Data warehouses are now critical to efficiently utilizing data to derive deep insights. The big question is, which data warehouse is best for my business? There are three data warehouse giants that you need to consider: Amazon Redshift, Google BigQuery, and Snowflake.

Here are some key differences between Redshift and Snowflake:

  • Pricing
    • Snowflake pricing reflects compute charges separately from storage and follows a pay-as-you-use model.
  • Support
    • Snowflake has better support for JSON-based functions and queries than Redshift.
  • Scaling
    • Snowflake offers instant scaling, whereas Redshift takes minutes to add more nodes.
  • Automation
    • Snowflake has more automated maintenance than Redshift.
  • Integrations
    • Redshift better integrates with Amazon's rich suite of cloud services and built-in security.
  • SQL
    • Snowflake's built-in SQL has an updated autocomplete feature.

We've already compared Amazon Redshift vs. Google BigQuery and Google BigQuery vs. Snowflake, but what about Amazon Redshift vs. Snowflake? supports all these data warehouses with a no-code data integration solution with blazing-fast ELT/CDC and reverse ETL capability so our clients can build powerful data integration pipelines into any cloud data warehouse of their choice. However, we want to ensure that clients comparing Redshift vs. Snowflake make the right choice.


Table of Contents


What Is Redshift?

Redshift is a fully managed, cloud-ready petabyte-scale data warehouse service that can seamlessly integrate with business intelligence (BI) tools. Amazon makes it quite easy for you to start out with a few hundred gigabytes of data and scale up or down seamlessly, based on immediate demands. This allows businesses to leverage their data to acquire valuable business insights about themselves or their customers.

To create your cloud data warehouse, you have to launch a set of nodes known as a Redshift cluster. Each node in the cluster is then partitioned into “slices.” Each slice is allocated a portion of the node’s memory and disk space. This helps balance the workload assigned to the node, which optimizes query performance. Once you have provisioned the cluster, you can upload data sets and run data analysis queries.

Regardless of your data set's size, you can take advantage of fast query performance by using the same SQL-based tools and BI applications.

Amazon Redshift exhibits superior performance by taking advantage of internal networking components. By utilizing high-bandwidth connections, proximity, and custom communication protocols, the system achieves high-speed communication between nodes.

When To Use Redshift

Redshift is ideal for any use cases where the data is so huge it's measured in petabytes. In fact, the larger the volume of data, the better value Redshift becomes as a database proposition.

 Redshift is also perfect for dealing with real-time analytics, even with data that's streaming in from multiple sources. This empowers businesses to make data-driven decisions quickly and better adapt to changes in the market.

Complex data sets such as behavioural analytics are also made simple by Redshift. An app developer needing information on how users interact with the app on different devices might use Redshift to log data of this type.  

Visit our Integrations page to learn more about the Redshift connector or schedule an intro call.


What Is Snowflake?

Like Redshift, Snowflake is a powerful relational database management system. It’s offered as an analytic data warehouse for both structured and semi-structured data that follows a Software-as-a-Service (SaaS) model.

This means it’s not built on top of an existing database or a big data software platform like Hadoop, for example. Instead, Snowflake uses an SQL database engine with architecture specifically designed for the cloud.

Snowflake’s architecture has the unique feature of being a hybrid of traditional shared-disk and shared-nothing models. With a shared disk model, the system uses a central data store to which each compute node has access. With shared-nothing, each node in the cluster stores a portion of the entire data set locally.

Snowflake is also a three-layer system consisting of:

  • Database Storage: Snowflake manages how information like file size, structure, and metadata is stored in the database.
  • Query Processing: Snowflake processes queries using what's known as “virtual warehouses.” Each warehouse represents a cluster node that's independent of other cluster nodes and doesn't share compute resources across virtual warehouses.
  • Cloud Services: This layer represents the services that tie together the different components of the system such as authentication, infrastructure management, query parsing, and access control.

This data and analytics solution is also fast, user-friendly, and offers more flexibility than other more traditional data warehouses because Snowflake separates compute and storage functions in its pricing. That means you only pay for the features you need when you need them. You're never charged for what you haven't used, maximizing flexibility and scalability.

Both data warehouses offer data extract, transform, and load (ETL) capabilities to link to business data from various sources. There are similarities between the two solutions, yet each platform offers unique capabilities and functionalities in relation to security and performance.

For instance, if you’re thinking about running your data analytics workload entirely on the cloud, the similarities between these two robust cloud data warehousing solutions are far greater than their differences.

When To Use Snowflake

Snowflake offers cloud-based data storage and analytics in the form of the Snowflake Elastic Data Warehouse. In this scenario, users can analyze and store data using cloud-based hardware and software.

If you use Snowflake ETL, once the data is stored in Amazon S3, for instance, you have the public cloud ecosystem at your disposal. You can leverage its power without using tech services like Hadoop.

Because Snowflake handles unstructured data, it's ideal for connecting to a data lake and sorting or reorganizing raw data held here.

DevOps teams who embrace Agile software development techniques may find Snowflake's cloud-native infrastructure ideal for logging dynamic usage trends or other rapidly shifting data sets.

Both cloud warehouse systems are powerful and offer some unique features for data management, but it's important to understand the key differences between Redshift vs. Snowflake. Choosing the right solution means comparing integrations, database features, maintenance requirements, security provisions, and, of course, cost.


Redshift vs. Snowflake: In-Depth Comparison

Comparing Redshift vs. Snowflake shows how these data warehousing solutions and their applications can work depending on your organization's specific requirements. For instance:

  • To bundle or not to bundle: Redshift bundles the compute and storage services, providing instant scalability to enterprise level if necessary. But Snowflake offers compute and storage as separate services, and both have tiered editions, which for some businesses might be the more flexible, common-sense solution—you still get the features you need but can scale at any time.
  • JSON: Deal? Or no deal? Snowflake offers more robust JSON storage than Redshift, meaning the functions for JSON storage and query are natively built into Snowflake. Redshift, on the other hand, splits JSON into strings upon load, making it much more difficult to query and use.
  • Security: All you can eat? Or one plate at a time? Redshift's abundant security options allow businesses to customize an encryption solution. Snowflake's security and compliance attributes are natively built into each tiered option, so security is a no-brainer: it's there from the moment of integration according to your business's data strategy.
  • Data tasks: Done for you? Or DIY? Amazon Redshift needs dedicated maintenance. There are several tasks that Redshift can't automate, like vacuuming data or data compression. That can require a lot of hands-on upkeep. Conversely, Snowflake automates many of these tasks, which can save you significant time if you ever have to diagnose or resolve an issue.

Depending on the level of optimization you need in a data warehouse, gauge the features above. When compared with your data strategy, these features are primary indications of whether the feature offered by Redshift or Snowflake is a benefit or disadvantage for your organization.

Redshift vs. Snowflake: Integration and Performance

If your company is already working with AWS, Redshift might seem like the natural choice. However, you can also find Snowflake in the AWS Marketplace with on-demand functions.

Redshift integrates seamlessly with AWS services like Athena, Database Migration Service (DMS), DynamoDB, CloudWatch, and Kinesis Data Firehose.

Also, if your data is in Amazon S3, DynamoDB, or Amazon EMR, Redshift can take advantage of Massively Parallel Processing to load your data quickly. 

Redshift can present performance challenges if the Sort and Distribution keys are not planned properly. These keys define how the system stores and accesses information. They can be complex to set up and maintain. For example, once you set a distribution key on a table, it cannot be changed later. Thus, you need to fully understand these concepts before implementing them. 

Also worth considering is that, if you use a live app database, Redshift isn't a suitable option. Although it's fast at running queries and analytics on large datasets, it doesn't offer the same performance on live apps. You’ll need to pull the necessary information into a caching layer and subsequently load that into Redshift if you need it for this use case.

If you’re thinking of using Snowflake, it’s important to note that it doesn’t have the same integrations as Redshift. This, in turn, makes it challenging to integrate the data warehouse with tools like Athena or Glue.

However, Snowflake makes up for this with a variety of other integration options, including Apache Spark, IBM Cognos, Qlik, and Tableau, to name a few. As a result, you can say that both solutions are about even in terms of integrations. In this respect, it's not really a case of Redshift vs. Snowflake, but understanding which integrations are important for you as an organization.

In the Redshift vs. Snowflake debate, Redshift is the more established solution, but Snowflake has made significant strides in the past few years.

Data optimization options like materialized views and dist keys mean dashboards for both data warehouses have the potential to run up to 150 faster than the source databases.

Snowflake vs. Redshift: Database Features

Snowflake makes it quite easy to share data between different accounts. If you want to share data with your customers, for instance, you can share it without ever having to copy any of the data.

This is a highly efficient approach to working with third-party data. Redshift offers similar features when used in conjunction with Amazon S3 or AWS Data Exchange services. But Redshift doesn’t support some semi-structured data types, like Array, Object, and Variant, without additional and often complex extensions. Snowflake does.

When it comes to strings, Redshift VARCHAR (varying character data) limits data types to 65535 characters. You also must choose the column length ahead.

In Snowflake, strings are limited to 16MB and the default value is the maximum string size, so there’s no performance overhead. As a result, you don’t have to know the string size value at the beginning of the exercise.

Redshift vs. Snowflake: Maintenance

With Amazon’s Redshift, users are forced to look at the same cluster and compete over available resources. In fact, you have to use WLM queues to manage it, and this can be quite challenging when you consider the complex set of rules that have to be understood and managed.

This problem doesn’t exist with Snowflake. You can seamlessly start different data warehouses of varying sizes to look at the same data without copying it. So, these can be allocated to different users and tasks quite easily.

In the Redshift vs. Snowflake debate, Snowflake wins when it comes to vacuuming and analyzing tables regularly. Snowflake provides a turnkey solution. With Redshift, this can become a problem as it can be challenging to scale up or down.

Redshift Resize operations can also quickly become extremely expensive and lead to hours of downtime.

Since compute and storage are separate in Snowflake, you don’t have to copy data to scale up or down. You can just switch data compute capacity at will, although adding and removing nodes must be done manually.

Redshift vs. Snowflake: Security

For any successful big data project, security will be at the heart of all activities. However, this can be difficult to maintain consistency because every new data source can potentially open new vulnerabilities. This can lead to a gap between the data that’s being generated and the data that’s being secured.

When it comes to security, it’s not a case of Snowflake vs. Redshift, as both products boast enhanced security. However, Redshift also provides features and tools to manage it, like Access management, Cluster encryption, Cluster security groups, Data in transit, Load data encryption, SSL connections, and Sign-in credentials. Access in Redshift can be granularized so you can grant users or groups access to only the specific data in tables they need for a given task.

Redshift clusters can be launched inside your infrastructure Virtual Private Cloud (VPC). This allows you to restrict inbound or outbound access to your clusters. 

Snowflake offers similar tools and features to ensure security and compliance with regulatory bodies. But the security features aren’t available across all versions, so you have to know which edition you’re working with. The base versions offer basic security options, while the options are more robust depending on the version you select. There is a benefit to this method. Using this approach, you can customize your system to purchase only the security features you need. 

Redshift vs. Snowflake: Costs

Both Snowflake ETL and Redshift ETL have different pricing models. If you take a closer look, you’ll find that Redshift is less expensive when it comes to on-demand pricing. Both solutions provide 30% to 70% discounts for companies that choose to prepay.

With one-year or three-year Reserved Instance (RI) pricing, you can access additional savings that you’ll miss out on with a standard on-demand pricing model. Redshift calculates costs based on a per-hour per-node basis.

So, you can calculate your monthly commitment as follows:

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

Snowflake’s charges heavily depend on your monthly usage pattern. This is because each bill is generated at hour granularity for each virtual data warehouse. Furthermore, data storage costs will also be separate from computational costs. For example, storage costs on Snowflake can start at an average compressed amount at a flat rate per terabyte. This will be accrued daily and billed each month.

However, this can quickly become confusing because Snowflake offers seven tiers of computational warehouses, with the smallest cluster costing one credit or a certain cost per hour. These costs double as you go up each level.

As a result, in the Redshift vs. Snowflake debate, it’s safe to conclude that Redshift is less expensive compared to Snowflake on-demand pricing. But to benefit from significant savings, you must sign up for the one- or three-year recurring contract.


Redshift vs. Snowflake: Pros & Cons

Amazon Redshift Pros

  • Amazon Redshift is highly user-friendly.
  • It demands very little administration. For example, all you have to do is create a cluster, select a type of instance, and then manage scaling.
  • It can be seamlessly integrated with a variety of AWS services (in the world’s largest cloud ecosystem of capabilities).
  • If your data is stored on Amazon S3, Spectrum can easily run complex queries. You just have to allow scaling of the compute and storage independently.
  • It’s highly suitable for aggregating/denormalizing data in a reporting environment.
  • Provides lightning-fast querying for analytics and allows for concurrent analysis.
  • Offers multiple data output formats, including JSON.
  • On-demand reserved instance pricing that covers both compute power and data storage, per hour and per node.
  • In addition to enhanced database security capabilities, Amazon also has an extensive integrated compliance program.
  • Provides safe, simple, and reliable backups.

Amazon Redshift Cons

  • Not appropriate for transactional systems.
  • Sometimes you have to roll back to an old version of Redshift while you wait for AWS to release a new patch.
  • Amazon Redshift Spectrum will charge extra, based on the bytes scanned.
  • Redshift lacks some modern features and data types, and the dialect is a lot like PostgreSQL 8.
  • There can be problems with hanging queries in external tables.
  • Data integrity verification can pose challenging
  • Primary key and foreign keys are informational only in Redshift. The system does not enforce uniqueness. As such, you’ll need to use some other process to deduplicate data

Snowflake Pros

  • Snowflake works great for enterprises that operate primarily on the cloud.
  • This data warehouse solution is extremely user-friendly and compatible with most other technologies.
  • The built-in SQL interface is also highly intuitive, and it has a useful autocomplete feature.
  • Integration with other cloud-based data sources is straightforward because Snowflake itself is a cloud-based data warehouse.
  • Easy to set up and get running.
  • Supports an extensive ecosystem of third-party partners and technologies.
  • Set and Forget Model: True SaaS can be integrated with cloud services, data storage, and query processing.
  • Data storage and compute charges will be based on different tiers and cloud providers and charged separately.
  • Allows secure views and secure user-defined functions.
  • Account-to-account data sharing can be enabled through database tables.
  • Integrates with Amazon AWS.

Snowflake Cons

  • If you’re running a business using on-premises technology that doesn’t easily integrate with cloud-based services, Snowflake might not be the right option.
  • You'll use a minute’s worth of Snowflake credits whenever you start a virtual warehouse and get be charged every second after that.

Redshift vs. Snowflake: Summarizing Key Differences and Managing Data with

A quick summary of the key differences when it comes to AWS Redshift vs. Snowflake:


AWS Redshift



Tightly coupled compute and storage            

Serverless, decoupled compute and storage


Adding and removing of nodes has to be done manually, concurrency scaling can be added at an extra cost

Auto-scaling is enabled but users cannot resize nodes without additional purchases of virtual warehouses



AWS Identity and Access Management (IAM)

Role-based access control (RBAC), multifactor authentication (MFA), SOC I and II compliant

Data types supported

Structured and some semi-structured data, can handle unstructured data when run in conjunction with Amazon S3   

All data types, including unstructured data since 2021

Maintenance/admin key points

Credential and permissions management must be done manually by users

Automated performance tuning

Data integrity protection

“Snapshots” back up clusters and can be taken at any time

“Time Travel” and “Fail Safe” features allow recovery of data in cases of extreme failure

Pricing benefits  

Significant savings when saving up for long-term contracts 

Five different pricing options from On-demand to Enterprise

The choice in the Redshift vs. Snowflake contest is relative to your resources and specific business demands. For example, if your organization is tasked with managing massive workloads that can range from millions to billions, then the clear winner here is Redshift.

While AWS’s offering is cost-effective, companies also have the option of reducing expenses by choosing query speeds at a lower price point for daily active clusters.

As Redshift is a popular Amazon product, there’s also detailed documentation and support that helps teams overcome any potential hurdles to adoption. However, at its most basic, your data warehouse decision has to be made based on your own unique daily usage patterns and the amount of data you work with.

Regardless of which cloud data warehouse you choose in the Redshift vs. Snowflake debate, can help you transfer data safely and securely with our ETL and ELT functionality and blazing-fast CDC platform. 

Try our platform for yourself and start your 14-day free trial today, or schedule an intro call with our experts to learn more about unifying all your business data, regardless of whether you ultimately choose Redshift or Snowflake.