Snowflake vs AWS Redshift: Data Warehouses Compared (2024)

As the data warehousing industry grows and expands, more and more businesses leverage the cloud. This helps them cut costs and increase efficiency. This is where Snowflake and Amazon Redshift enter the picture. These are two of the leading cloud-based data warehousing solutions available today.

Snowflake was designed from the ground up to take advantage of the cloud. Redshift is an evolution of AWS’s existing data warehouse infrastructure.

The primary difference between these two platforms is in how they handle workloads. This article will look at the difference between Redshift and Snowflake.

How To Choose The Right Data Warehouse?

To determine which data warehouse is right for your business, it’s essential to look at the differences between Snowflake vs Redshift.

When you look at Redshift vs Snowflake, you’ll realize they are cloud-based data warehouses that accommodate large datasets.

Let’s look at the features of each platform in detail so you can decide which one is right for your business.

What is AWS Snowflake?

AWS Snowflake is a data warehouse. It works just like a database or a data mart, but it also has the capabilities of a data lake.

While traditional databases and data marts store data in tables, Snowflake uses a new SQL database engine. This comes with an innovative architecture to completely separate storage from computing.

This is what gives Snowflake its power: the ability to scale, both in storage and compute resources, independently.

A traditional database system, whether it’s on-premises or in the cloud, is composed of three parts. There is the storage layer (where the data resides), the compute layer (where queries are run), and the services layer (where queries are prepared and optimized for execution).

All three layers exist together on one server or virtual machine in a traditional database system. That means that when you need more storage capacity, you have to buy more disks to add to your server. When you need more computing power, you have to buy more memory and CPU cores to add to your server.

The result?

You end up either paying for much more capacity than you use—or worse—you get bottlenecked by your limited capacity.

With Snowflake, you can grow your storage and compute resources independently. This happens without downtime or performance limits.

Benefits Of Snowflake

How is snowflake different from WAS?

Well, Snowflake is a data warehouse that can be used for cloud-based analytics. Snowflake also provides support for structured, semi-structured, and unstructured data.

Snowflake combines the power of data warehousing, big data, and the cloud. It is also very scalable, especially compared to its competitors. 

When Is Snowflake Used?

Snowflake is used for data warehousing. Data warehousing is a process for storing and managing large amounts of data in a single place.

In other words, it’s a way to collect large amounts of data from different sources. It also stores that data in one convenient place and then analyzes that data to learn valuable insights.

What Is Amazon Web Services (AWS) Redshift?

Amazon Web Services (AWS) Redshift allows you to start analyzing your data within minutes. 

This is done using your existing business intelligence tools. It offers parallel query execution and columnar storage on high-performance local disks. It also integrates with your current ETL and business intelligence tools.

Its architecture enables you to maintain high performance while scaling to accommodate hundreds of terabytes or petabytes of data. Data can be quickly loaded into Amazon Redshift from flat files stored in Amazon S3. It can then be automatically replicated across multiple nodes for high availability. You can also unload data from Amazon Redshift to S3 in either columnar or row formats for additional analytic uses.

Benefits of AWS Redshift

Amazon Redshift makes it easy to analyze all your data using your existing business intelligence tools. It uses data compression, columnar storage, and zone maps.

This helps reduce the amount of I/O needed to perform queries.

When Is AWS Redshift Used?

AWS Redshift is used when you’re looking for a data warehouse solution. It helps manage your data and take the load off your other systems by handling tasks like complex analysis and reporting. Redshift also helps you turn that data into actionable information.

This helps you make informed decisions about your operations.

If you think that AWS Redshift may be right for you, these are some common indicators:

• You have a lot of different types of data coming in from a variety of sources.
• Your current systems cannot handle the amount of data you have, or they aren’t doing it well enough.
• You want to be able to analyze your data in ways that go beyond basic queries and summary statistics. This can include things like machine learning and forecasting.
• You need to be able to take action based on the results of your analyses (e.g., change course to keep up with demand, cut costs by optimizing resources).

Differences Between AWS Redshift vs Snowflake

The main difference between Amazon Redshift vs Snowflake is how each platform handles scaling.

Redshift is a scaled-out MPP database built for fast SQL queries across multiple nodes. Snowflake is a single-tenant SaaS offering. It comes with auto-scaling storage and computes billed resources separately.

Similarities Between AWS vs Snowflake

When comparing AWS Snowflake vs Redshift, you’ll realize they are relational databases. 

They have a similar SQL-based schema structure, but they differ in how they implement it. For example, Amazon Redshift allows users to define standard SQL data types and user-defined types (UDTs). Snowflake does not offer this functionality.

Additionally, when working with Amazon Redshift, users will find that each column has a strict data type and cannot contain NULL values, whereas, in Snowflake, there is an “unknown” type that can be used instead of NULLs.

The Amazon Redshift query optimizer automatically determines which columns require sorting or hashing when creating tables; Snowflake requires users to manually specify these parameters during table creation time (although once specified, they do not need to be specified again).

Both services offer encryption at rest and in transit to keep your data secure and keep the performance scalable to handle your workloads, no matter how much they grow. You can also check the Snowflake vs Redshift pricing comparison to know the difference.

Choosing Between Snowflake vs AWS Redshift

Redshift is more scalable than Snowflake, at least when it comes to storage capacity. Redshift can store up to one petabyte of data in a single cluster. Redshift also lets you choose from a variety of different node types.

On the other hand, Snowflake has a broader range of application programming interfaces (APIs) than Redshift, which means that the platform can be used with more different types of software development kits (SDKs).

Snowflake also offers a broader range of out-of-the-box features than Redshift does – for instance, data sharing capabilities, and automatic caching.