AWS Redshift vs Snowflake: Your Choice Depends on Your Use Case

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AWS Redshift and Snowflake are two popular platforms in the cloud-based data platform space. AWS Redshift started out as a cloud-based data warehouse service and has since been moving towards becoming a data platform by adding more features, such as data lake implementation support and an independent query engine. Snowflake is a cloud-agnostic, serverless data platform that supports many workloads, including data lakes, data warehouses, and machine learning.  Being cloud-agnostic, you can run Snowflake on any cloud – Amazon Web Services (AWS) Google Cloud Platform (GCP), and Microsoft Azure (Azure). This article will discuss the key differences and similarities between Snowflake and Redshift.

Quick Comparison

Redshift and Snowflake are cloud-based data platforms that employ massive parallel processing to help ingest and process huge volumes of data. The two platforms accomplish this objective differently because of the approach in their underlying design and marketing visions. 

Feature Redshift Snowflake
Multi-cloud Support Redshift is an offering from AWS that tightly integrates with AWS. It does not have multi-cloud support.  Snowflake is a cloud-agnostic offering that can work with all three major cloud service providers: Google Cloud, Microsoft Azure, and Amazon. 
Serverless Nature Redshift requires some configuration and setup nodes and instance types. More recently Redshift has also launched a serverless version. Snowflake is completely serverless and does not require any node configuration. 
User-Defined Function (UDF) Support Redshift supports Lambda functions for implementing its UDFs, which helps it support UDFs written in any language or runtime supported by Lambda.  Snowflake supports Java, Python, Javascript, and SQL for UDFs.
Pricing Structure Redshift offers two kinds of cost structures. For Dense Compute instances, the costs of storage and processing power are tied together. For RA3 type of instances, the costs of storage and processing power are separate. Snowflake uses a tiered storage and compute structure with additional features available in higher tiers. The pricing structure also varies according to the selected cloud provider.
Scaling Redshift scaling is done by adding more nodes to the existing cluster, which takes minutes to complete.  Snowflake scaling is done by increasing the virtual warehouse size or adding more clusters to the virtual warehouse, which takes tens of seconds to complete. 
Maintenance Operations Redshift requires manual vacuum operation to maintain query performance. Snowflake is maintenance-free, not requiring manual intervention to maintain query performance.

Understanding Redshift

AWS Redshift is a data warehouse that supports an SQL standard compatible with PostgreSQL. It can handle petabytes of data and still deliver fast query performance by using columnar data storage.

Under the hood, Redshift is a cluster comprising compute nodes and leader nodes. Compute nodes are worker nodes that come with their own processing power and memory, while leader nodes are responsible for communicating with external clients, developing execution plans, and communicating with the compute nodes. Redshift provides two options for compute nodes: Dense Compute and RA3 instances. Customers should use RA3 for storage-intensive workloads and Dense Compute instances for performance-intensive workloads below 1 TB in size. RA3 instances permit the scaling of storage and performance independently. 

Redshift Architecture

Redshift Architecture (Source)

Beyond traditional warehouse features, Redshift offers many nice-to-have features. Its federated query feature allows one to query external operational databases and join their data with Redshift to derive insights. Redshift Spectrum enables querying data located in S3 or other data lakes using the processing power of your Redshift cluster. Redshift ML helps build machine learning models through SQL and deploy them using AWS Sagemaker.

Understanding Snowflake

Snowflake is a cloud-based data platform that can be employed for various use cases, such as data warehouses, data lakes, or even unified data stores. Snowflake comes with robust SQL support and boasts high availability and reliability. Snowflake’s query engine can power complex data pipelines and large-scale analytics through the use of compressed columnar data storage. 

Snowflake architecture separates storage and computing power via a hybrid of shared disk and shared-nothing architectures. This unique architecture helps keep data in a central repository and simultaneously process or query data locally in compute nodes – enabling Snowflake to scale compute and storage independently. This is similar to other massively parallel processing architectures, such as BigQuery. Snowflake employs the concept of a virtual warehouse to organize workloads that are unaffected by other workloads. Due to legacy nomenclature, it is often referred to simply as a “warehouse”.

Snowflake Architecture

Snowflake Architecture (Source)

Snowflake operates in a serverless manner, with the user having no control over hardware specifications. It allows users to select the cloud provider and parameters. They can choose the size of the warehouse and the number of clusters in each warehouse to define the scaling strategy, which makes the initial learning curve minimal. Furthermore, Snowflake can process data stored in external locations like AWS S3 and Azure Blob Storage and can handle unstructured data in folders through the concept of directory tables. 

Data Warehouse Selection Criteria

Organizations must consider several factors while choosing a cloud-based data warehouse. Here are the most important ones.

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Integrations With Existing Platforms

Data warehouses ingest data from various sources, including external third-party ones. Integration with your cloud provider of choice and the data sources is one of the most important factors to consider while choosing one. 

Serverless vs. Server-Based Operation

Cloud data warehouses offer an advantage in maintaining infrastructure. But not all of them are free from servers. Some still require the organization to choose from various server configurations and optimize them.

SQL Layer And Analytical Functions

No two organizations have the same workload patterns. Some of the workloads require complex transformations within the data warehouse. Others require pipelines that consist of a series of transformations. Some organizations may benefit from a rich set of UDFs, while others only need a simple SQL layer. Whether the organization follows ETL or ELT methodology is also an important factor to consider as it places the computation cost either inside the data warehouse (ELT) or outside of it (ETL). 

Costs for Specific Work Load Patterns

Just the ability to handle the organization’s workload pattern is not enough to tilt the balance in favor of a data warehouse. The workload patterns influence the cost structure. Some workload processing choices may be tuned towards high storage requirements, while others may provide favorable cost structures in low storage and high processing power requirements. 

Scaling Flexibility

Cloud data warehouses offer various scaling configurations. While most offer the ability to auto-scale, factors such as response times, bounds of scaling, and ad-hoc scaling flexibility differ across providers.

Manual Maintenance Requirements

Not all cloud warehouses are zero maintenance warehouses. Some of them require manual operations at periodic intervals to maintain optimum performance. 

Redshift vs. Snowflake

Amazon-Specific vs. Cloud-Agnostic

Redshift is a proprietary product from Amazon that integrates well with other Amazon Web Services offerings. While it is possible to load data to Redshift from any source. (NOTE: Amazon service limits most features to other Amazon services). For example, when it comes to external tables, Redshift supports only S3 as the data source. 

Snowflake is a cloud-agnostic platform that can work with AWS, Google Cloud, or Azure. Snowflake external tables support many data sources, like Azure Cloud storage, AWS S3, and Google Cloud storage

Serverless Operation

Redshift operation is not completely serverless: Users must be involved in setting up the cluster and deciding on node types.

Snowflake is completely serverless, so it is easier for teams with less engineering bandwidth to set up. Users do not require any knowledge of the underlying infrastructure to get started with Snowflake. 

SQL Layer And User-Defined Functions

Redshift and Snowflake exhibit slight differences in how user-defined functions are implemented. 

Redshift supports SQL and Python to implement user-defined functions and also allows the use of a Lambda function to create custom functions. Support for Lambda functions helps developers implement their logic in any programming language supported by Lambda. The choices here include Java, Node.js, Go, or even a custom runtime. 

Snowflake supports Java, Javascript, SQL, and Python for implementing user-defined functions. 

To summarize, Redshift offers more flexibility in the use of user-defined functions. 

Pricing Structure

Redshift pricing is defined in terms of the nodes used. Storage and processing power are priced together when Dense Compute instances are used. Whereas storage and processing are priced separately in the case of RA3 instances, allowing payment for the storage used independently of the number of compute nodes. The cost for Redshift Spectrum depends on the amount of scanned data. 

Snowflake pricing is in terms of credits used based on storage and processing requirements. The amount of credits used varies depending on the size of the warehouse and the total time the warehouse is up and running. The first minute is charged in full; after that, the charge is per second. The dollar cost per credit depends on the cloud provider, region, and Snowflake edition. Snowflake offers ten “T-shirt-sized” warehouses, ranging from extra small (XS) to 6-Extra-Large (6XL). Snowflake is available in four editions: Standard, Enterprise, Business Critical, and Virtual Private Snowflake.

To summarize, Snowflake’s pricing structure differs from Redshift, and the type of workload is a big factor in the ultimate cost. Because of multi-cloud support, the Snowflake pricing structure may feel complicated, but in fact, it is the simpler of the two because Snowflake pricing options are based on the fundamental idea of keeping storage and processing power separate. Redshift pricing structure allows storage and processing-power pricing separately or bundled, depending on the type of nodes used. 

Redshift is usually cheaper in the case of on-demand mode and in cases where data storage requirements are very high. 

Scaling

Redshift offers an elastic scaling option that can increase the number of nodes in an existing cluster in a matter of minutes. It also has a classic resize option to create a new cluster with upgraded node types, but this is time-consuming.

Compared to Redshift, Snowflake offers faster scaling. Snowflake can be scaled by resizing the warehouses or by adding more clusters. The resizing of the cluster can be done on the fly while queries are running. Snowflake auto-scaling works by adding multiple clusters to the same warehouse. One can set the minimum, and the maximum number of clusters for the warehouse, and Snowflake will then automatically scale within those specifications. Snowflake scaling happens in tens of seconds as opposed to minutes in Redshift.

Manual Maintenance Requirements

Redshift requires a special process, vacuuming, at regular intervals to maintain query performance. Vacuuming reorganizes the data sort order and prepares it for quick retrieval and processing. Even though Redshift automatically executes vacuum operations, Amazon also recommends manual vacuum execution at regular intervals.

Snowflake does not require any manual intervention or maintenance by developers.

Redshift vs. Snowflake — When to Use Which?

 

Redshift and Snowflake solve the fundamental problem of storing and processing data at scale, but they take different approaches to solve the problem, so each has its strengths and weaknesses. 

When to Use Redshift

  • Redshift makes sense if your application environment is already hosted in the AWS ecosystem and if you don’t mind staying with AWS for the long term.
  • Redshift pricing is cheaper than Snowflake when used in on-demand mode, especially when the workload is both storage- and compute-intensive. 
  • Redshift makes sense when your workload requirements are stable and you do not require rapid scaling. Redshift is not very fast at scaling, often taking minutes to complete.
  • You may prefer Redshift if you have a DevOps team well-versed in configuring clusters and performing manual maintenance activities. 
  • Redshift is well suited to use cases that do not involve processing data from external sources. Redshift can process data in S3, but it cannot yet process data in Azure blob storage or Google cloud storage. Deeming its data governance less flexible. 

When to Use Snowflake

  • You may want to use Snowflake if your organization is leaning toward a multi-cloud deployment and does not want to get locked into a single cloud provider.
  • Storage is priced separately and becomes expensive for very large datasets: As data accumulates, Snowflake becomes gradually more expensive than its competitors. Accordingly, Snowflake provides a cost-effective alternative for doing fast, complex operations on smaller amounts of data.
  • Snowflake works well when your workload requirements can make use of Snowflake’s fast scaling and concurrency features. It does not make sense to pay more for Snowflake if you cannot take advantage of it.
  • Snowflake is preferred if your organization does not want to spend time maintaining a cluster and executing manual housekeeping activities.
  • Snowflake offers flexibility for use cases involving processing data from many external sources in multiple cloud environments. Snowflake can process data in S3, Azure Storage, and Google Cloud Storage. 
  • Snowflake provides a lot more data governance options compared to Redshift such as tag-based masking policies and sensitive data classification functions. So if your organization is particular about data governance features, Snowflake may be a better choice.
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Conclusion

Redshift and Snowflake are two very popular products in the data warehousing space. Both provide query engines that can be used on data that resides outside the tables, so they can also be used in executing data lake workloads. Redshift integrates well with Amazon services, while Snowflake does better when multi-cloud support is needed; it enables integration with data sources from other cloud providers. Snowflake is cloud-agnostic, but it is also considerably more expensive. Considering all these factors, there is no right or wrong product here: The best choice depends on your use case.