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In today’s growing data driven world many businesses need fast and scalable solution to manage massive volumes of data. Traditional data warehouse architecture that exists in the market is not much scalable, they often suffer from performance and network bottlenecks which hinders companies growth. That is why Snowflake Architecture comes into picture. Let’s deep dive into the world of snowflake data warehouse architecture.
1. What is Snowflake?
Snowflake is a Cloud Data Warehouse platform which was designed to handle massive amount of data storage, perform query processing efficiently and provide users with unmatched speed and scalability. Unlike existing traditional architectures like Shared Disk and Shared Nothing Architecture, snowflake architecture takes the best features from both the architectures which is also known as Multi-Cluster Shared Data Architecture that decouples Compute and Storage which allows businesses to scale both aspects individually based on workload demands.
1.1 Why Businesses Choose Snowflake Over Traditional Data Warehouses?
Unlike traditional architectures, which requires complex infrastructure management on which companies don’t want to spend much of their time. Snowflake simplifies data management and operations by performing execution on external cloud service provider like AWS, Azure & Google Cloud. Below are the key advantages of snowflake data warehouse arcitecture:
- Scale Compute resources and Storage on Demand.
- Execute high-speed queries with efficient query optimization.
- Pay as you go model (only pay for the resources you consume).
2. Understanding Snowflake Architecture
Let us now deep dive into 3 distinct layers of snowflake architectures:
1. Database Storage Layer
Database storage layer is a decoupled layer which uses Hybrid Compressed Columnar Storage. Hybrid Compressed Columnar Storage is a type of storage in which data is compressed into BLOB’s which will be stored into external cloud service provider (AWS, Azure and GCP). Snowflake manages all aspects about storage of data which means organization, file size, structure, compression, metadata and statistics are taken cake by snowflake itself. One more key feature of this layer is it is optimized for OLAP and analytical purposes rather than transactional purposes.
2. Query Processing (Compute) Layer
This layer is the muscle of the system as it provides compute resources like CPU, memory & Temp for processing all of our queries. These queries are processed using Virtual Warehouses which are Massively Parallel Processing compute clusters which are allocated by Snowflake from Cloud Provider. Each Virtual Warehouse is an independent compute cluster which does not share any compute resources with other warehouses which in turn leads to efficient performance.
3. Cloud Services Layer
This layer is the brain of the system because in this layer collection of services co-ordinate and manage the components and services. These cloud services layer run on the compute instances of Cloud Services Provider which is maintained by Snowflake behind the scenes. This layer is also responsible for managing Authentication and Access Control, Metadata Management, Query parsing and optimization and Infrastructure Management.
3. Snowflake SnowPro Core Certification Practice Questions.
1. Which of the following best describes Snowflake’s architecture?
A. Single-tier architecture
B. Shared-disk and shared-nothing hybrid architecture
C. Fully shared-nothing architecture
D. Monolithic architecture
Correct Answer: B. Shared-disk and shared-nothing hybrid architecture
Explanation: Snowflake uses a hybrid architecture where storage is shared across all compute clusters, but each virtual warehouse operates independently, resembling a shared-nothing model.
2. What layer of Snowflake is responsible for query execution?
A. Cloud Services Layer
B. Storage Layer
C. Compute Layer (Virtual Warehouse)
D. Metadata Layer
Correct Answer: C. Compute Layer (Virtual Warehouse)
Explanation: The compute layer, known as Virtual Warehouses, is responsible for executing queries and performing data processing tasks independently.
3. How does Snowflake store data internally?
A. As JSON documents
B. As flat files in block storage
C. In compressed, columnar format on cloud storage
D. In traditional row-based database tables
Correct Answer: C. In compressed, columnar format on cloud storage
Explanation: Snowflake stores data in a compressed, columnar format in cloud-based object storage for performance optimization and efficient querying.
4. Which cloud providers does Snowflake support?
A. AWS, Google Cloud, Microsoft Azure
B. AWS, Oracle Cloud, Microsoft Azure
C. Google Cloud, IBM Cloud, AWS
D. Microsoft Azure, IBM Cloud, Oracle Cloud
Correct Answer: A. AWS, Google Cloud, Microsoft Azure
Explanation: Snowflake is a cloud-agnostic platform that runs on AWS, Google Cloud, and Microsoft Azure, providing flexibility to users.
5. What role does the Cloud Services Layer play in Snowflake?
A. It processes queries and executes computations
B. It manages metadata, authentication, and query optimization
C. It stores the actual data blocks
D. It acts as a caching layer for queries
Correct Answer: B. It manages metadata, authentication, and query optimization
Explanation: The Cloud Services Layer handles metadata management, security, query optimization, and access control, ensuring seamless Snowflake operations.
6. What is the primary benefit of Snowflake’s separation of storage and compute?
A. Faster disk access times
B. Ability to scale compute and storage independently
C. Elimination of metadata management
D. Reduced need for indexing
Correct Answer: B. Ability to scale compute and storage independently
Explanation: Snowflake allows users to scale storage and compute separately, optimizing cost and performance based on workload requirements.
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