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Table of contents
Snowflake and its Features
Architecture of Snowflake
Database Storage
Compute Layer
Cloud Services
Advantages of Snowflake
Performance and Speed
Accessibility and Concurrency
Availability and Storage
Seamless Data Sharing
Data Warehouse and Snowflake
Use of Data Warehouse
Snowflake Method
Key Takeaways
Last Updated: Mar 27, 2024


Author Naman Kukreja
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In today’s modernizing world, data has become one of the most vital necessities for everyone. It doesn't matter if the user and the provider both need the data in their way and if they are consuming data and performing operations accordingly. They must have a place to store the data.

Either small or big firm of them has their databases. The main challenge most firms or companies face in starting is having a data warehouse of their own. Earlier, purchasing a data warehouse was a very expensive task. With the space to store data, you also need proper software and application to analyze the data correctly.

That’s where snowflake comes into play. We will learn all about snowflake while moving further in the blog. So without wasting any further time, let’s proceed with our topic.

Snowflake and its Features

Snowflake saves your space, storage for creating data warehouses, data marts, data lakes, etc. Snowflake is a cloud-based platform used to eliminate the need for data warehouses and other stuff.

It is based on the top of Microsoft Azure, Amazon Web Services, Google Cloud infrastructure. There is no software or hardware to install, select, manage, or configure, and because of that, most companies don't want to spend their resources setting up and maintaining the servers. So snowflake takes advantage in this case as we can quickly move the data in the snowflake using Stitch, which is an ETL solution.

Its data sharing capabilities and architecture make it different. Its storage allows computing and storage to scale independently, making it very useful for customers as they can pay for computing and storage alone.

It also has a sharing feature that makes it more trustworthy among companies.

Earlier, when you purchase the infrastructure, then you have to buy the content along with it, for example, buying the cable TV connection earlier, you have to buy the content with the infrastructure, but with the use of snowflake, the user has control over what they need and then pay accordingly.

All the companies don't have the same need. Some need more storage and minor CPU cycle and vice versa. So they need not pay for integrated bundles, and users can pay for only the resources they use. Time is calculated in seconds, whereas storage is calculated with the terabytes stored per month.

Architecture of Snowflake

Architecture defines or describes the structure of the software. Snowflake is mainly made of three layers Storage, compute, and services, and each of them is independently scalable.

Database Storage

As the name suggests, it is a database containing all the data loaded in snowflake, including semistructured and structured data. Snowflake automatically manages all the aspects of data storage, i.e., file size, organization, compression, structure, statics, and metadata. The database layer runs independently from any other layer like compute layer.

Compute Layer

The compute layer comprises warehouses, not real warehouses but virtual warehouses, and they execute the data processing task required by queries. Each virtual cluster or warehouse can access all the data present in the above layer, i.e., the storage layer. After getting the data to work independently, warehouses cannot compete for computer resources. This has many advantages, like automatic scaling enabling non-disruptive, which means compute resources can rebalance or redistribute the data in the storage layer while the queries are running.

Cloud Services

The cloud service is the third layer in the architecture that coordinates the entire system and uses ANSI SQL. It is beneficial as it eliminates the requirement of manual data warehouse tunning or synchronization. The services in this layer include:

  • Authentication
  • Metadata management
  • Access control
  • Query parsing and optimization
  • Infrastructure management.

Advantages of Snowflake

Snowflake is built to solve many problems users or providers face in hardware warehouses, such as data transformation, failure, or high query volume delays. It uses the cloud to solve these problems. Below are some of the benefits or advantages that snowflake gives us:

Performance and Speed

We can store as much data in the cloud according to our requirements, whether it is large or small, and because of this, it is referred to be elastic. It is also known as a virtual warehouse, and you can take advantage of this in computing faster and better. You can pay for the virtual warehouse only when you use it, which is also cost-effective.

Accessibility and Concurrency

In an old method of warehouses like traditional hardware warehouses, the issues regarding concurrency will be standard, leading to failures and delays when a large number of queries come at the same time.

Snowflake resolves this problem with the help of its unique multicluster architecture. In this, the queries from one virtual warehouse do not interfere with any other question, and because of this, data scientists can get the result of what they want without waiting for the whole process to complete.

Availability and Storage

Snowflake is readily available to everyone. It is divided into different platforms like AWS, azure. It is designed to work consistently and can tolerate error like network failures, and can solve it with just a minimal effect on customers. It has a different level of security, such as encryption on all network communication are done.

Seamless Data Sharing

The architecture of snowflake allows data sharing among snowflake users. It also will enable organizations to share data with their customers or non-customers. It can use a reader account for non-users, and with the help of this, they can create a snowflake account.

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Data Warehouse and Snowflake

You need to understand a slight difference between a database and a data warehouse that a data warehouse is a relational database, but like regular databases, it is not used for transactional work. It is used for analytical work. So it aggregates and collects data from various sources and analyses the data.

Use of Data Warehouse

Database warehouse has two key features or functions. First, it integrates and gathers the data needed by the business. Second, it is used as the processing engine and query execution and enables the users to interact with the data present in the database.

Complex queries are difficult to conduct without pausing database update processes for a short period. Data mistakes and gaps will unavoidably result from a regularly interrupted transactional database. As a result, a data warehouse acts as a separate platform for aggregating data from numerous sources and then performing analytics on that data. Because of this separation of roles, databases may focus solely on transactional tasks without being interrupted.

Snowflake Method

The Snowflake Cloud Data Platform contains a built-from-the-ground-up cloud-based SQL data warehouse. It combines high performance, concurrency, simplicity, and affordability at levels not conceivable with existing data warehouses, thanks to a unique novel architecture designed to manage all aspects of data and analytics.

Snowflake physically separates storage, computing, and services yet logically blends them (like metadata and user management). Snowflake can be more responsive and adaptive since each component is separate and can be enlarged and contracted separately.

Snowflake uses a centralized persisted data store that can be accessed from any compute node. Snowflake, like shared-nothing architecture, uses MPP (massively parallel processing) compute clusters to process queries. Each node in the cluster keeps a subset of the complete data set locally in this configuration.

Snowflake may also function as your data lake while keeping low cloud data storage costs. A Snowflake data lake can natively ingest and query a wide range of different data formats in a relational format with complete transactional ACID integrity, ranging from JSON, CSV, and tables to Parquet, ORC, and more.

Data Lake, Data Sharing, Data Marketplace, and elastic infrastructure and integrations for Data Engineering, Data Application Development, Data Science, and AI and ML projects are all part of the Snowflake Cloud Data Platform.


1. What are the unique features of snowflake architecture?
It has unique features like query processing, data storage, cloud services, etc.

2. Mention some ways to access snowflake’s data warehouse.
Some methods to access snowflake's data warehouse are python libraries, Web user interface, JDBC drivers, etc.

3. List some benefits of the snowflake database.
Snowflake provides us with many benefits, and some of them are high security, high availability, concurrency, and accessibility.

4. What is a data warehouse?
A data warehouse can be understood as a relational database used on an enterprise server.

Key Takeaways

In this article, we have extensively discussed what a snowflake is, its architecture, its advantages, and with this what is a data warehouse, how the snowflake is used in the data warehouse, and much more.

We hope that this blog has helped you enhance your knowledge regarding data warehousing and if you would like to learn more, check out our articles on Code studio. Do upvote our blog to help other ninjas grow.

 “Happy Coding!”

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