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Amazon Redshift vs Snowflake: Everything You Need to Know

Data-driven intelligence powers successful modern businesses. Most enterprises use high-performing cloud data warehouses to store their operational data while enabling business intelligence activities and data analysis. 

Snowflake and Amazon Redshift are the best-in-class cloud-based data warehouse solutions. These user-friendly and cost-effective services have revolutionized the speed, volume, and quality of business analytics in modern data warehouses.

Although both solutions are ranked top in the market, choosing one over the other can be tricky. It isn’t a question of either solution’s superiority, but rather which one will suit your data strategy better.

Let’s explore Amazon Redshift and Snowflake, compare these solutions, and outline core considerations when selecting a modern data warehouse.

Snowflake and Redshift Fundamentals

Both Snowflake and Redshift are robust cloud-based data warehouses with exciting options for data management. 


Snowflake’s data warehouse offers analytical insights for both structured and nested data. This software-as-a-service (SaaS) enables you to build scalable modern data architecture with maximum flexibility and minimum downtime.

The data warehouse uses a SQL database engine, which makes it easier to understand and use. Snowflake separates compute from storage and enables you to integrate third-party services like Amazon Simple Storage Service (S3) or Elastic Compute Cloud (EC2) instances.

Snowflake’s easy-to-use, fast, and flexible architecture uses a concept called virtual warehouse. This virtual warehouse sits atop the database storage service and enables you to build multiple data warehouses over the same data.

A query service layer sits atop this virtual warehouse and manages the infrastructure, query optimization, and security. This architecture enables you to run different types of jobs at a faster pace without affecting each other.

Amazon Redshift

Amazon Redshift is also a fully functional data warehouse solution designed for businesses to store and analyze large volumes of data for real-time analytical insights. Besides that, Redshift ML allows users to integrate Machine Learning capacities within the Redshift cluster by providing a simple, secure, and optimized integration between Redshift and Amazon SageMaker. It has a columnar data structure, and its query layer offers PostgreSQL standard compatibility.

Amazon Redshift Spectrum, a feature of Amazon Redshift, enables faster and more comprehensive data analysis by allowing users to perform SQL queries directly on data stored in the Amazon S3 bucket and supports additional data types like JSON, Parquet, ORC, Avro and other file formats. Redshift Spectrum also extends Amazon Redshift’s data warehouse capabilities with faster data access and query optimization.

One prominent Amazon Redshift feature is that it can be integrated with the entire AWS big data ecosystem. It offers you a complete solution for building ETL pipelines to load and process data. Moreover, it enables streaming data ingestion and query optimization to provide you near real-time analytics. 

Amazon Redshift uses a shared-nothing architecture. In this architecture, every compute node has its own memory, disk space, and CPU. The service organizes these compute nodes into clusters. Each cluster has a leader node to handle all cluster-related tasks like communication, query execution, and even managing other nodes. This architecture enables users to build multiple databases on a single cluster and supports frequent inserts and updates. 

Redshift also offers data sharing capabilities with multiple clusters. It allows users to query the data across multiple clusters and databases — or even across multiple AWS accounts — without having to copy that data.

On that note, Amazon Redshift is more optimized for high-performance workloads than Snowflake is. And they also enable users to leverage other business intelligence tools. In addition, Amazon Redshift offers a cost-effective, scalable infrastructure to perform queries on large data volumes. Amazon Redshift RA3 nodes come with managed storage that enables you to optimize your data warehouse by scaling and paying for compute and managed storage independently. RA3 allows you to choose the number of nodes based on your performance requirements and charges you only for the managed storage that you use.

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Common Features

Some features Amazon Redshift and Snowflake share:

  • SQL querying can access both services, and both integrate with third-party ETL and business intelligence (BI) tools.
  • They both use massive parallel processing architecture and offer fast query execution.
  • They both offer flexible, scalable, and secure data storage.

Key Differences

Similarities aside, there are some key differences we need to cover. 


Snowflake and Amazon Redshift use different architecture and show differing behavior depending on the type of running job. So, making comparisons on performance can be a little tricky.

Both Snowflake and Amazon Redshift leverage columnar storage and massive parallel processing. This architecture enables advanced analytics and saves significant time on large queries by leveraging concurrent computation. While both solutions offer concurrency scaling, Amazon Redshift also provides machine learning capabilities. 

The two services also differ in unoptimized query run time. Namely, Snowflake performs unoptimized queries better.

Although Amazon Redshift’s initial query time may be a little longer, query compile cache helps optimize query times for repeat queries. Moreover, Amazon Redshift offers various ways to standardize queries and data structure. Users can leverage ATO (Automatic Table Optimizations) where Redshift automatically manages the SORTKEY and DISTKEY to optimize queries and provides a significant reduction in run time for queries involving JOIN and WHERE. In cases where customers want to set these values manually, Redshift offers that capability as well.


Snowflake previously had the advantage of automated maintenance. In comparison, Amazon Redshift required some manual housekeeping. 

However, Amazon Redshift later announced its auto vacuuming, auto workload management (WLM) queue, improved queues leveraging machine learning (ML), and more. This automation bridges the gap between the two data warehouses while dramatically reducing Amazon Redshift maintenance.

Ecosystem and Integration

To make the most out of collected data, businesses must first understand them. That’s why third-party analytic tools are needed to provide specific insights. 

Both Snowflake and Amazon Redshift provide support for third-party integrations. However, Amazon Redshift offers the most extensive ecosystem and third-party integrations, including ETL and business intelligence tools, giving it a definite edge.


When it comes to pricing, Snowflake and Amazon Redshift offer different structures. 

Snowflake is pay-as-you-use. This may provide better value for minimal query use scattered across longer time windows. The cluster automatically shuts down when idle, and the service doesn’t charge the user when there’s no query load.

However, it’s tricky to predict Snowflake’s actual cost because of its complicated tiering computational structure. Snowflake offers seven tiers of computational warehouses and computes computing cost separate from storage which adds to the confusion when calculating. As a result, Snowflake might prove to be more expensive in most use cases.

On the other hand, Amazon Redshift offers simple and transparent pricing. For example, users may save up to 75 percent when they commit to a particular use level.

Users can also easily determine the price using the following formula:

Amazon Redshift Monthly Cost = [Price Per Hour] x [Cluster Size] x [Hours per Month] 

Moreover, Amazon Redshift offers both on-demand pricing and a Reserved Instance (RI) pricing model. Amazon Redshift is reportedly 1.3 times less expensive than Snowflake for on-demand pricing and 1.9 to 3.7 times less expensive than Snowflake when reserving instances for one or three years.


AWS has always been committed to maximizing security for its users, including in its data warehouse solutions. Amazon Redshift addresses security more comprehensively, whereas Snowflake takes a more jagged approach.

Snowflake offers encryption along with VPC/VPN network isolation. However, its security scope depends on which product edition you select and has cost implications.

On the other hand, Amazon Redshift offers end-to-end encryption that you can tailor to fit your security requirements. Moreover, it provides additional security features and tools like access management, cluster encryption, security groups, sign-in credentials, SSL connections, and VPC/VPN to manage your security. Also, Redshift users do not pay extra (i.e. licensing cost or different tier pricing) for enabling security features. 

Storage and Compute Separation

Snowflake separates the storage from compute, so users can independently scale these services up or down. 

Previously, Amazon Redshift didn’t offer any physical separation between compute and storage. This lack of separation means you must add more clusters for more storage space or computing power. With the introduction of R3 nodes this allows the user to scale compute independently of storage creating a similar scaling environment as Snowflake.

Redshift Spectrum, a feature of Redshift, enables you to fire SQL queries directly on data stored in the S3 bucket, eliminating data movement. With RA3 nodes, Amazon Redshift Managed Storage includes AQUA (Advanced Query Accelerator) capability at no additional cost. AQUA is a distributed and hardware-accelerated cache that enables Amazon Redshift to run up to 10x faster than other enterprise cloud data warehouses by automatically boosting certain types of queries.

Pros and Cons 

Now that we’ve compared their features, let’s quickly summarize each data warehouse’s pros and cons.

Snowflake Pros 

  • It’s a SaaS solution with an easy-to-use web-based UI.
  • It separates the compute from storage so users can scale up and down as required and charges based on tier and cloud provider.
  • It’s multi-cloud with access across cloud providers like Azure, Google Cloud Platform (GCP), and others.
  • It offers automated maintenance.
  • It supports JSON and other semi-structured data types.

Snowflake Cons 

  • It primarily operates on the cloud and doesn’t offer any support for on-premises infrastructure.
  • It can be more expensive than Amazon Redshift in most use cases.
  • Its security compliance highly depends on the product version you’re using and can incur additional costs.
  • Snowflake tends to lock users into a technology solution since users must learn particular tools like Snowpipe, SnowSQL, Snowpark, and others to work with it.

Amazon Redshift Pros 

  • It can co-exist with on-premise infrastructure and also offers close and seamless integration with other AWS services.
  • It provides a transparent and straightforward pricing model whether you follow the on-demand or RI pricing model, and RI also offers significant cost savings.
  • It offers enhanced data security as well as safe and reliable backup options.
  • It provides faster query execution for near-time and concurrent analysis.
  • It provides multiple data output formats.
  • AWS is constantly adding new features like ML integration, separate storage and compute with RA3 nodes, AQUA, concurrency scaling free for one hour/day of usage, variable workloads with predictable costs, and more to make it a top-notch, cost-controlled data warehouse solution.

Amazon Redshift Cons 

  • Amazon Redshift Spectrum offers flexibility but costs extra.
  • Amazon Redshift is now available on two different release cycles: Current Maintenance Track and Trailing Maintenance Track. The user can choose which track they are following but defaults to Current Maintenance Track.


Snowflake and Amazon Redshift both offer best-in-market data warehouse solutions. The choice between the two is relative to your business requirements and resources.

For example, if your organization has a low-query workload and wants an automated, scalable, multi-cloud platform, then Snowflake might be a better option. 

On the other hand, if your business manages massive workloads on structured data and semi-structured data, and has a high-query workload using other AWS services, then Amazon Redshift is a clear winner.

When it comes down to deciding between Snowflake and Amazon Redshift, you must consider your needs and resources. With the right tool, you can start making the most out of your data. Mission is able to help build your data infrastructure utilizing either of these popular data warehouse solutions. 



  1. How do Snowflake and Amazon Redshift support data migration from existing databases or data warehouses? Are there specific tools or services that facilitate the migration process for each platform?

Regarding the migration support and process from existing databases to Amazon Redshift and Snowflake, it's key to highlight that both platforms are equipped with robust mechanisms to ease this transition. Amazon Redshift leverages the AWS Database Migration Service to facilitate a smooth and secure transfer of databases to the cloud, minimizing downtime. Conversely, Snowflake provides an array of connectors and integration options that enable efficient data ingestion from various sources, including on-premises databases. This ensures businesses can migrate their data while maintaining operational continuity and safeguarding data integrity.

  1. How do Amazon Redshift and Snowflake handle data governance and compliance, especially with regulations like GDPR and CCPA?
  2. Amazon Redshift and Snowflake both offer robust data governance and compliance features to ensure data is managed securely and in accordance with regulations like GDPR and CCPA. These include data encryption, audit logging, and access controls. Implementation specifics can vary, with both platforms continuously evolving to offer better compliance tools and certifications to meet global data protection standards.
  4. What specific machine learning capabilities are integrated within Amazon Redshift and Snowflake, and how do they support advanced analytics directly within the data warehouse?
  5. Both Amazon Redshift and Snowflake support advanced analytics and machine learning directly within the data warehouse. Redshift integrates with Amazon SageMaker to facilitate ML model development and deployment. Snowflake offers Snowpark, enabling developers and data scientists to execute ML workflows directly on Snowflake using familiar languages like SQL, Python, and Scala.



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