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October 22, 2021

Difference Between Redshift and Snowflake


Artur Haponik

CEO & Co-Founder

Reading time:

11 minutes

When it comes to data warehouse implementation, there are several options for you to choose from. However, in the last few years, two solutions have come to the forefront. These are Amazon Redshift (a part of Amazon Web Services) and Snowflake, a standalone solution designed by a company under the same name. In this article, we are going to take a closer look at both these data warehouse solutions. What do you need to know about each one of them? And which one should you pick? In a few moments, you will discover answers to these questions.

At Addepto, we work with various data warehouses on a regular basis. These are some of our primary tools that are useful when it comes to projects based on:

  • Machine learning
  • Data analytics
  • Business intelligence

You might even say data warehouses are at the very center of what we do every day. And difficult not to mention Amazon Redshift and Snowflake when discussing data warehouse solutions. Many organizations wonder which one is better. Today, you will discover answers to these questions, as we’re going to do a small Snowflake vs. Redshift comparison.

Let’s get down to business!

What do you need to know about Amazon Redshift?

The very first thing you need to know is that Redshift is a part of the larger AWS environment. It’s a fully managed data warehouse solution that’s available only in the cloud computing model. You should use Redshift to store big data and conduct database migrations, even extensive ones.

One of the biggest advantages of Redshift is that it works brilliantly with diverse data sources and data analytics tools. In order to make the most of Amazon Redshift, you ought to start with the ETL process, which is indispensable when it comes to data warehousing. Some time ago, we talked a lot about the ETL process on our blog.

amazon redshift

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And because Redshift is a part of the Amazon AWS platform, you have quick and easy access to other Amazon cloud services, including Amazon S3.

The architecture behind Amazon Redshift

Redshift has a unique architecture that makes this solution stand out from its competition. Let’s examine some of the most important features of this data warehouse solution:

  • Columnar storage[1]: As Amazon informs us on their website, it’s a very efficient solution because it enables optimizing analytical query performance. This columnar design reduces the overall disk requirements and reduces the amount of data you need to load from the disk, making your work far more effective.
  • Intuitive dashboard: Redshift comes with a ready-made console for administrators to create, configure, and manage Amazon Redshift clusters. Within the Redshift dashboard, you have easy access to the current number of clusters and nodes, cluster health status, critical performance metrics, and performance workloads.

Take a look at how it looks like:


redshift console

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  • Clusters: Since we’ve already tackled this subject, it is vital to emphasize that Redshift’s infrastructure is based on clusters, which are based on multiple nodes (it’s where you store data tables). These nodes, in turn, are composed of smaller slices. The number of slices per node depends on the node instance types. Therefore, we can indicate three types of instances in Redshift: Dense Compute (dc2), Dense Storage (ds), and Managed Storage (ra3)[2]. Such a way of storing data enables processing even large volumes of big data–quickly and efficiently.
  • Data security: The good news is, Amazon pays a lot of attention when it comes to the cybersecurity of their services. That said, Redshift comes with a safety feature enabling you to utilize database encryption for your clusters in order to protect data at rest. Mind you, although additional data security measures are optional, you should make sure your assets are properly secured, especially when you deal with sensitive personal or financial data. If you want to find out more, read our Big Data Security Issues and Challenges blog post.

With Amazon Redshift done, we can switch to Snowflake. What do you need to know about Redshift’s main competitor when it comes to data warehouse solutions?

Snowflake: Redshift’s major competitor

Generally speaking, there are lots of similarities! Both solutions are cloud-based, both are offered in the SaaS model, and both can be used to store, process, and analyze large volumes of data. Moreover, Snowflake is even built on top of the Amazon Web Services or Microsoft Azure cloud infrastructures![3]

However, when it comes to Snowflake, you should be aware of a couple of differences before making a decision. For starters, Snowflake is based on an SQL database engine that’s designed with cloud computing purposes in mind. Secondly, Snowflake emphasizes the sharing functionality, allowing users to share data freely in real time. And lastly, Snowflake can store different forms of data, including structured and semi-structured data.

Now, let’s talk a bit more about Snowflake’s architecture.

Snowflakes’s architecture

One of the most significant Snowflake’s differentiators is that this platform automatically manages all aspects of data storage, from organization, through compression, up to metadata and statistics.

Interestingly, this advanced storage layer runs independently of computing resources. This means that users get more flexibility and don’t have to pay for the resources or services they don’t need.


snowflake architecture

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According to, Snowflake is composed of three separate layers. Each of these layers is fully independent and scalable. What do you need to know about them?

  • Database storage: We’ve already mentioned this layer. This is where your data is stored and processed.
  • Compute layer: Here, you have virtual data warehouses (which can be viewed as clusters, just like in Amazon Redshift) that execute diverse data processing tasks.
  • Cloud services: This layer is based on ANSI SQL, and you can say it supervises and manages the entire Snowflake system. This is where infrastructure management, metadata management, and access control happen.

Snowflake vs. Redshift comparison: Which one should you pick?

Snowflake and redshift are superior in their own distinct ways. And therefore, the choice between the two data warehouses is relative to your data strategy. To help you determine which solution is best for your organization, we are going to compare them against each other based on their pricing, security features, maintenance, and performance. Read on for more insights.

Costs of use

Which solution is more economical than the other? There is no straightforward answer to this question since your bill is tied down to your use case. This means that you pay according to your demand and volume. The only point of distinction here is that the two data warehouses have varied pricing models for different plans.

Snowflake uses a pay-as-you-use pricing strategy. This may be an appropriate option for minimal query usage spread across a wide time interval. The clusters will automatically shut down when no queries are running and resume after you load the queries. This can significantly reduce your expenditure when your query load decreases.

However, it’s hard to predict Snowflake’s cost since its computational processes are isolated from the warehousing process. This also means that the computational pricing is discrete. The platform offers seven grades of data warehousing options, with each grade having different prices. And since the computation pricing is discreet, it can be hard and confusing to calculate the overall price. Consequently, this makes Snowflakes more expensive in most use cases.

Redshift, on the other hand, offers a more flexible payment model. Its pricing is based on the total number of clusters and the total number of hours. To calculate your monthly price, you multiply the size of the cluster by the cost per hour and the number of hours in a month. The hourly price is standard for all users, while the size of clusters varies from one business to another.

Snowflake VS. Redshift: cybersecurity measures

Big data security is a crucial aspect that you should keenly scrutinize when choosing a data warehouse. Even with security systems that offer a lot of scrutiny, data breaches still occur. This mainly happens due to a lack of two-factor authentication or when employees share login credentials through social media.

When it comes to data security, it’s not about Redshift vs. Snowflake, as the two platforms offer stringent data security measures. However, they have slightly different approaches. So, to help you understand how the two platforms differ security-wise, we have compiled a list of their respective features below.

Security features of Redshift

Cloud security is a top priority for Redshift. It offers a data center and an architecture built to satisfy the needs of security-sensitive businesses. Access to the platform is controlled at four levels:

  • Cluster Connectivity: Redshift’s cluster is locked by default, so nobody can access it unless they’re authorized to do so. To grant access to other users, you should associate the cluster with a security group. A cluster security group has a set of rules that identify IP addresses or an EC2 security group[4] that’s authorized to access your cluster. When you first launch your cluster, Redshift automatically creates a default security group that’s empty. You can add your own rules to the group, then associate it with your Redshift cluster.
  • Cluster Management: The Identity Access Management (AIM) user grants permission to create, configure, and delete clusters. AIM users can use AWS Command Line Interface (CLI), AWS Management Console, or Application Programming Interface (API) to manage their clusters.
  • Database Access: Access to database objects such as views and tables is granted by user accounts. You can only use the resources that your user account has been authorized to access. You can create your user account and manage its permissions by using GRANT, CREATE GROUP, CREATE USER, and REVOKE SQL.
  • Temporary Credentials and Single Sign-on: You can configure your SQL client with ODBC or JDBC drivers to manage the process of creating temporary passwords as part of your account’s login process.

Security features of Snowflake

  • Network Policy: It grants or denies access to the platform’s URL from certain IP addresses/ ranges.
  • Account authentication: It supports OAuth, single sign-on, multi factor authentication (MFA), and key pair authentication for secure connection to the platform
  • User and Administration Group (SCIM Integration): Snowflake uses SCIM to manage users and groups in cloud applications.


Snowflake security feautures

  • Role-Based Access Control (RBAC): RBAC[5] is an access control framework that gives control over how users can access objects. It also allows actions to be performed on objects.
  • Default Data Encryption: Snowflake offers encryption to data in In-Transit and at REST using AES 256 encryption. The encryption has multiple levels of keys and key rotation to keep the data secured.

Both Snowflake and Redshift offer two-factor authentication, but the key point of differentiation is that Snowflake’s scope of compliance options and security depends on the edition that you’ve opted for.

Snowflake VS. Redshift: Usage and maintenance

Previously, Snowflake had an added advantage over Redshift due to its automated maintenance.

However, the playground was leveled after Redshift introduced its auto vacuuming, improved queues leveraging machine learning, auto workload management (WLM), and more. These tools have drastically reduced Redshift’s maintenance.

Snowflake, however, still has the upper hand when it comes to scaling up and down. With this platform, you can resize in a matter of seconds, something which takes a lot of time in Redshift. This is because Snowflake has separate compute and storage space, so it doesn’t have to copy any data to scale up and down.

So which platform should you choose?

Snowflake or Redshift? The choice between the two data warehouses is subject to your business needs.

For example, if your business manages massive workloads, then the best choice would be Redshift because it’s cost-effective and its pricing structure is flexible.

You should take time and evaluate whether a particular data warehouse solution matches your needs. Set up a free trial to taste the waters before settling for a solution. And if you’re looking for help with your choice –remember that the Addepto team is at your service!


[1] Columnar Storage. URL: Accessed Oct 18, 2021.
[2] Amazon Redshift Architecture. URL: Accessed Oct 18, 2021.
[3] What is a Snowflake Data Warehouse? 5 Benefits to Your Business. URL: Accessed Oct 18, 2021.
[4] How to Create Amazon EC2 Security Groups. URL: Accessed Oct 18, 2021.
[5] What is Role-Based Access Control (RBAC)? URL: Accessed Oct 18, 2021.


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