Snowflake vs AWS-native Redshift: Data Warehouse Comparison

Snowflake vs Redshift: The Battle of the Data Warehouses – Full Comparison

The key feature for businesses to be successful and grow rapidly is to have intelligent insight of the data they store, and use that insight to make well-informed, analytical decisions.

A data warehouse stores operational data and becomes an electronic library, which not only secures data but also enables business intelligence activities. The stored data can be analyzed and used to enhance the organization’s performance.


Snowflake is a cloud-based data warehouse that provides analytic insight to both structured and nested data. It works as Software-as-a-Service (SaaS) and enables you to build a modern data architecture to store and scale data flexibly and independently.

Moreover, Snowflake is not built on an existing database, or on a software platform. It uses an SQL database engine, which is designed for the cloud, making it easier for anyone who knows SQL to understand and work with it.

Snowflake has a flexible, faster, and easy-to-use architecture, which allows seamless data sharing and addresses concurrency issues. Snowflake’s architecture allows you to build multiple data warehouses over the same data; data analysts and engineers can get what they want, including queries, at a fast pace without interruption.

What is AWS Snowflake?

With the built-for-the-cloud architecture, Snowflake is competent to many different cloud platforms, including AWS. AWS Snowflake fits perfectly with the AWS data ecosystem. It runs on Amazon Elastic Container Service (EC2) and Amazon Simple Storage Service (S3). It provides fast data analytics, advanced reporting and controlled access to data, and much more to all AWS users. 

Amazon Redshift

Amazon Redshift is a fully functional data warehouse that is part of the additional cloud-computing services provided by AWS. It is designed for businesses to store, expand, and analyze large data troves and get real-time analytical insights.

Redshift uses SQL and ETL tools to optimize your queries and give you real-time operational insights (more on Redshift streaming). It works with compute resources called nodes, which are collected in groups called clusters.

Redshift also uses business intelligence (BI) tools and gives you a cost-effective infrastructure to perform queries on petabytes of data so you can get fast, analytical insights to your data.

What is Amazon Redshift Spectrum?

Amazon Redshift provides a feature called Amazon Redshift Spectrum that provides data analysts with quick and comprehensive analysis of the data. They can perform SQL queries directly on the data stored in Amazon S3 buckets without having to transfer them to the databases.  Redshift Spectrum extends your Redshift data warehousing and offers multiple features; fast query optimization and data access, scaling thousands of nodes to extract data, and many more.

Common Features of AWS Snowflake & Amazon RedShift

Living in a data driven world, today data is growing exponentially, every second. It is important to know which data warehouse will be suitable for your business.

Snowflake and Amazon Redshift are the two data warehouses most in demand today. Both of them have their own features and advantages. The two warehouses have the following common features:

  1. They are both accessed by SQL and integrated with ETL and BI tools.
  2. They both use massive parallel processing architecture.
  3. They are both designed for users to manage their data intelligently and make data driven decisions to improve their workload performance.
  4. They are both column-oriented.

Key Differences Between Snowflake and Redshift

Both data warehouses have their own special features. It is important to know which data warehouse will be suitable for your business.

Below, we compare Redshift and Snowflake features to give you an understanding of the key differences of both data warehouses.


Snowflake is entirely built on the cloud; it has no physical infrastructure meaning you will not be needing a team for the maintenance of any virtual or physical hardware. Snowflake almost automatically maintains your software and data.

Redshift requires a lot of manual maintenance. You will have to ensure that you are running your commands, updating rows and monitoring your clusters for better performance.


Redshift has relatively lower cost than Snowflake. With this you can optimize how you pay for your costs, with or without commitment. You can save up to 75% by committing for 1 to 3 years for the Reserved Instance pricing model. There are many other many other models and options for you to make an informed and cost-effective decision.

In Snowflake, you can remove the idle servers and no cost will be incurred to them. This warehouse also separates your cloud and storage, which results in a reduction of the cost as you will be paying for what you use; storage costs are different from computational costs.


Redshift doesn’t scale up and down easily. This is because Redshift adds or removes new nodes to every cluster. It does not have the auto-scaling feature and so it can take anywhere from minutes to hours, depending on the size of your clusters, to scale and resize your data.

Snowflake seamlessly auto-scales without any delay, in seconds or minutes. This is because data is stored separately from the computing clusters. Therefore, the system allows data to be shared without interrupting the computing workload or the users– allowing fast scaling without any interruptions.

Basic Database Features

Redshift gives you elasticity to operate your database performance. With different features like distribution, partitioning, and so on, you can optimize your performance and your tables.

Redshift does not support nested data types.

Snowflake, however, does not provide all these features. It automatically optimizes your performance, giving you fewer choices to customize your data and how you choose to store it.

Snowflake has more robust support for JSON based functions and queries.

Snowflake supports nested data types and sharing data between different accounts, without copying them.

Data Security

Both warehouses provide the best possible models to keep your data secured.

Redshift provides a more, flexible and customizable end-to-end encrypted security model. You can enforce the security and compliance features according to your requirements, to make sure you clusters, data files, and connections are protected.

However, Snowflake provides a more strict security model. It features always-on encryption, depending on the edition of the product you’re using. So choose the edition you think will be suitable for your system.

Suitable Applications

Deciding the right data for your business organization depends on your business requirements and resources.

Redshift is the right choice if you

  • plan to use AWS services
  • have a high query load
  • have a completely structured data
  • are ready to commit for a year or more than a year for your busy clusters

Redshift features also include Spectrum that can help in querying your data and in getting a comprehensive analysis on your stored data in Amazon S3.

Snowflake is the right choice if you

  • are an organization, with a low-query load
  • require frequent scaling, up/down
  • want an automated solution with no maintenance


Today, our data is growing exponentially. Data warehousing allows you to store your operational data to make analytical and intelligent decisions to improve your organization’s workload performance. In this article, we’ve discussed the two major data warehouses, Snowflake and Amazon Redshift, so you can make a well-informed decision in choosing between the two. 

Published in: Blog , Cloud Architecture
Upsolver Team
Upsolver Team

Upsolver enables any data engineer to build continuous SQL data pipelines for cloud data lake. Our team of expert solution architects is always available to chat about your next data project. Get in touch

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