.
Cost Efficiency: Snowflake follows a pay-per-use pricing model, meaning businesses only pay for the computing and storage
resources they use. This model allows for cost savings as companies can avoid paying for idle resources.
Performance Optimization: Snowflake automatically optimizes queries to ensure fast performance, even when processing large
datasets. Its multi-cluster architecture ensures that multiple users or teams can run queries concurrently without impacting the
performance.
Zero Maintenance: With Snowflake, users do not need to manage infrastructure. Snowflake takes care of all maintenance tasks,
including scaling, updates, and optimization. The platform is fully managed, so users can focus on data analysis rather than system
management.
Security: Security is a priority with Snowflake, offering robust features like end-to-end encryption for data both at rest and in transit.
Snowflake uses role-based access control (RBAC) to manage data access and ensure that only authorized users can view sensitive
information.
Data Sharing: Snowflake simplifies data sharing by allowing businesses to securely share data in real-time with partners, clients, or
other teams within the organization. This process does not require moving or replicating the data, making it more efficient and
reducing the potential for errors or data duplication.
Support for Semi-Structured Data: Snowflake natively supports semi-structured data types like JSON, XML, and Avro. This allows
businesses to work with semi-structured data without needing to transform it, saving time and simplifying the data pipeline.

5. Performance Optimization in Snowflake Architecture
Snowflake is designed to deliver high performance even as data grows in size and complexity. The architecture’s unique features
allow businesses to optimize performance without compromising scalability or cost efficiency.
Below are key strategies Snowflake employs to ensure optimal performance:
Automatic Query Optimization: Snowflake uses automatic query optimization to ensure that queries run efficiently. When a query is
executed, Snowflake automatically analyzes and optimizes it to determine the best execution plan.
Multi-Cluster Architecture: One of the standout features of Snowflake is its multi-cluster architecture, which allows multiple compute
clusters to be spun up or down automatically based on workload demand.
Result Caching: Snowflake caches query results to speed up repeated queries. When a query is run, Snowflake stores the results in a
cache. If the same query is executed again (without any changes to the data), Snowflake serves the result from the cache,
significantly reducing query time and improving performance.
Automatic Scaling: Snowflake automatically scales compute resources based on the workload. If a large number of users are running
queries concurrently, Snowflake will scale the compute power accordingly to ensure that queries are processed efficiently without
delays.
Separation of Compute and Storage: By separating compute and storage, Snowflake can allocate compute resources only when
needed and can adjust them based on workload requirements. This not only improves performance but also ensures that compute
resources aren’t tied to storage capacity, giving users greater flexibility and cost-efficiency.
Query Profiling and Execution History: Snowflake provides detailed query profiling and execution history, enabling users to analyze the
performance of individual queries. By examining these reports, businesses can identify any bottlenecks or areas for improvement,
allowing for targeted performance optimizations.
Partitioning and Clustering: Snowflake allows users to create clustering keys for tables to optimize how data is stored. These keys
enable more efficient data retrieval by grouping related data. While Snowflake automatically manages partitioning behind the
scenes, businesses can define clustering keys to ensure faster query performance for large datasets.
Materialized Views: Snowflake supports materialized views, which store the results of a query and update them automatically when
the underlying data changes.
Snowflake’s Elastic Compute: Snowflake’s elastic compute resources ensure that no matter the scale of the data, performance
remains consistent. Resources can be scaled up during periods of high demand and scaled-down during periods of low activity,
ensuring that users pay only for the resources they need without compromising on performance.
6. Cost Efficiency of Snowflake Architecture
Pay-Per-Use Pricing: Snowflake charges only for what you use—paying for storage and computing based on actual consumption. No
upfront costs or long-term commitments.
Independent Scaling of Compute and Storage: You can scale compute and storage separately. If more storage is needed, you can
increase it without adding more compute resources, and vice versa.

Automatic Scaling and Suspension: Snowflake automatically scales compute power during high demand. You can also suspend
compute resources during downtime (like weekends), so you only pay for active usage.
Efficient Storage Management: Snowflake uses data compression techniques, reducing the amount of storage required. This helps
lower storage costs, especially for large datasets.
Zero-Copy Cloning: You can create copies of your data (for development or backup) without duplicating storage costs. The clone
only uses storage if you make changes to the data.
No Maintenance Costs: Snowflake is fully managed, so there are no costs for maintaining hardware or infrastructure. Everything is
handled by Snowflake, reducing operational overhead.
Resource Optimization with Virtual Warehouses: Snowflake allows you to adjust the size of virtual warehouses based on workload.
This helps match compute power to actual needs, preventing overspending.
Cost Savings for Semi-Structured Data: Snowflake can store semi-structured data (like JSON) without transformation, saving on the
costs and time required for data preparation.
Data Sharing at No Extra Cost: You can securely share data with external partners without replicating it, saving on storage and
ensuring everyone has access to the latest information.
Cost Monitoring and Control: Snowflake provides tools to monitor usage and set alerts to track costs. This helps you control your
spending and avoid unexpected charges.
7. Security Features of Snowflake Architecture
Snowflake offers effective security features that protect data while ensuring compliance with industry standards. Key security features
include:
End-to-End Encryption: Snowflake encrypts all data, both at rest and in transit, using strong encryption standards like AES-256. This
ensures that your data is protected throughout its lifecycle.
Role-Based Access Control (RBAC): Snowflake uses RBAC to ensure that only authorized users can access specific data and
resources. You can define roles and permissions at various levels (e.g., database, schema, table) to control access.
Multi-Factor Authentication (MFA): Snowflake supports MFA to add a layer of security. This requires users to provide two forms of
identification (e.g., password and code from a mobile app) before accessing the system.

Data Masking: Snowflake allows you to implement dynamic data masking, which obscures sensitive data in real time based on the
user’s role. For example, personal information can be hidden for users who don’t need to see it.
Virtual Private Snowflake (VPS): Snowflake offers VPS, which provides a dedicated virtual environment for customers. This adds a
layer of isolation to protect data from other users and provides better control over security configurations.
Network Policies: Snowflake allows you to configure network policies that restrict access to your data based on IP addresses or IP
ranges. This ensures that only users from trusted networks can access your Snowflake account.
Automatic Auditing and Logging: Snowflake provides detailed logs of all actions performed on the platform, including data access
and modification. These logs are important for auditing purposes and help track any suspicious activity.
Compliance with Industry Standards: Snowflake complies with major standards like SOC 2 Type II, HIPAA, PCI DSS, and GDPR. This
makes it suitable for industries that require high levels of security and regulatory compliance.
8. Industries That Benefit from Snowflake Architecture
Snowflake is a versatile platform that can benefit a wide range of industries by providing scalable, secure, and cost-effective solutions
for managing and analyzing large volumes of data. Some of the industries that benefit the most from Snowflake include:
Finance and Banking: Snowflake’s security features and ability to handle large volumes of data make it an ideal choice for financial
institutions. It helps with data analysis, fraud detection, and risk management while ensuring compliance with industry regulations.
Healthcare: Snowflake is used by healthcare providers to securely store and analyze patient data, clinical trials, and other medical
records. Its compliance with HIPAA ensures that healthcare organizations can meet privacy standards while improving patient
outcomes through better data analysis.
Retail and E-Commerce: Retailers and e-commerce businesses leverage Snowflake to analyze customer behaviour, optimize
inventory, and personalize marketing campaigns. Its ability to integrate with multiple data sources and perform advanced analytics in
real-time helps businesses stay competitive.
Manufacturing: Manufacturing companies use Snowflake to monitor production processes, manage supply chains, and optimize
inventory. Snowflake’s scalability allows it to handle large datasets, such as IoT sensor data, to improve operational efficiency.
Telecommunications: Telecom companies use Snowflake to analyze call data records, network performance metrics, and customer
behaviour. Snowflake’s ability to handle real-time data and scale as needed helps telecom providers optimize service delivery.
Media and Entertainment: Snowflake is used in the media and entertainment industry for content personalization, audience analysis,
and ad targeting. It can process large datasets, such as video streaming data and social media interactions, to improve user
engagement.
Government: Government agencies use Snowflake to store and analyze public data, improve decision-making, and support initiatives
like smart city planning. Its security and compliance features ensure that sensitive government data is protected.
Education: Educational institutions and e-learning platforms use Snowflake to manage student records, track academic performance,
and enhance learning experiences through data-driven insights.

9. Latest Updates in Snowflake Architecture
Snowflake continuously updates its platform to improve performance, security, and ease of use. Some of the latest updates include:
Native Application Framework: Snowflake has introduced a Native Application Framework that allows users to build and deploy
applications directly within the Snowflake platform. This enables businesses to create custom applications with integrated data
processing, reducing the need for separate tools.
External Functions: Snowflake now supports external functions, which allow users to extend Snowflake’s capabilities by calling external
services (e.g., APIs, machine learning models) directly from within their SQL queries. This enables greater flexibility in processing and
analysis.
Time Travel Enhancements: Snowflake has improved its Time Travel feature, allowing users to access historical data with even more
flexibility. With longer retention periods and easier navigation, businesses can recover deleted or modified data with ease.
Materialized Views: Snowflake now supports materialized views, which allow users to pre-compute complex queries and store the
results for faster access. This improves query performance, particularly for reports and dashboards that need to access large
amounts of data.
Improved Data Sharing: Snowflake has enhanced its data sharing capabilities, making it easier for organizations to securely share live
data with external partners, customers, or departments without data duplication or additional storage costs.
Support for Semi-Structured Data Formats: Snowflake continues to expand its support for semi-structured data formats, such as
JSON, Avro, and Parquet, making it even easier for businesses to work with data from various sources.
Data Marketplace: Snowflake’s Data Marketplace allows organizations to access and share third-party data. This enables users to
integrate external data sources into their analytics, enhancing the value of their data-driven insights.
Automatic Clustering: Snowflake has introduced automatic clustering, which automatically reorganizes tables to improve query
performance, eliminating the need for manual indexing or partitioning.
Conclusion
Snowflake’s architecture offers a modern and flexible approach to data warehousing, combining scalability, security, and cost-
efficiency.

With its ability to separate computing and storage, support for both structured and semi-structured data, and robust security
features, Snowflake is well-suited for businesses across various industries, including finance, healthcare, retail, and more.
The latest updates, such as enhanced data sharing, external functions, and automatic clustering, make Snowflake even more
powerful, providing users with greater flexibility and improved performance. Whether you’re a small startup or a large enterprise,
Snowflake provides a reliable and secure platform for managing and analyzing your data, enabling better decision-making and
driving innovation.
FAQ’s
Snowflake Architecture diagram
1. What is Snowflake Architecture diagram?

Snowflake architecture diagram is a cloud-based data warehousing model designed to separate computing, storage, and services for better
scalability, flexibility, and performance. It enables businesses to store, analyze, and share data efficiently.
2. How does Snowflake differ from traditional data warehouses?

3. Is Snowflake easy to use?

4. What are the benefits of Snowflake Architecture?

5. How does Snowflake improve security?

6. Can Snowflake handle big data?

7. How does Snowflake save costs?

8. What industries use Snowflake?

9. Is Snowflake compatible with other tools?

10. What is Time Travel in Snowflake?

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Snowflake Architecture Explained – Layers, Components & Benefits | Brolly Academy

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