Data is not just an asset for your organization, but the very lifeblood. You can leverage real-time insights for various operations and actions, like personalizing customer experiences and predicting market trends. The staggering amount of data triggers a shift to a strategic imperative—cloud data warehousing.
With an expected market size of $155.66 billion by 2034, the growth is helmed by two titans—AWS Redshift and Snowflake.
If you are weighing a migration from traditional on-premise solutions to a cloud-based one, you might seek an Amazon Redshift vs Snowflake comparison. So, how do you choose?
Your decision will affect your scalability, cost, competitive edge, and other crucial aspects. To make it an easier decision, we are here with a complete comparative guide, uncovering their strengths and weaknesses.
Let’s get started!
What is Amazon Redshift?
Amazon Redshift is a fully managed, cloud-based, and petabyte-scale data warehouse service. The platform can analyze and store data on such a large scale due to its cloud-based compute nodes. It uses column-based databases to form a connection between SQL-based query engines and business intelligence solutions.
Amazon Redshift has a 14.87% share in the data warehousing market. It ranks second in this category, only next to Snowflake.
The platform leverages Massively Parallel Processing (MPP) and PostgreSQL on dense storage nodes. It enables AWS Redshift to deliver query outputs for massive datasets quickly. AWS cloud server infrastructure, like S3, is useful for backing up data. Also, you only pay for what you use.
What is Snowflake?
Snowflake is a cloud-based data platform for secure data sharing, seamless multi-cloud experience, and high-level scaling. The software-as-a-service (SaaS) platform relies on a virtual warehouse framework that uses third-party cloud-compute resources, including Azure, AWS, or Google Cloud Platform (GCP).
Snowflake enjoys a 19.96% share in the data warehousing market and stands in the top position.
The platform does not run on private cloud infrastructures, including on-premises or hosted. It handles all the behind-the-scenes performance tuning, optimization, and infrastructure to help you focus on using insights optimally. It also stores, analyzes, and processes gigantic data volumes cost-effectively.
Elevate your data strategy with Aegis Softtech Snowflake consulting services. We offer comprehensive support to ensure your organization fully leverages Snowflake's capabilities.
Let’s move on to a closer look at Amazon Redshift vs Snowflake on different key aspects.
Redshift vs Snowflake: Architecture
Amazon Redshift architecture handles big datasets, delivers high-performance analytics, and performs complex queries. It traditionally uses a cluster-based architecture with tightly knit storage and compute nodes. Its features, including Concurrency Scaling and Redshift Serverless, offer automatic scaling and high flexibility.
While it now has RA3 nodes to scale compute, it wasn’t originally designed to separate compute and storage. Its serverless option is based on an abstracted unit called Redshift Processing Unit (RPU). Every RPU offers 16GB RAM and 2 vCPUs.
Snowflake boasts a hybrid architecture by combining the best elements of shared-nothing and shared-disk architectures. Its decoupled storage, cloud services, and compute layers offer workload isolation and unlimited compute scale. Virtual warehouses handle computing for independent scaling.
Its unique architecture supports both structured and semi structured data. Since it runs entirely on the cloud, you can create an account on any leading cloud provider’s platform, including AWS, GCP, and Azure. There is extreme flexibility, and you only pay for what you use.
Redshift vs Snowflake: Maintenance
With AWS Redshift, you will have to send queries in a queue. It also automatically inserts additional capacity with concurrency scaling. But these, too, must be managed through workload management (WLM) queues.
Snowflake offers better maintenance with its separate compute and storage architecture, which makes scaling up and down easier. Its auto-resume and auto-suspend features let you downsize the warehouse once you’re done running queries.
Redshift Vs Snowflake: Security
Amazon Redshift provides end-to-end encryption (for both incoming and outgoing data from Redshift) to ensure high data security. It integrates seamlessly with different AWS security tools, including the AWS Key Management Service and VPCs for network isolation. Organizations that are already heavily invested in an AWS-only security ecosystem will benefit from this platform.
Snowflake’s core security features include secure data sharing and granular access controls. Its role-based access control (RBAC) model allows you access to data objects according to your role and not your location. It enables seamless data sharing across clouds, external organizations, and accounts without requiring manual data movement or copying.
Redshift Vs Snowflake: Pricing
Redshift offers a few paths to choose from. The first one is Provisioned Redshift, where you can use On-Demand Instances without any long-term commitments or upfront fees.
The second one is Reserved Instances, where you get greater savings from a long-term commitment. And, the third is Amazon Redshift Serverless, where you pay for your actual usage by automatically terminating, scaling (up or down), and spinning capacity as required.
Snowflake follows a time-based model, which means you are charged for the time you spend executing queries. So, if you run a query in two minutes, you will be charged for those two minutes. Moreover, there are smart Snowflake cost optimization ways that cut down your overall investment in the platform.
Redshift vs Snowflake: Performance
You can quickly execute complex queries with Redshift’s Massively Parallel Processing architecture and columnar storage system. It uses machine learning for high query performance by prioritizing short queries over long-running ones. Two features that help in optimizing its performance are Automatic Workload Management (WLM) and Short Query Acceleration (SQA).
The Snowflake architecture has separate storage and compute resource layers for independent scaling. You can thus increase your compute power/resources (virtual warehouses) according to your growing data. This, along with techniques including automatic clustering, maintains high-speed query performance. Micro-partitioning by Snowflake stores table data in small chunks for parallel partition querying.
Redshift vs Snowflake: Use Cases
Redshift is a great choice for organizations that are deeply integrated within the AWS ecosystem. It has seamless connectivity with Glue, SageMaker, and S3 data lakes. Its traditional MPP design benefits organizations that prefer to have control over their cluster infrastructure for performance tuning. It supports near real-time analytics within Amazon Web Services.
Snowflake works well for unpredictable and elastic analytical workloads because of its unique architecture. Organizations with diverse cloud strategies receive additional support from their multi-cloud capability. Since it handles semi-structured data (Parquet, JSON), it is also a great choice for various Snowflake use cases, including web analytics, IoT, and logs.
Redshift vs Snowflake: Scalability
Redshift, with its Concurrency Scaling feature, offers strong scalability. You can add or remove nodes to handle increasing query loads and data volumes. It provides transient capacity for sudden spikes in concurrent users and queries.
Redshift Serverless helps simplify this by dynamically allocating compute resources. However, while scaling operations are flexible, they may include managing cluster sizes or configurations.
Snowflake, with its unique architecture, completely separates storage and compute. The result is independent scaling of compute and storage. You can simply size the virtual warehouses up or down instantly for vertical scaling. Similarly, you can auto-scale out by adding more clusters for horizontal scaling. Its heightened level of elasticity ensures cost efficiency and optimal performance without manual intervention.
When to Choose What: Redshift vs Snowflake
Are you struggling to pick the right cloud data warehouse for your business needs? It’s a common dilemma many enterprises face.
Deciding between Redshift and Snowflake may feel like a big decision, especially considering both offer powerful analytics capabilities. There are a few aspects that determine which one truly aligns with your specific goals, budget, and existing infrastructure.
Let’s break down when each platform shines.
Choose Redshift When:
1. Deep AWS Integration
If you are already heavily invested in the AWS ecosystem, Redshift is a better pick. It offers seamless integration with other AWS services, including EC2, Glue, and S3. It simplifies data pipelines and management within a familiar environment.
2. Predictable Workloads & Cost Control
Leverage Redshift Reserved Instances if your data processing demands are consistent. The commitment can significantly save costs, especially compared to on-demand pricing.
3. Granular Control Over Infrastructure
You prefer a higher degree of direct control over your cluster’s configuration, including storage, networking, and node types. Redshift offers the flexibility to fine-tune important aspects for specific performance requirements.
Choose Snowflake When:
1. Elastic Scalability & Concurrency
Your business experiences highly variable workloads or supports multiple concurrent users. Snowflake boasts a unique architecture that enables independent scaling of storage and compute. It automatically adjusts resources up or down to meet business demand without manual intervention.
2. Multi-Cloud Strategy
If your organization operates across multiple cloud providers or plans to in the future, choose Snowflake. It offers a consistent management and experience across leading clouds, preventing vendor lock-in and simplifying operations.
3. Zero-Maintenance & Fully Managed
Businesses prioritize lower operational overhead. Snowflake is a fully managed service that handles all infrastructure, optimization, and patching automatically. Thus freeing your team to focus on data analysis.
Data Migration Made Seamless: The Aegis Softtech Advantage
A whopping 402.74 million terabytes of data is created daily, making data one of the biggest weapons you can use to succeed today. The correct usage, however, is also one of the biggest areas of concern for most organizations.
Our Snowflake vs Amazon Redshift comparison dissects all the important differentiating aspects to paint a clearer picture.
Making a choice can be quite a challenge, which makes having a trusted partner a critical decision. At Aegis Softtech, we have 20+ years of experience in delivering scalable, end-to-end data warehouse solutions. Our team of experts will carefully assess your business and build a plan around it.
Intrigued? Get ready to utilize the power of your data. Reach out to our experts in Snowflake development services by scheduling a free call.
Learn how you can unify all your business data for results that you deserve.
FAQs
Q1. Is Redshift better than Snowflake?
There is no definitive answer to which one is better since it all depends on your business needs and priorities. If you work primarily within the AWS ecosystem, then Redshift could be a better option. However, Snowflake’s cloud-agnostic approach, unique architecture, and ease of use give it an edge.
Q2. Is Amazon Redshift an ETL tool?
No, Redshift is not an ETL (Extract, Transform, Load) tool, but rather a cloud-based data warehouse service.
Q3. Why migrate from Redshift to Snowflake?
Migrating from Redshift to Snowflake can give you benefits like better performance, cost savings, simpler data sharing, and better scalability.
