Snowflake vs Redshift: An In-Depth Guide to Choosing the Right Cloud Data Warehouse


Hi there! As a fellow data analyst, I know how challenging yet critical it is to choose the right cloud data warehouse solution for your organization. Both Snowflake and Redshift are extremely powerful, but each has unique strengths and weaknesses depending on your specific needs.

In this comprehensive guide, I’ll arm you with in-depth knowledge to evaluate and compare Snowflake and Redshift across key criteria so you can confidently pick the best platform for your data, workloads, and use cases.

I’ll share insightful research, interesting stats, my own opinions as an experienced data analyst, and plenty of examples so you have all the information you need to make the smartest choice for your organization. Let’s dive in!

The Rise of Cloud Data Warehouses

First, let’s briefly discuss the factors driving adoption of cloud-based data warehouses:

• Lower costs – By leveraging the cloud provider‘s infrastructure, you avoid large capital expenditures on servers and networking equipment. The pay-as-you-go pricing shifts costs from CapEx to OpEx.
• Increased scalability – Cloud data warehouses scale massively and elastically to accommodate volatile workloads. You only pay for the resources used.
• Enhanced flexibility – Capacity can be adjusted quickly based on changing requirements. On-premises data warehouses are far less agile.
• Faster deployment – Cloud data warehouses can be up and running in minutes versus weeks or months for on-premises deployments.
• Improved availability – Leading cloud providers offer 99.99% or better SLAs for availability. Redundancy across regions enhances resilience.
• Managed service – The cloud provider handles all the maintenance and optimization. You avoid dedicating precious internal IT resources.
• Tight integration – Cloud data warehouses integrate seamlessly with data sources, analytics tools, and other complementary services.

According to Gartner, the cloud data warehousing market grew over 35% in 2019. By 2022, it‘s projected to reach over $13 billion. Redshift and Snowflake currently lead the market.

Snowflake Overview and Capabilities

Snowflake pioneered a breakthrough cloud-native architecture expressly designed for the cloud rather than retrofitting an on-premises design. This gives Snowflake unique advantages:

• Multi-cluster shared data architecture – Snowflake spans multiple clusters and enables dynamic sharing of resources across virtual warehouses. This provides extreme scalability and concurrency.

• Independent scaling – Compute and storage scale independently. This allows precise alignment of resources to your workload‘s needs.

• Per-second billing – Snowflake uses a pay-per-second model rather than hourly billing like Redshift. For spiky workloads, this significantly lowers costs.

• Time travel – Snowflake offers unlimited data history for traveling back in time. Redshift data history depends on backup retention policies.

• Data sharing – Snowflake enables securely sharing live data across accounts and even organizations. Redshift data sharing is limited within an AWS account.

• Semi-structured data – Native support for semi-structured data like JSON, Avro, and Parquet gives Snowflake an advantage with modern data types.

According to Gartner‘s 2020 Magic Quadrant, "Snowflake is the runaway leader in this market due to a combination of product capabilities like time travel and data sharing and near-flawless execution."

Let‘s look at some stats that highlight Snowflake‘s dominance:

• 134% YoY revenue growth in Q2 FY21 – Snowflake is growing at an astounding pace as enterprises rapidly adopt their solution.
• 3,117 total customers including 146 of the Fortune 500 – Blue chip customer base attracted by Snowflake‘s innovations.
• 171 of the Forbes Global 2000 as customers – 37% penetration into the largest public companies.
• $127M average contract value based on Q2 FY21 results – Huge contract sizes reflect massive enterprise deployments.
• 79 customers with $1M+ in product revenue – These major customers demonstrate Snowflake‘s ability to scale.

Snowflake is clearly riding a huge wave of momentum thanks to its cloud-native architecture and stellar execution in the market. But what about Redshift?

Redshift Overview and Capabilities

Redshift constitutes Amazon’s cloud data warehouse offering. Its key characteristics:

• MPP architecture – Like Snowflake, Redshift leverages distributed MPP design for performance at scale.

• Columnar storage – Columnar storage minimizes I/O and boosts analytics query performance since only relevant columns are read.

• Integration with AWS services – Redshift integrates seamlessly with services like S3, EMR, RDS, and SageMaker.

• Result caching – Redshift caches query results to significantly improve response times for repeated queries.

• Continuous replication – Redshift can replicate production data to isolation workloads to prevent impacting mission-critical OLTP operations.

• Automatic backups – Redshift continuously backs up data to S3 and enables restores up to 35 days.

Although Redshift pioneered cloud data warehousing and remains a powerful solution, especially for AWS-centric organizations, Snowflake has surpassed Redshift in recent years through its innovative architecture, flexible pricing model, and booming popularity with customers.

Redshift growth declined to 21% in Q2 2020 versus over 100% for Snowflake, indicating Snowflake is gaining considerable market share.

However, Redshift still holds appeal for its deep AWS integration, mature feature set, automated management capabilities, and favorable pricing at scale. The choice between Snowflake and Redshift depends heavily on your organization‘s specific needs and circumstances.

Next let‘s do a deep dive into how Snowflake and Redshift compare across critical evaluation criteria.

Comparing Key Capabilities

Snowflake and Redshift share plenty of similarities – both leverage columnar MPP architectures, support ANSI SQL, deliver high availability, provide security capabilities like encryption, etc.

But they differ meaningfully in several areas:

Architecture

• Snowflake employs a unique multi-cluster, shared data design optimized for the cloud.

• Redshift uses traditional single-tenant clusters like legacy on-premises data warehouses.

• With Snowflake, resources are allocated dynamically based on workload demand. Redshift has fixed resources per cluster.

• Snowflake’s architecture enables far greater scale, concurrency, and workload isolation than Redshift.

• If your workload experiences major spikes or varies widely, Snowflake is much better equipped to handle it.

Clearly, Snowflake‘s groundbreaking architecture provides significant technical advantages over Redshift. Snowflake was built specifically for cloud scale and elasticity. Redshift is anchored to more static on-premises architectural principles.

Performance and Scalability

• Snowflake allows instant, independent scaling of storage and compute. Redshift requires scaling cluster nodes in fixed units.

• Snowflake automatically scales to handle workload surges and concurrency. Redshift relies on manual tuning and scaling.

• Snowflake’s performance stays consistently fast under heavier workloads and concurrency versus slowdowns seen in Redshift.

• However, Redshift offers handy performance optimizations like result caching, workload management, and enhanced monitoring.

• For structured workloads under a billion rows, performance differences are negligible. At larger scale, Snowflake pulls ahead.

The bottom line: Snowflake is designed to dynamically optimize performance and accommodate volatility that cripples Redshift. But Redshift isn‘t far behind Snowflake for more stable workloads under a few hundred terabytes.

Semi-Structured Data

• Snowflake natively supports semi-structured data like JSON, Avro, Parquet, and ORC.

• Redshift focuses primarily on traditional structured data like CSV and relational sources.

• Snowflake’s flexibility with semi-structured data enables richer insights by tapping into diverse modern data sources.

• If your enterprise utilizes NoSQL databases, event streams, or object storage like S3, Snowflake is better equipped.

Obviously, Snowflake has a substantial advantage ingesting and analyzing semi-structured and unstructured data prevalent in modern datasets. Redshift lags here.

Pricing and Billing

• Snowflake uses per-second billing rather than hourly for Redshift, which can drive extreme cost savings.

• For workloads with short queries or large variance, Snowflake is far cheaper. Redshift hourly commits you to the full hour.

• But at larger scale with steady workloads, Redshift‘s pricing tiers become very competitive on a per-terabyte basis.

Depending on your workload patterns, Snowflake‘s per-second billing can dramatically reduce costs. But don‘t underestimate Redshift‘s pricing advantage for heavy, consistent workloads at scale.

Data Sharing and Marketplace

• Snowflake’s unique architecture enables seamlessly sharing live data across accounts or even organizations.

• Redshift data sharing is limited to within a single AWS account.

• The Snowflake Data Marketplace and Partner Connect make it easy to discover and leverage third-party data.

• Redshift lags far behind Snowflake on data sharing capabilities and availability of value-added data/service integrations.

For data monetization or harvesting third-party data, Snowflake is far ahead of Redshift in capabilities for data sharing and marketplace integration. Redshift‘s data sharing options are relatively primitive.

Data History and Time Travel

• Snowflake offers unlimited data history, enabling analyzing past snapshots for trend analysis or auditing.

• Redshift data history depends on configuring and maintaining database snapshots and backups. Historical data is limited.

Unquestionably, Snowflake’s essentially unlimited data history for time travel analysis is a game-changing capability Redshift can’t match. Redshift data history requires manual configuration and administration.

Security

• Both platforms offer encryption, access controls, VPC support, compliance certifications, authentication integration, and more.

• Snowflake has native end-to-end encryption. Redshift leverages AWS KMS for encryption.

• Snowflake MFA support is more flexible. Redshift uses AWS tools for MFA.

• Snowflake enables cross-org data sharing without compromising security.

When it comes to security, Snowflake and Redshift are comparable overall. Snowflake offers better native security tooling, but both meet enterprise standards. For most, security shouldn‘t be the deciding factor.

Key Factors to Consider

Based on the analysis above, here are some key points to consider when choosing between Snowflake and Redshift:

For Snowflake

• Your workload experiences major spikes or variability
• Need to analyze semi-structured data sources
• Flexible per-second billing significantly lowers costs
• Require historical data for time travel analysis
• Cross-organization/account data sharing is important
• Your enterprise utilizes multiple cloud platforms, not just AWS

For Redshift

• Tight integration with AWS services is critical
• Workload involves heavy ETL from S3, RDS, etc.
• Pricing advantage from steady, high-volume structured workloads
• Existing expertise in cluster management and tuning
• Leveraging Redshift-specific features like replication

Architectural Comparison

Here is a helpful table summarizing the key technical differences between Snowflake and Redshift:

	Snowflake	Redshift
Architecture	Multi-cluster, shared data	Single-tenant clusters
Resource allocation	Dynamic	Fixed per cluster
Scaling	Independent for storage and compute	Cluster node-based
Performance optimization	Automatic	Manual tuning required
Concurrency	Excellent	Limited
Semi-structured data	Native support	Limited support
Time travel analysis	Unlimited history	Constrained history
Data sharing	Seamless across orgs	Limited within account

Snowflake‘s architecture delivers fundamental advantages in elasticity, concurrency, and workload isolation. But Redshift offers familiarity for on-premises data warehouse veterans.

Expert Recommendations

For smaller workloads under 100 TB where variability is limited, Redshift remains a cost-effective option leveraging AWS‘s pay-as-you-go pricing and tight integration. The overhead of Snowflake may not justify the investment.

But in my experience, Snowflake becomes the superior choice for larger, more dynamic workloads, especially those involving semi-structured data. The architectural and performance advantages outweigh Redshift‘s economies of scale.

I‘ve also found Snowflake‘s per-second billing and unlimited historical data create tremendous value even for smaller workloads. Time travel analysis and ad-hoc exploration are game-changers.

Ultimately, every organization has unique priorities and constraints guiding the ideal data warehouse solution. Use the comprehensive perspective provided in this guide to determine whether Snowflake or Redshift is the best choice for your needs. Neither option is one-size-fits-all.

I hope you‘ve found this information helpful for your decision process. Don‘t hesitate to reach out if you have any other questions! I‘m always happy to help fellow data analysts.