Snowflake vs. Redshift: Key Differences, When to Use Each and Which One to Choose in 2024

Data warehousing is an important part of today’s business landscape. A data warehouse is a centralized, large repository that stores every kind of data your organization needs—whether structured, semi-structured, or unstructured. No matter the type of data, a data warehouse provides a consistent view of the data with quick access to historical data. That way, you can query all your data consistently to make informed business decisions.

Essential characteristics of a data warehouse include consistent schemas across different tables so that you can retrieve consistent query results, and support for the querying of multiple tables without the need for custom code. Two of the largest players in the data warehouse space are Snowflake and Amazon Redshift. Both provide the necessary data warehousing capabilities, but which is better for your unique needs?

This article will explain the key differences between Snowflake and Redshift, and which data warehouse can best suit your data needs.

Key differences between Snowflake and Redshift

Both Snowflake and Redshift are popular data warehousing options, but you should be aware of the key differences in order to make an informed purchasing decision.

• Architecture: One of the key differences between Snowflake and Redshift is the architecture. Snowflake is a SaaS-based platform that can run on any of the major cloud providers, such as AWS, Azure, or Google Cloud. Snowflake is built on a newer architecture that decouples the storage and compute components in its data warehouse. Data resides in a central repository while compute instances are sized, scaled, and managed independently. For example, you may need to scale up data processing, while maintaining the same storage capacity. Snowflake is also completely serverless, so there is no hardware to maintain.

  Amazon Redshift, on the other hand, is a PaaS (Platform as a Service) solution. As you might expect, the Redshift platform runs only on AWS. It uses an older architecture that stores data in single-unit compute/storage nodes. The nodes perform both data processing and storage. However, Redshift offers several node types, so you have some flexibility. The Redshift architecture allows for large scale data analysis and storage. Redshift runs on cloud servers, which requires some maintenance.

• Pricing: Snowflake employs a pay-per-use model, like many cloud services. With Snowflake, you are billed based on the amount of compute resources you use, as well as the amount of storage used. Since storage and compute resources are separate, you are billed separately for each, and you are billed only for the resources you use. Snowflake could be a better option if your storage/compute needs fluctuate.

  Redshift offers different nodes to accommodate your workloads: RA3 or DC2. Redshift’s prices depend on the node you use. RA3 nodes allow you to pay for compute and managed storage independently. You pay for the number of nodes you need for your performance requirements and for the managed storage you use, much like Snowflake. DC2 nodes enable compute-intensive data warehouses. Each node is a combination of data storage and compute usage. Its pricing depends on how many nodes you need. Redshift offers large discounts for signing a contract, so if you know your needs in advance, Redshift can be a more economical option.

• Data Support: Both Snowflake and Redshift can handle a wide range of data types and semi-structured data formats like JSON and Parquet. Snowflake used to have the advantage of unstructured data support, but Redshift improved its support of unstructured data.

• Scalability: Snowflake has more dynamic scaling capabilities, as it can make more resources available quickly to handle larger queries. Redshift, on the other hand, requires the addition or removal of individual nodes, which can be time-consuming.

• Security and Compliance: Snowflake uses RBAC (role-based access control) to manage users and privileges. It also uses MFA (multi-factor authentication) for account security. Redshift uses AWS IAM (Identity and Access Management) to restrict access to authorized users. Both Snowflake and Redshift offer robust data encryption. In addition, both providers offer SOC1 Type II, SOC 2 Type II, and HIPAA compliance.

• Maintenance and Administration: Snowflake requires almost no maintenance, as it scales and optimizes queries automatically. By contrast, Redshift requires manual maintenance and administration. However, if you already have AWS, Redshift integrates seamlessly with AWS.

When should you use Snowflake?

Snowflake comes out ahead of Redshift in certain scenarios.

• You require elastic scaling. Since Snowflake has a pay-as-you-go pricing model and can dynamically scale compute resources easily, Snowflake is a good option if your data demands are always changing. Your business may be growing rapidly, and you need to scale up quickly without manual intervention. Redshift requires some maintenance for scaling up or down.
• You need to integrate semi-structured and unstructured data. Snowflake does a better job integrating with data lakes such as Amazon S3 and Azure Data Lake Storage. These repositories allow you to store any kind of raw data.
• The query load is expected to be lighter. Snowflake is not as powerful as Redshift at performing heavy querying on very large data sets.
• You need automated management. You don’t need a full-time administrator for Snowflake. Snowflake is a complete SaaS (Software as a Service) solution that requires no maintenance.

When should you use Redshift?

The following are scenarios where Redshift is the better option.

• You already use the AWS ecosystem. If your organization has already made an investment in AWS, it makes sense to integrate AWS with Redshift. You can access and ingest your data across the AWS ecosystem, including data lakes and other data warehouses, without having to copy data or perform time-consuming ETL (Extract, Transform, Load) operations. You can also leverage AWS analytical tools.
• Your data workloads deal with structured data. If your data is mostly structured data, Redshift can perform complex queries on large datasets very quickly.
• You deal with high query loads. Redshift is very fast at performing queries because it allows clusters to work independently without affecting other clusters’ performance. It also leverages PostgreSQL and Massively Parallel Processing (MPP) to deliver quick query outputs.

Which data warehouse is better: Snowflake or Redshift?

Both Snowflake and Redshift offer data warehouses solutions that store vast amounts of data and perform fast data analysis. The data in each is accessible through SQL-based query engines. They both offer excellent security. The choice comes down to your business needs.

You can make a strong case for Redshift if you already use the AWS ecosystem, as it integrates natively with AWS. It is more economical to add Redshift to AWS than to add Snowflake to AWS.

You also need to consider the skill sets of the users of the system. Snowflake is very user-friendly and designed to work straight out of the box. Redshift is more powerful but requires infrastructure setup and hardware configuration.

Accelerate your cloud data strategy with CData Connect Cloud

Once you decide on a cloud data warehouse provider that fits the needs of your technical users, you need to consider your business teams. These users need easy access to the data with their favorite data analysis tools. CData Connect Cloud provides direct, live connectivity to data solutions like Snowflake without installing software, as well as to additional sources like Redshift through custom data connectors. Your analysts can then access and query live data directly from such tools as Looker, Power BI, and Tableau, among others.