As data volumes explode, companies need robust cloud data platforms that can scale on demand while delivering actionable insights from petabytes of information. Two leading options, Snowflake and Amazon Redshift, seem similar on the surface – fully managed cloud data warehouses supporting SQL analytics. But under the hood, their distinct architectures lead to key differences in performance, costs, security, and use case suitability.
This in-depth comparison examines how Snowflake and Redshift stack up across crucial criteria like scalability, maintenance, automation, pricing, ease of use, and more. Read on to gain the understanding needed to select the best data warehouse for your needs.
What is Snowflake?
October 2014, Snowflake’s cloud-native data warehouse burst onto the scene after two years in stealth mode, with a uniquely innovative architecture separating storage and compute. Rather than retrofitting existing database software for the cloud like some competitors, Snowflake engineered a solution from the ground up designed specifically for cloud efficiency, flexibility, and scalability.
The Snowflake architecture consists of three independent layers:
- Storage, which organizes incoming data for optimal accessibility.
- Compute, which leverages “virtual warehouses” that can expand elastically to accommodate workload spikes.
- Cloud services, which coordinates activities across the platform.
This groundbreaking approach makes Snowflake incredibly adept at supporting diverse data needs. The separation of storage and compute enables independent scaling to accommodate fluctuations in users, queries, and data volumes. Snowflake is also multi-tenant, being able to run on AWS, Azure, or Google Cloud infrastructure. It handles all hardware provisioning, configuration, tuning, and maintenance itself, allowing users to focus on extracting value from data.
Snowflake shines for organizations requiring a versatile, industrial-strength cloud data platform capable of centralizing diverse data types and workloads. Its capabilities have sparked rapid adoption across industries.
Snowflake key features
Snowflake's architecture blends the best of traditional and modern data warehousing technologies. Key features include:
- SQL support – Snowflake uses standard SQL along with extended support for semi-structured data like JSON.
- Efficiency at scale – Snowflake handles many concurrent users and queries efficiently without degradation in performance.
- Data exchange – Snowflake Data Exchange enables data sharing with specific groups of business partners, both internal and external.
- Marketplace – Snowflake Marketplace provides businesses with access to a vast repository of over 2,300 live, ready-to-use data, services, and Snowflake Native Apps, provided by over 530 third-party partners.
- Security – Snowflake provides enterprise-grade security, encryption of data in transit and at rest, access controls, and compliance certifications.
- Fully managed service – Snowflake takes responsibility of all aspects of data storage — the organization, file size, structure, compression, metadata, statistics etc.
- Web interface – browser-based UI provides dashboards, workflows, and self-service capabilities.
- Time travel – Snowflake provides access to historical data for rollback, cloning, or audit purposes with zero-copy cloning and near-infinite Time Travel.
- Auto-scaling – Snowflake provides automated clustering, auto-suspend, auto-resume, and auto-scaling capabilities to optimize resources and costs.
- Pay-per-second billing – compute usage is billed by the second, and storage is billed monthly. This aligns costs closely with consumption patterns.
Now, let's talk about Redshift.
What is Amazon Redshift?
Initially released in 2012, Amazon Redshift pioneered cloud data warehousing by allowing companies to launch petabyte-scale data warehouses in minutes on AWS infrastructure. Redshift automates hardware provisioning, installation, configuration, scaling, security, backups, patching, and more.
What make Redshift exceptional is that it uses a shared-nothing architecture, running on isolated clusters of compute nodes that can massively parallel process queries. A leader node handles external communication and distributes workloads among the compute nodes storing data locally. Redshift uses columnar storage, data compression, and machine learning to optimize analytics performance.
Unique capabilities like Redshift Spectrum allow querying exabytes of unstructured data directly in Amazon S3 without loading or transforming data. Redshift provides a fully managed enterprise-grade cloud data warehouse tightly integrated with AWS. It powers analytics applications ranging from business intelligence (BI) to machine learning.
Source: Amazon Redshift
Amazon Redshift key features
Redshift's architecture and pricing model are tailored to handle large-scale data workloads efficiently through a number of key features:
- Columnar storage – Redshift uses a high-performance columnar data storage system, optimized for OLTP (Online Transaction Processing) queries, making data compression and retrieval more efficient compared to traditional row-based systems,
- Massively parallel processing architecture – Redshift leverages MPP architecture to enable fast analytics across large datasets.
- Native AWS integration – easily integrates with AWS data sources like S3, EMR, and DynamoDB for data pipelines.
- Security – provides encryption plus integration with AWS security services like Amazon Virtual Private Cloud (VPC) and Identity and Access Management (IAM) for access controls.
- Automated scaling – Amazon Redshift can automatically scale storage and compute based on defined workload needs.
- Concurrency scaling – Amazon Redshift can add and remove computing capacity automatically based on concurrent usage levels.
- Redshift Spectrum – enables direct querying of exabytes of unstructured data in Amazon S3 without loading.
- ML optimization – Redshift uses machine learning to improve query performance through query optimization and prioritization.
- Fully managed service – Redshift is easy to deploy, as it automates provisioning, backups, patching, and more.
- Pay-as-you-go pricing – Redshift offers flexible billing based on provisioned resources consumed per hour or monthly.
Snowflake vs Redshift: in-depth comparison
Now that we have provided a high-level overview of Snowflake and Redshift independently, let’s dig into the details of Redshift vs Snowflake comparison. How these two cloud data warehouse solutions differ across some key factors?
Snowflake vs Redshift: performance
Blazing fast query performance is imperative for data platforms. Snowflake vs Redshift take different architectural approaches to deliver optimal query speeds.
Snowflake’s independent scaling of storage and compute lets you boost cluster resources to maintain performance as data volumes or users grow. The shared data, multi-cluster architecture allows running multiple queries in parallel without contention. Techniques like automatic clustering and micro-partitioning also enhance performance.
Redshift leverages columnar storage and massively parallel processing (MPP) for fast query speeds. Its machine learning optimizations like Short Query Acceleration (SQA) and automatic Workload Management (WLM) maximize performance by allocating resources dynamically based on query complexity and priority.
Both data warehouses are capable of impressive performance, but Snowflake’s flexibility offers more control, while Redshift provides tighter integration with other AWS services.
Snowflake vs Redshift: scalability
The ability to scale your data warehouse smoothly is crucial as data and user demands grow. Architectural differences give Snowflake and Redshift distinct scalability characteristics.
Snowflake was purpose-built around the concept of independently scaling storage and compute. This means you can expand your cluster resources or storage capacity on demand without downtime. Snowflake also automatically optimizes performance by scaling query clusters up and down based on workload.
Redshift uses a more traditional relational database scaling model of adding nodes to the cluster as needed. So you have to manually track usage spikes and scale by provisioning larger or additional clusters. While Redshift offers some automated concurrency scaling, the core architecture is not as flexible as Snowflake’s.
For workloads with significant fluctuations or rapid growth, Snowflake’s independent scaling provides seamless flexibility, whereas Redshift requires meticulous tracking and capacity planning.
Snowflake vs Redshift: maintenance
Ease of maintenance is another key data warehouse consideration, as it affects overhead costs and management complexity.
Snowflake simplifies maintenance since storage and compute are decoupled. Resources can be spun up or suspended on demand without having to resize fixed clusters. Snowflake also automates optimizations like concurrency scaling, minimizing manual tuning.
Redshift requires more hands-on maintenance, like pre-defining distribution styles and sort keys to enhance performance. Scaling and resizing operations can also take significantly longer with Redshift compared to Snowflake’s seamless elasticity.
For users that want to minimize overhead managing infrastructure, Snowflake’s architecture and automation simplify maintenance whereas Redshift has a steeper learning curve.
Snowflake vs Redshift: automation
Related to maintenance, the degree of workload automation also differs between Snowflake vs Redshift architectures.
Snowflake was built for the cloud, so it provides extensive automation capabilities out of the box. Snowflake can automatically suspend, resume, and scale resources dynamically based on workload patterns to optimize performance and scalability, significantly reducing the operational burden and lowering total cost of ownership.
Redshift offers some automated features like concurrency scaling to handle usage spikes temporarily by adding and removing clusters. Generally, Amazon Redshift enables the automation of cluster management operations through AWS CloudFormation, for tasks like creating clusters, managing their states, and optimizing resource usage. But it relies much more heavily on manual monitoring and intervention to tune performance and manage infrastructure.
Snowflake’s native cloud automation allows it to adapt to evolving demands seamlessly whereas Redshift requires vigilant oversight to optimize configurations and resources.
Snowflake vs Redshift: security measures
For any data solution, security is an absolute necessity. Snowflake and Redshift both provide robust security capabilities but approach them differently.
Snowflake’s architecture revolves around a sophisticated, granular role-based access control model spanning storage, compute, and services. Users only access data their role allows, enabling unified security policies across accounts, platforms, and regions. Snowflake also provides robust encryption and data masking.
Redshift, the most prominent of Snowflake alternatives, relies more on end-to-end encryption, integrating with AWS security services like Key Management Service (KMS) and Virtual Private Cloud (VPC) to restrict access. While permissions can be granted selectively, data sharing involves exporting and importing data.
Both offer strong security postures, but Redshift fits well for users heavily invested in AWS-centric security tools.
Snowflake vs Redshift: pricing
Data warehouse costs quickly scale with usage, so pricing models are pivotal considerations when choosing solutions.
Snowflake pioneered a pay-per-second billing model for compute paired with monthly storage pricing. Users are billed per second only for resources consumed during query execution. Snowflake cost cannot be precisely calculated in advance. However, the pricing model offers workload-based optimizations to minimize wasted spend on idle resources.
Redshift offers flexible pricing options tailored to specific usage patterns. On-demand instances allow pay-per-hour billing for compute with no commitments. For steady-state workloads, reserved nodes offer up to 76% discounts for term commitments. Additional capabilities like Concurrency Scaling and Redshift Spectrum are priced based on usage.
For spiky, unpredictable workloads, Snowflake’s fine-tuned usage pricing aligns costs tightly with value. But Redshift offers more diverse options to balance performance and spend for more stable needs.
Snowflake vs Redshift: ease of use
Finally, the developer experience and learning curve required for administrators and users is pivotal in data warehouse adoption. When it comes to ease of use, a key aspect in the Snowflake vs Redshift battle, which one emerges as the superior choice?
Snowflake prioritizes simplicity, integrating directly with major cloud platforms and providing unified web-based control and automation features. The decoupled architecture also gives developers flexibility to scale resources independently.
While Redshift does require more management of clusters and servers, it also offers tight integration with AWS services and tooling specifically for administrators already working within that ecosystem.
For cross-cloud flexibility with minimal tuning, Snowflake is simpler to ramp up. But within an AWS-centric environment, Redshift builds smoothly on existing skills and toolchains.
Snowflake vs Redshift: use case scenarios
The ideal data warehouse depends heavily on your organization's specific needs and technical environments. Following we break down suitable scenarios for Amazon Redshift vs Snowflake.
When to use Snowflake: pros
Let’s focus on Snowflake benefits first. You will be more likely to choose Snowflake if your organization:
- Already uses tools outside just the AWS stack; Snowflake integrates broadly,
- Need robust support for semi-structured data like JSON and XML,
- Have dynamic workloads with significant usage spikes and lulls; Snowflake scales seamlessly,
- Priority is minimal management overhead and maximum flexibility.
- Have data sharing needs across accounts, clouds, and external organizations.
- Seeking strong workload isolation and customizability with fewer vendor restrictions.
For example, Snowflake use cases include media companies with large volumes of JSON data on user engagement across apps and platforms that additionally need flexible scaling to handle spikes in traffic and analytics.
When NOT to use Snowflake: cons
Cons of Snowflake making Redshift the better choice in Snowflake vs Redshift are cases when the company:
- Primarily uses AWS services currently – Redshift integrates tightly with the AWS ecosystem,
- Mainly have steady-state workloads with predictable sizing and concurrency patterns,
- Seeking lower complexity even if it means less real-time flexibility,
- Already have in-house expertise with AWS services and want to leverage those skills.
For example, we would discourage choice of Snowflake for an automotive company running steady ETL workflows with predictable sizing needs that wants to minimize complexity.
When to use Redshift: pros
Redshift is the most natural direction when choosing between Redshift vs Snowflake when your company is already using AWS services extensively and:
- Need to scale up to massive petabyte-scale structured datasets,
- Require steady, cost-effective performance for traditional BI reporting and dashboards,
- Seeking fast, simple deployment of a cloud data warehouse on AWS,
- Need close integration with AWS data services like S3, EMR, DynamoDB, and more.
For instance, when an e-commerce company requires fast deployment of a cloud data warehouse for BI dashboards and reports, Redshift will be the most recommended choice.
When NOT to use Redshift: cons
We found four distinct reasons why Redshift may face defeat in the Snowflake vs Redshift if the competition was announced by your organization:
- Company extensively uses semi-structured data like JSON; Redshift optimizations favor structured data,
- Organization requires real-time elasticity and auto-scaling; Redshift has more rigid scaling models,
- You need to accommodate many concurrent queries or highly dynamic workloads,
- Company plans to leverage multiple cloud platforms; Redshift runs solely on AWS.
For example, for a software firm using BigQuery for analytics across GCP and Azure, Redshift would be hard to implement, as the company needs multi-cloud support.
TL;DR Snowflake vs Redshift
Ultimately there are no absolutely “right” or “wrong” choices – just tradeoffs. For enterprises seeking an industrial-strength, flexible solution spanning multiple clouds, Snowflake is hard to beat. But within a purely AWS-centric environment handling structured analytics, Redshift may provide the best performance and value.
The optimal data warehouse depends heavily on your technical environment, data types, workload patterns, and business priorities. By understanding the core architectural differences between Snowflake and Redshift, you can make an informed choice aligned to your needs.
Here is a comparison table for Snowflake vs Redshift focused on difference between Redshift and Snowflake:
Regardless of whether you choose Redshift vs Snowflake or vice versa (feel free to reach out to us if you are unsure which one would work best), RST Software can help you implement both solutions, tailored to your specific requirements. Simply contact us, and we will assign one of our cloud experts to evaluate your business case.