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Snowflake vs Redshift: The Difference

Updated: Jan 16

With the growing demand for cloud-based data warehousing solutions, Snowflake and Redshift have emerged as two of the most popular platforms in the market. Both offer powerful features and strong performance, making it difficult for organizations to choose between them.

In this article, we will explore the differences between Snowflake and Redshift and discuss their respective strengths and weaknesses.

What is Snowflake?

Snowflake is a cloud-based data warehousing platform that provides on-demand access to computing resources, storage, and software tools for managing and analyzing large amounts of data.

Instead of installing and maintaining software on their own servers, Snowflake users access the platform via the Internet, paying only for the resources and features they need on a subscription basis.

By using the cloud infrastructure of AWS or MS Azure, Snowflake can offer a highly scalable and cost-effective solution for processing big data without requiring its users to invest in expensive hardware or IT infrastructure. This makes Snowflake a popular choice for businesses of all sizes looking to store, manage, and analyze large amounts of data in a more efficient and flexible way.

Snowflake Pros:

  • It is a fully managed SaaS platform that requires no maintenance or hardware

  • It separates computing from storage, allowing for flexible pricing and configuration

  • It supports multi-cloud deployment and instant scaling

  • It has better support for JSON-based functions and queries than Redshift

  • It has a built-in SQL with an updated autocomplete feature

Snowflake Cons:

  • It only supports bulk data loading during data migration.

  • It has fewer data customization choices than Redshift.

  • It may not be compatible with some AWS services or security features

What is Amazon Redshift?

Amazon Redshift is a service provided by Amazon Web Services (AWS). It is a fully managed, petabyte-scale data warehouse that enables businesses to store and analyze large amounts of structured and semi-structured data using a massively parallel processing (MPP) architecture.

Redshift is designed to handle complex queries over large data sets quickly and efficiently, leveraging columnar storage, data compression, and zone maps to optimize query performance. It also integrates with other AWS services, such as S3 for data storage and management, and can be easily scaled up or down to meet changing business needs.

With Redshift, users can run sophisticated analytics, generate reports and business intelligence, and gain insights into their data in real-time. It is widely used by businesses of all sizes, from startups to enterprises, in industries such as finance, healthcare, e-commerce, and more.

Redshift Pros:

  • It is a fully managed, petabyte-scale data warehouse that can integrate with BI tools

  • It allows for cost optimization through Reserved/Spot instance pricing

  • It supports data flexibility through features like partitioning and distribution

  • It better integrates with Amazon’s rich suite of cloud services and built-in security

Redshift Cons:

  • It requires some manual maintenance and hardware

  • It takes minutes to hours to add or remove nodes for scaling

  • It has less support for JSON-based functions and queries than Snowflake

  • It may vary in performance depending on the cluster size, data distribution, and query optimization

The Difference: Snowflake vs Redshift





Snowflake uses a unique architecture that separates compute and storage, allowing users to scale these resources independently. This makes it highly scalable and flexible and can offer better performance for complex queries.

Redshift uses a shared-nothing architecture, where each node contains both compute and storage. This can limit scalability and may lead to performance bottlenecks for complex queries.

SQL Compatibility

Snowflake supports ANSI SQL and has a SQL engine that is designed to run exclusively in the cloud.

Redshift also supports ANSI SQL but has a more traditional SQL engine that may require some modifications to run in a cloud environment.


Snowflake is a fully managed service, which means that AWS takes care of all maintenance and upgrades.

Redshift requires more manual maintenance, such as patching and upgrading the software.


Snowflake's pricing is more granular and offers more fine-grained control over resources, which can help businesses optimize their costs based on actual usage.

Redshift may be more cost-effective for smaller workloads but can become more expensive as data volumes and query complexity increase.

Scaling performance

It implements auto-scaling. It also supports multi-cloud deployment

It requires the addition/removal of nodes for scaling. It is limited to AWS

Data Flexibility

It supports few data customization choices

It supports data flexibility through features like partitioning and distribution

JSON support

It has better support for JSON-based functions and queries than RedShift

It better integrates with Amazon's rich site of cloud services and built-in security


It offers consistent performance across different workloads and query types

It may vary depending on the cluster size, data distribution, and query optimization

Ease of Management

It has more automated maintenance than RedShift such as backups, replication, and recovery. It also has a better admin console and documentation than Redshift

It is difficult to maintain compared to Snowflake

Query Optimization

Snowflake's SQL engine includes automatic query optimization features, which can improve query performance by optimizing query plans based on the underlying data distribution and statistics.

Redshift also offers query optimization features but may require more manual tuning and optimization to achieve optimal query performance.

Data Loading

Snowflake supports a wider range of data loading options, including bulk loading, streaming, and external tables. It also has built-in support for data transformation and processing through its Snowflake Data Pipeline feature.

Redshift also supports bulk loading and streaming but may require more manual setup for data processing and transformation.

Which one to choose?

When it comes to choosing a cloud-based data warehousing solution, Snowflake and Redshift are two of the most popular options on the market. Both platforms offer powerful features and strong performance, but choosing between them can be challenging.

Below are some of the factors you can consider for an informed decision that meets the specific needs of your organization.

  1. Data size and complexity: If you're working with large volumes of data or have complex data models, Snowflake may be a better choice due to its ability to handle such workloads. Snowflake's architecture is designed to handle massive amounts of data and offers strong support for semi-structured data types such as JSON, Avro, and Parquet.

  2. Workload type: If your primary use case involves analytics and querying large datasets, Redshift may be the better choice due to its columnar storage, automatic compression, and parallel processing capabilities. Redshift is optimized for these types of workloads and is often used for business intelligence and reporting.

  3. Budget: Snowflake and Redshift have different pricing models, so it's important to consider your budget when making a decision. Snowflake charges based on the amount of data stored and the amount of processing power used, while Redshift charges based on the size of the cluster and the amount of time it is used. Depending on your workload and usage patterns, one platform may be more cost-effective than the other.

  4. Security: Both Snowflake and Redshift offer strong security features, including data encryption, network isolation, and access controls. However, Snowflake is known for its advanced security features, such as its secure data sharing and multi-factor authentication capabilities.

  5. Integration with other tools: Depending on your tech stack, you may find that one platform integrates better with your existing tools and workflows. For example, Snowflake has native integrations with tools such as Tableau, Looker, and Matillion, while Redshift integrates well with AWS services such as S3 and Lambda.

It's also worth noting that both Snowflake and Redshift offer free trials, so you can try them out and see which one works best for your specific use case.


Both platforms offer strong security features, but their pricing models and integrations with other tools may differ. When choosing between the two, consider your specific needs and priorities, such as the type of workload you'll be running and your budget, to determine which platform is the better fit for your organization.


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