PortablePortable Logo

Snowflake vs. Redshift Comparison: 2024 Deep-Dive

Data-driven companies across the globe are upgrading from legacy data management architectures to cloud-based data warehouses and data lakes for analytics. Now is your opportunity to do the same.

Two of the best data warehousing solutions to evaluate are Snowflake and Amazon Redshift.

What Are the Business Use Cases for a Data Warehouse?

There are three ways companies can create value from data:

  1. Analytics –The goal is to organize all of your data into a centralized location to power insights and dashboards. Business leaders need data analysis at their fingertips to make better strategic decisions
  2. Process Automation –The goal is to save time by automating manual tasks and business processes. Instead of manually copying information from one system to another, it should take place automatically
  3. Product Development –The goal is to turn data into valuable products that customers can purchase. These could be insights, automated workflows, or raw data feeds for monetization

Historically, companies would create separate tech stacks, teams, and workflows for each of these workloads. They would hire a business intelligence team to stand up a data analytics stack, write SQL and build dashboards. They would separately hire an IT team to automate workflows with Python or an Integration Platform as a Service (iPaaS) solution. And they would hire engineers and product managers to build data products with on-premises technology or using cloud platforms like GCP, AWS, and Azure.

Nowadays, companies are becoming more data-driven. The technologies, reporting structures, and teams are becoming more complex. Real-time and streaming workflows are being added. Machine learning and artificial intelligence (AI) are common and data teams are looking for a scalable solution for data processing.

With cloud-based software-as-a-service (SaaS) data warehouses like Snowflake and Redshift, data teams are now empowered to centralize all use cases under a single team and data stack.

What Are The Best Options For A Data Warehouse Solution?

As companies undergo digital transformation, one of the first pieces of technology they upgrade is their analytics environment.

Most teams evaluate a handful of the best cloud warehousing solutions including:

  • Snowflake
  • Amazon Redshift
  • PostgreSQL
  • Google BigQuery
  • Azure Synapse
  • Clickhouse
  • Databricks (or Apache Spark)

Now that we've outlined the options, let's dig into comparing the capabilities of two of the best solutions on the market: Snowflake and Redshift.

How Should You Compare Snowflake and Redshift?

Let's walk through the key considerations for each solution, but first, let's provide a quick overview of each platform.

Snowflake. Snowflake offers a SaaS data warehouse for analytics teams. Because Snowflake is not associated with a larger cloud platform (like AWS, GCP, or Azure), it is a neutral solution in the market relative to most data warehousing alternatives and has built a strong ecosystem of technology partners. The Snowflake Data Cloud is one of the best data warehousing solutions for analytics teams.

Redshift. Redshift is a fully managed cloud-based data warehouse. It is part of Amazon Web Services (AWS) and offers essentially unlimited scaling for big data at an affordable price.

Now for the details.

Pricing

Snowflake and Redshift have similar pricing models with nuanced differences.

Snowflake Pricing. Snowflake pricing is based on the volume of data stored and the compute resources you use. The first pricing option is on-demand pricing whereby you pay for usage month to month. The second option is capacity pricing whereby you pay for usage credits upfront.

Redshift Pricing. Redshift pricing is based on the hours your instance is running. You can select On-Demand Instances with no long-term commitment or you can select Reserved Instances with commitments, but discounted rates. Amazon Redshift Serverless is a new capability set that allows you to spin up Redshift instances that run only while your workflow is processing.

Integrations

Your data warehouse is only as good as the data sources you can ETL into your analytics environment and the downstream use cases you can unlock.

It is common for cloud warehouses to offer native integrations that analyze cloud storage data from the major cloud providers (i.e. Amazon S3, Google Cloud Storage, Azure Blob Storage, etc.). It's always easy to connect downstream visualization tools (i.e. Power BI, Tableau, Looker, etc.) to build dashboards on top of your cloud data warehouse or data lake as well.

Snowflake Integrations. Snowflake's position as an industry leader means it's compatible with most ETL tools for data ingestion and BI tools for visualization and insights. Snowflake also has a large data marketplace with hundreds of third-party apps.

Redshift Integrations. Redshift was one of the earliest cloud data warehouses on the market. It is also part of the Amazon Web Services (AWS) ecosystem, so usage is widespread and integrations are available. Redshift has native integrations to AWS platforms like DynamoDB, and a simple interface to connect to visualization tools. With capabilities like Redshift Spectrum (a native capability set similar to AWS Athena), users can even analyze data living in cloud storage buckets directly from Amazon.

For both platforms, there are always data sources that are not natively integrated. This is a common scenario where clients use Portable's 300+ no-code ETL connectors to sync data.

Database Features

Once data is loaded into your analytics environment, you need to be able to process the data. To do so, the warehouse you select needs to have strong database capabilities.

Snowflake Database Features. Within Snowflake, data is organized into databases and schemas. A schema organizes tables, views, and data, while a database organizes your schemas. Snowflake has strong database capabilities (including JSON processing) and recently released Unistore, which allows Snowflake to act as both an analytical and transactional database.

Redshift Database Features. Redshift has very robust database features, allowing for the processing of data from disparate formats and data types. Redshift offers a simple API to interface with the platform and can be queried using your console or SQL client of your choice.

Ecosystem

In the data world, no one platform will be able to solve every problem for a client. By building ecosystems, cloud warehouses can partner with a wide array of industry-leading tools and technologies to offer solutions bigger than a single product.

Snowflake Ecosystem. Snowflake has created a remarkable brand, a strong ecosystem, and deep partnerships with companies across the modern data landscape. In addition to the typical partnerships you can find with most warehousing solutions, Snowflake offers data-sharing capabilities that can help users directly share data sets with clients or partners.

Redshift Ecosystem. As an Amazon cloud service, Redshift is part of one of the largest cloud ecosystems on the planet. You can expect strong integrations and partnerships with other solutions in the Amazon ecosystem. Amazon has also created a data marketplace to help drive ecosystem usage and engagement for Redshift.

Performance And Maintenance

Technical performance and maintenance are critical for any analytics, automation, or product development use case. As data volumes grow, it's important to leverage capabilities like caching, vacuuming, and concurrency scaling. Let's outline the considerations for both Snowflake and Redshift.

Snowflake Performance And Maintenance. Snowflake stores data in a columnar format within the storage layer allowing Snowflake to only scan columns that are referenced by a query. By pioneering the concept of separating data storage from compute resources, Snowflake has offered strong performance whether the volume of data you are processing is small data, large data, big data, or petabyte-scale data.

Redshift Performance And Maintenance. In recent years, Redshift has had a reputation for being less performant than other data warehouses; however, nowadays, Redshift is highly performant and scalable. The platform offers massively parallel processing by having your Redshift cluster fan-out queries to compute nodes. From a maintenance perspective, Redshift offers similar capabilities to other best-in-class platforms.

Security, Governance, Compliance

The foundation of any data initiative must always be security, governance, and compliance. Not just encryption, but also role-based access control, authentication and authorization, backups, policies, procedures, and granular controls.

Snowflake Security, Governance, Compliance. Snowflake supports several types of data encryption, including end-to-end and client-side, making it one of the most robust options for data security. For larger enterprises, there are ways to deploy Snowflake VPC solutions and integrate them with on-premises systems.

Redshift Security, Governance, Compliance. Redshift offers granular security features including access management, cluster encryption, SSL connections, and more. Because Redshift is part of the AWS cloud platform, warehouses and other AWS services can be set up within their security groups to restrict permissions and access.

Redshift vs. Snowflake: Which Should You Choose?

Choosing a data warehousing solution is an important decision that you need to make based on your own specific needs.

We've outlined the pros and cons of both Snowflake and Redshift to help frame out the scenarios in which each solution makes sense.

One of the best ways to make a decision is to try before you buy. With Portable you can load data into both Snowflake and Redshift, build a dashboard and see how each platform performs before making a final decision.

There's no downside to exploring our connector catalog or moving data to help with the process.