Snowflake Architecture: A Simplified Guide for Beginners
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Following are the concepts covered in Snowflake Architecture:
Table of Contents
What is Snowflake?
Snowflake is a cloud data warehouse platform offered as Software as a Service (SaaS). This advanced platform is used for data warehousing, data engineering, data application development, data science, data analytics, data lakes, and for securely sharing and consuming shared data. Snowflake is highly scalable and supports a near-unlimited number of concurrent workloads.
The Snowflake architecture is a hybrid model of shared-disk and shared-nothing database architecture. Having a clear overview of these two architectures would help us in an easy understanding of Snowflake architecture.
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Shared-Disk Architecture
The Shared-disk architecture is a distributed computing architecture used in traditional databases and consists of a single storage layer accessible by all cluster nodes. In this architecture, the nodes share the same disk devices but have their own private memory and CPU. It communicates with the central data storage layer to source data and processes it.
Shared Nothing Architecture
It is quite opposite to shared-disk architecture, and consists of distributed cluster nodes, along with their own CPU, disk storage, and memory. Here the advantage is that the data can be divided and stored across cluster nodes.
Snowflake Architecture
Snowflake is natively built for the cloud and comes with a unique multi-cluster shared data architecture. This advanced architecture has been designed to deliver the performance, elasticity, scalability, and concurrency demanded by modern organizations.
Snowflake Architecture is a combination of shared-disk (SD) and shared-nothing database architecture. Snowflake uses a central data repository for persisted data same as shared-disk architectures and makes the data accessible from all nodes in the platform.
Snowflake works similar to shared-nothing architecture to execute queries using MPP (massively parallel processing) compute clusters. Here each node in the cluster stores a small portion of data and that becomes the entire data set stored locally.
The main reason behind following hybrid architecture is to offer data management simplicity to its customers using shared-disk architecture and high performance and scalability using shared-nothing architecture.
The following Image gives you a clear overview of Snowflake Architecture:
Snowflake architecture consists of three key layers which include the Cloud services layer, the Query processing layer, and the database storage layer. Let’s understand how each layer works.
Database Storage Layer:
Each time data is loaded into Snowflake, Snowflake organizes data into internal optimized, columnar and compressed format. After organizing the data, Snowflake stores this data in cloud storage.
Snowflake takes care of multiple aspects of data storage, which include file size, compression, data structure, statistics, metadata, and much more. All the objects stored in the Snowflake are not directly accessible or visible to customers. Users can only access the data stored in the Snowflake by running SQL query operations on it.
Query Processing Layer:
This is the layer where query execution is performed. Snowflake uses virtual warehouses to process queries. Every virtual warehouse is an MPP (massively parallel processing) compute cluster and consists of multiple compute nodes allotted by Snowflake from a cloud provider.
In Snowflake Each virtual warehouse is independent and does not rely on or share resources with other virtual warehouses. This enables the virtual warehouses to scale independently without affecting the performance of other warehouses.
Cloud Services:
The cloud services layer contains a group of services that coordinates activities on the Snowflake cloud warehouse platform. All these services integrate different components of Snowflake to execute user requests right from the login to query dispatch.
Following are some of the notable services managed in this layer:
- Authentication
- Metadata management
- Access control
- Infrastructure management
- Query parsing and optimization
Snowflake Architecture Features
Following are the Snowflake Architecture features:
1) Cloud Agnostic:
Snowflake doesn’t have its own cloud and can be deployed on top of public cloud providers like Amazon, Microsoft Azure and Google Cloud Platform. It comes with a common and interchangeable code base that allows the users to move data to the cloud across in any change.
2) Separate Storage and Compute:
One of the notable points about the Snowflake architecture is that it separates storage from computing. This means the users do not have to compete for resources. Moreover, there is no limit on the number of queries or workloads running and also allows limitless accessibility to users. All workloads can easily use the computer power 24/7.
3) Supports a Range of Data Types:
Snowflake supports multiple data types and stores them in their original form. It also prevents users from creating new data silos.
4) Scales Elastically:
Snowflake offers automatic cloud elasticity. Snowflake instantly adds the extra resources on-demand and customers need to pay only for the resources they have used.
Closing Thoughts
The Innovative Snowflake architecture allows data storage, processing and analytic processes that are more flexible, faster, more economical, and easier to use. Hope you have got a clear idea of Snowflake architecture, how it stores and manages data. To gain a deep understanding of Snowflake you can also check out our Snowflake tutorial here.
Author Bio
Yamuna Karumuri is a content writer at CourseDrill. Her passion lies in writing articles on the IT platforms including Machine learning, Workday, Sailpoint, Data Science, Artificial Intelligence, Selenium, MSBI, and so on. You can connect with her via LinkedIn.
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