snowflake vs redshift

 Snowflakes and Amazon Redshifts are two examples of giant household data warehouse platforms that offer outstanding performance, scale, and business intelligence capabilities. It may seem challenging to individuals and businesses to select software from the several data warehouses that will satisfy their needs. However, most companies will analyze and compare the features and pricing of two or more data warehouse software before deciding what platforms to use; for example, the snowflake vs. redshift comparison.

Snowflake and Amazon Redshift are top performers for cloud solutions that run on the AWS platform. This software has also revolutionized the volume, speed, and quality of business intelligence insights. If you need to choose between the two, this article will guide you toward making the right choice.

Data Warehouse is a respiratory system for storing data collected from different sources. Data warehouses are necessary for utilizing data to provide meaningful business insights and analysis. 

What is Snowflake?

Snowflake is a SaaS-based data platform compatible with all cloud service providers, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Its data warehouse helps you create scalable modern data architecture with high mobility and the lowest downtime while giving analytical insights for both structured and nested data.

What is Redshift?

Founded by Amazon, the platform was strategically named Redshift as a deliberate jab at Oracle, given that Oracle’s whole branding is red. Redshift is a traditional data warehouse and a PaaS-based(Platform-as-a-Service) solution for business intelligence. It is also a fully functional data warehouse solution that enables businesses to store and process large amounts of data for real-time analytical insights.

Snowflake vs Redshift: Data Warehouse Comparison

Although both pieces of software share certain essential fundamentals, they also may have different functions. This section will compare the two platforms and see how well they can fit your company’s demands.

For example, Snowflake is a SaaS based data platform, while Redshift is PaaS based data. Snowflake, the older of the two platforms, was founded in 2012 while Redshift launched in 2013. However, both platforms are one of the oldest data warehouses in existence.

Below are some of the technical differences between the two softwares:

Snowflake Vs. Redshift: Performance

Amazon Redshift and Snowflake make use of columnar storage and intense parallel processing. This architecture uses concurrent processing to speed up huge queries and allow sophisticated analytics. Concurrency scalability is a feature of both platforms, but while Amazon Redshift has machine-learning capabilities, Snowflake does not.

When it comes to unoptimized query run time, Snowflake performs unoptimized queries better. For optimized queries, Redshift often takes longer for query optimization, but if these queries are run frequently, they tend to be quicker. However, this isn’t the case with Snowflake, which provides far higher performance with raw queries.

Snowflakes wins this Category.

Snowflake Vs. Redshift: Architecture

Snowflake is entirely serverless, so you never have to manage or maintain any hardware, as the storage and computing are detached to optimize performance and query concurrency. The standard version of AWS Redshift is not serverless, and storage and computing are tightly conjoined. However, Redshift offers several node types, giving you more power and flexibility when configuring clusters.

Snowflakes wins this category.

Snowflake Vs. Redshift: Integration

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

If you are committed to the AWS platforms will find data integration on Redshift more seamless than on Snowflake, this is because Snowflake is not so embedded with the AWS ecosystem. Snowflake also does not have the same range and depth of vendor partnerships as Redshift. However, an analysis will be simple to complete if your focus on integration is with Tableau, Apache Spark, IBM Cognos, and Qlik.

Additionally, if your businesses heavily rely on JSON storage, Snowflake will unquestionably outperform Redshift. Users may easily query and store data using the built-in architecture and Snowflake schemas, but Redshift has strained operations due to the proliferation of queries.

Redshift wins this category.

Snowflake Vs. Redshift: Scalability

With Snowflake auto-scaling features, you can scale up more computing resources needed to deal with any query load. In Amazon Redshift, autoscaling isn’t very flexible, as it can take several minutes to hours to manually add or remove individual nodes.

Snowflake wins this category.

Snowflake Vs. Redshift: Security

Redshift scores some key points on security and compliance as it takes a complete approach to security and compliance, whereas Snowflake employs a more subtle strategy. Additionally, Redshift makes it easier to restrict inbound or outbound access to clusters than Snowflakes.

Snowflake offers encryption along with VPC/VPN network isolation. However, the security scope of the product version you choose comes with a cost implications.

Redshift, on the other hand, provides end-to-end encryption that you can customize to meet your security needs. Additionally, it gives you access control, cluster encryption, security groups, sign-in credentials, SSL connections, and VPC/VPN capabilities to manage additional security features and tools. More importantly, there are no additional fees (such as license fees or other pricing tiers) for Redshift users to enable security features.

Redshift wins this category.

Snowflake Vs. Redshift: Data Support

Snowflake supports semi-structured (JSON, Avro, Orc, CSV, JSON, Parquet), unstructured and structured data. Amazon Redshift only supports structured and semi-structured data. However, the platform does not support semi-structured data types like objects, arrays, or variants. To query unstructured data, you will need to store it within an S3 data lake and query using Amazon Spectrum.

Snowflake wins this category.

Snowflake Vs. Redshift: Pricing

In its pricing scheme, Snowflake keeps compute and storage separate in its pricing structure. Redshift combines them. Each hour of Redshift costs roughly 25 cents, while Snowflake costs about $40 a month. However, the rate of usage will vary tremendously depending on the workload.

Concurrency scaling is a feature that Snowflake automatically includes in all editions at no extra fees. Redshift has a cap on the number of concurrent users it will allow each day, but you will be charged per second if the cap is exceeded. 

Redshift charges an hourly rate that is uniform and well-known to all users. Long-term contracts users get significant discounts. In Snowflake the computing process is isolated from the warehousing process, which results in discrete pricing. Additionally, there are five versions available, ranging in price from the most basic to the most expensive as you move up the tiers. 

In general terms, the Snowflake is more expensive than Redshifts. Therefore, for this category, Redshifts wins it.

The Cons of Snowflake and Redshift


  • Snowflake may not be the right fit, if you run an on-premises business that doesn’t easily integrate with cloud-based services.
  • As earlier stated, Snowflake is more expensive than Amazon Redshift in most use cases.
  • Snowflake tends to lock users into a technology solution because it requires users to learn tools like Snowpipe, SnowSQL, Snowpark, and others to work with it.
  • You’ll use a minute’s worth of Snowflake credits whenever you start a virtual warehouse, as it charges every second.
  • It may cost more to comply with security regulations, depending on the product version you’re using,


  • Redshift does not support many common PostgreSQL data types.
  • It’s unsuitable for transactional systems due to the need to use two different database services (e.g., RDS/Aurora + Redshift).
  • Problems with hanging queries in external tables may occur.
  • The Amazon Redshift Spectrum service charges extra based on the number of scanned bytes.
  • You will also need to use other methods to guarantee the security of your data.

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