Snowflake vs Redshift: Best Data Warehouse for SaaS

Data Engineering
Sep 15, 2023
Snowflake vs Redshift: Best Data Warehouse for SaaS

Whether you want to do data analysis, data modeling, data visualization or something else, you’ll need a reliable data warehouse to get the job done. With so many choices and most platforms offering similar functionalities and pricing, it can be difficult to make the right choice.

Today, we’re going to compare two popular choices for data scientists and engineering teams: Snowflake vs. Redshift. Let’s take a look at the key features, similarities and differences, as well as pricing, to help you choose your next data warehouse.

What is Snowflake?

Snowflake is a powerful data warehouse that stores, manages, and analyzes large data volumes in the cloud. It’s cloud-agnostic, meaning you can host it on your cloud service of choice: Amazon Web Services (AWS), Google Cloud Platform (GCP) or Microsoft Azure.

Businesses use Snowflake as an analytical database for structured and semi-structured data. Generally, it’s a great fit for many business intelligence use cases. It’s also popular for real-time analytics and streaming big data, thanks to its specific architecture and custom SQL query engine.

What is Amazon Redshift?

Amazon Redshift is a PaaS (Platform as a Service) data warehousing tool and one of the first tools in this industry. Launched in 2013, AWS Redshift was one of the first data warehouses in the industry. It uses SQL for querying and it’s one of the most popular choices for data science professionals today.

It’s a fully managed data platform, running on Amazon Web Services (AWS), to store large amounts of data in a structured way. Because of its architecture and its integration with many data analysis tools, it is perfect for analytics use cases with big datasets.

Snowflake vs Redshift: key similarities

Since they are both cloud-based data warehouses, Snowflake and Redshift have plenty in common. Before we look into how they are different, let’s look at some of the key similarities.

Data storage

Snowflake and Redshift both use columnar storage, which makes them great for analytical queries. With typical relational databases like PostgreSQL storing data in rows, your query time can take a big hit. If you want to do complex analytical queries, using a columnar database will speed up queries and make them more efficient.

Besides structured data, both data warehouse solutions also support semi-structured data formats like JSON, Avro, Parquet and ORC. However, while Snowflake supports them natively, for Redshift you’ll need to use Redshift Spectrum. It queries semi-structured data directly in Amazon S3, without loading it into tables first.

Performance and querying

Although Redshift and Snowflake have different architecture, there are some similarities in how they deal with performance and query optimization.

First of all, they both use Massively Parallel Processing (MPP). This data processing technique distributes data across multiple nodes, so that smaller workloads can run in parallel. By putting considerably less strain on heavy workloads, your queries will run much faster. Great if you’re dealing with very large datasets or complex queries.

Besides heavy workloads, both Redshift and Snowflake also support concurrency scaling. That means they can handle multiple queries at the same time. So for example, if you’re dealing with peak usage periods, you won’t run into time-outs but have consistent, smooth querying all the while.

Concurrency scaling diagram for Amazon Redshift

Data integration

If you need to integrate and analyze data from different data sources, Snowflake and Redshift are great solutions. They both support integrations with the most popular ETL tools, and offer options to query data directly in data lakes.

Snowflake vs Redshift: key differences

Whether Snowflake or Redshift is the best fit for you will depend on your specific needs. Let’s look at some of the key differentiators to help you make a better-informed decision.


Snowflake and Redshift handle computing differently, with different architecture. Snowflake uses virtual warehouses, where each virtual warehouse has a group of computing resources. Redshift users a cluster-based architecture, where each Redshift cluster has a leader node and additional compute nodes.

But what does that mean in practice? Below are a few guidelines to understand which one is better for your use case.

Snowflake is better if:

  • your workloads are unpredictable, and you want to scale computing up or down automatically.
  • you have many users querying at the same time, since the separate virtual warehouses are a bit smoother in performance.
  • you don’t want to deal with lots of manual, hands-on tuning to resize your data warehouse.
  • you need a clear separation between storage and computing, which is at the core of Snowflake’s architecture.

Redshift is better if:

  • you want to hold more control over scaling compute resources, for example to have a tighter grip on performance and cost.
  • most of your tech stack is running on the AWS ecosystem (e.g. DynamoDB, S3, AWS) for a tighter integration with your existing infrastructure.
  • you are using Amazon S3 for storage, as this will allow you to separate storage and computing with Redshift Spectrum, just like Snowflake separates the two.
Redshift vs Snowflake architecture diagrams

Security and access management

Both Snowflake and Redshift have robust security features, but they handle things like access management and authentication differently.

If you’re using other AWS services, Amazon Redshift is again great because of its easy integration. You can use AWS Identity and Access Management (IAM) for setting user permissions, and also for authentication.

Snowflake uses a built-in role-based access control system (RBAC) to assign user roles. It’s user-friendly, and a great option if you don’t want to rely on third-party tools or integrations. Snowflake is also great for multi-cloud environments since it’s cloud-agnostic. Unlike Redshift or BigQuery, which are limited to their respective clouds.

In terms of security, both have outstanding data encryption and network security. Redshift uses Virtual Private Cloud (VPC), with AWS Security Groups to control traffic to your clusters. Snowflake uses Virtual Private Snowflake (VPS), and although it doesn’t use security groups, you can restrict access with IP whitelisting.

Performance and scalability

Snowflake is the best option if you want to automate query optimization and performance improvements. If you need more granular control over your performance, Redshift is a better fit for you.

As mentioned above, this is because of Snowflake’s automatic scaling and data distribution. It uses a process called micro-partitioning, which means it automatically breaks up vast amounts of data into smaller, more manageable parts. This process drastically speeds up query performance without manually tinkering with your data warehouse.

RedShift, on the other hand, is a much better solution if you are running complex workloads that you need to control diligently. It automates optimizations for performance to some extent, but there is some manual tuning involved to perfect it. For example, you need to set distribution keys to decide which data sits in which node, and do ‘vacuuming’, which reorganizes the data when you add or delete data.

For a painless, low-maintenance setup - go for Snowflake. For flexibility and control over performance - Redshift is the way to go.

Snowflake vs Redshift: pricing

Although pricing shouldn’t be the main reason for choosing a data warehousing solution, it’s definitely worth researching. Snowflake and Redshift can both be cost-effective. But depending on your needs, one might be better than the other.

Snowflake uses an on-demand pricing model. Compute costs are charged on a per-second basis, so you pay for the amount of time it takes to process a query. If your queries are short and efficient, you can save a lot of money. Snowflake also separates compute and storage costs, which gives you a lot of flexibility. If you’re running heavy queries, but aren’t scaling your storage, their model is a great cost-cutter. Data storage costs depend on the amount of data and the level of data replication.

Redshift, on the other hand, uses flat-rate pricing, which means you’ll know upfront how much you’re going to pay more or less. If your workloads are predictable and you run a lot of queries consistently, Redshift might be cheaper than being charged per second. You pay for the capacity of your cluster, so as long as you stay within your node’s limits, you can run as many queries as you want.

Although Snowflake’s pricing is more flexible and variable, Redshift’s flat-rate pricing is more predictable. Both have their advantages and disadvantages, depending on your use case and budget.

Which data warehouse is right for you?

Redshift and Snowflake are both solid providers for SaaS companies who are building data analytics features.

If you have a steady, high volume of queries, and you need tight control over query performance, AWS Redshift is your best choice. Bonus points if you’re using AWS as your cloud provider, as it integrates seamlessly with other AWS tooling.

If you need to run multiple complex workloads at the same time, and you don’t want to tinker around with manual query optimization, Snowflake’s multi-cluster architecture and automated optimizations are a great fit.

Once you’ve decided on the data warehouse is best for you, you can start with the fun stuff: turning cloud-based data into interactive dashboards! If you need a good visualization tool that embeds seamlessly into your SaaS product, look no further than Luzmo. It has out-of-the-box connectors to BigQuery and Snowflake, so you can immediately start building dashboards for your product users in days, not months.

Our team of analytics experts will gladly show you a demo, and advise you on the best data infrastructure for your use case. Book a free consultation today!

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