14 Key Snowflake Features: The Ultimate Guide

Thalia Barrera
February 9, 2024
15 mins

With the increasing amount of data, managing and utilizing it effectively has become a necessity. Cloud-based data warehouses have emerged as a solution that offers flexibility, scalability, and high performance. If you're looking for a modern data warehousing solution, Snowflake is a great option to consider. This article examines the key Snowflake features and explores why it has become a popular choice.

What is Snowflake?

Snowflake is a cloud-based data warehousing platform that provides a fully managed solution for storing and analyzing vast amounts of data. It doesn't use existing database technology or big data software platforms like Hadoop for data processing. Instead, Snowflake has an entirely new SQL query engine combined with a unique cloud-native architecture.

Snowflake can seamlessly integrate with popular cloud providers such as AWS, Azure, and Google Cloud Platform. It can be scaled up or down automatically, ensuring efficient data loading, integration, and analysis. This allows multiple users to run numerous workloads simultaneously without concerns about resources. 

Overview of the Snowflake Architecture

Snowflake architecture combines features of both shared-disk and shared-nothing database architectures to leverage the benefits of each. Let's take a closer look into the details of these approaches.

  • Shared-disk Architecture: In this architecture, multiple cluster nodes (processors) that can access all the data stored on a shared memory disk are utilized. These nodes have CPU and memory but no disk storage of their own. Instead, they communicate with a central storage layer to retrieve data.
  • Shared-nothing Architecture: Data is partitioned and distributed among different nodes, which process the data independently and in parallel. Each node has its disk storage, and there is no central storage layer.

Snowflake is able to provide fast results by combining the advantages of two database architectures: shared-disk and shared-nothing. It uses a central repository, like a shared-disk database, where data is stored and accessible from all compute nodes. 

However, Snowflake also employs MPP (massively parallel processing) compute clusters for processing queries, similar to a shared-nothing architecture, where each node stores a portion of the data set locally.

Snowflake architecture mainly consists of three layers: cloud services, query processing, and storage. Let’s take a look at each of them in detail:

Database Storage Layer

The data in Snowflake is organized into multiple micro partitions, which are compressed and optimized internally for better performance. It follows a columnar format for storage, resulting in significantly faster querying. Snowflake utilizes the cloud to store data objects and maintain privacy by keeping them hidden and inaccessible to others. Access to these objects is only possible through SQL query operations using Snowflake.

Query Processing Layer

This layer is responsible for executing queries against the data in the storage layer. Query processing is carried out by Virtual Warehouses, which are computing units consisting of multiple nodes featuring Snowflake-provisioned CPU and memory. Snowflake supports the creation of multiple Virtual Warehouses, allowing you to allocate resources based on the specific workload. These warehouses can be started or stopped at any time and scaled up or down without affecting running queries.

Cloud Services Layer

This layer manages operations such as authentication, security, data management, and query optimization. It uses stateless computing resources that operate across different availability zones and provide highly accessible and usable information. Cloud Service layer offers a SQL client interface to interact with the Snowflake platform. This interface supports DDL and DML for defining database objects and for querying data.

With a detailed understanding of Snowflake's architecture, let's explore the key features that make Snowflake a leading cloud data platform.

14 Key Features of Snowflake

Snowflake offers several distinctive features that set it apart from other cloud-based data warehouse solutions. Here are a few of them:

Near-Zero Management

Snowflake offers near-zero management because it's a cloud-based, fully managed platform that requires no hardware to select, install, configure, or manage. The platform features auto scaling, auto suspend, and in-built performance tuning capabilities that eliminate manual administration. This means you can focus on data and analytics instead of spending time on resource management.


With Snowflake's auto-scaling feature, the warehouse size can automatically adjust based on the demand. This ensures that the system can efficiently handle varying workloads without manual intervention. Snowflake continuously monitors the workload, including query complexity, resource usage, and concurrency, to determine scaling actions. 


The cloning feature, also known as zero-copy cloning, is a fast and cost-efficient way to create a copy of any table, schema, or the entire database. The clone is a logical copy of the original object and points back to the original data. This means that cloning is instantaneous and doesn't use additional memory until changes are made to the new copy.

Time Travel

With Snowflake Time Travel, you can easily access historical data that may have been altered or deleted within a certain timeframe. This enables you to retrieve previous versions of data, providing a comprehensive view of data changes over time. In addition to this, Time Travel simplifies auditing and compliance requirements by providing you with precise control over data versions. This way, you can easily manage and keep track of different versions of your data.


Snowflake incorporates a fail-safe feature that allows for the recovery of any data that has been lost or damaged due to critical operational failures. During the time travel period, Snowflake stores deleted or updated data in the history for up to 90 days. Once this period elapses, the fail-safe stores the data for an additional seven days as a backup. This approach ensures an effective and cost-efficient method of recovering data with minimal effort.

Data Sharing

Snowflake's data sharing feature is quite interesting as it allows you to share your data with others without creating a new copy of the existing data. It means that no actual data is copied or transferred between accounts, and all sharing is done through Snowflake's services layer and metadata store. Therefore, you have to pay only for the computing resources that are used to query the shared data, as storage is not utilized.

Data Caching

Snowflake has a caching mechanism that helps to speed up frequently executed queries. This reduces the time it takes to retrieve data from storage. When a query is executed, Snowflake first checks if the required data is already cached. If it is, the query can be fulfilled directly from the cache, significantly reducing the time required to fulfill the query. 


As Snowflake automatically manages failover and resource allocation, you are unlikely to notice any impact of hardware failures or disruptions. This ensures uninterrupted access to your data, maintaining operational continuity.

Micro-Partitioned Data Storage

In Snowflake, data is stored in encrypted compressed files called micro-partitions. This approach allows Snowflake to scan only the necessary micro-partitions instead of entire tables, which can significantly improve query performance.

User-friendly Interface

Snowflake offers a user-friendly interface, making it easy for users of all levels to work with data. The platform features a web-based interface that enables you to easily manage and manipulate data without needing to write complex code or queries. 


Snowpark is a collection of intuitive libraries that lets you process non-SQL code within Snowflake. You can write in Java, Python, or Scala, whichever language you prefer, and execute it within Snowflake’s virtual warehouses. One of Snowpark’s advantages is that it eliminates the need for additional computing, configuration, or maintenance.

Automatic Performance Tuning

Snowflake offers a unique and powerful feature that enables automatic query performance optimization. The platform comes equipped with a robust query optimization engine that can automatically fine-tune query settings. This allows you to seamlessly query large datasets without spending time on manual tweaking or configuration. 


Snowflake offers advanced features that ensure optimal protection for your account. You can be assured that all your data stored in Snowflake is safeguarded using industry-leading security measures. This includes end-to-end encryption, data masking techniques, and access controls. Furthermore, it is SOC 2 Type II certified, which guarantees compliance with the highest security standards.


Snowflake offers a simplified pricing experience based on a pay-per-use model. This lets you only pay for the storage and computing power used to process a request on a per-second basis. You won't have to worry about any upfront costs. It is highly flexible, allowing you to scale your usage according to your needs while only paying for the resources you use.

Streamline Data Integration with Airbyte's Snowflake Connector

Numerous Snowflake features help you perform analytics seamlessly. You should streamline your data collection process to make the most out of your data. Although Snowflake's native data ingestion tools can be used to load data from various sources, it often requires programming expertise. Therefore, to overcome this, it is recommended to consider utilizing no-code data integration platforms such as Airbyte to fully automate your data pipelines.

Airbyte allows you to consolidate data from 350+ sources, including MySQL, Salesforce, Redshift, Postgres, and many more, to Snowflake. The platform's intuitive interface requires no coding skills, making it accessible to users of all technical abilities. This helps to speed up the data integration process, allowing you to get insights from your data faster.

Some of the key features of Airbyte include:

  • If you don’t find the desired connector in the pre-built list, you can build a custom connector using Connector Development Kit (CDK) without writing a single line of code. 
  • Airbyte also supports Change Data Capture (CDC) to ensure that changes made to the source systems are synchronized and captured into the target systems.
  • For customized transformations, Airbyte allows you to integrate it with dbt, a data transformation tool.

Wrapping Up

To sum up, Snowflake's key features offer an unparalleled data warehousing experience that is both efficient and reliable. With its cloud-based architecture, support for multiple data types, and native machine learning capabilities, it is the perfect solution to manage large volumes of data while maximizing its performance. 

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