As an enterprise architect advising clients on analytics modernization initiatives, few decisions carry more weight than your cloud data warehouse choice. With mounting data volumes and appetite for real-time insights, migrating to the cloud promises compelling benefits. But between Snowflake‘s rapid innovation and Redshift‘s tight AWS integration, I‘ve seen organizations struggle to pick between the two popular options.
In this comprehensive 2800+ word guide, we’ll cover everything you need to make an informed choice based on your specific use case, workloads and technical requirements. I’ll draw on hard-won experience helping Fortune 500 companies succeed with both platforms to offer sage advice. Whether you’re focused on performance, flexibility, security or cost, I’ve structured this analysis around key selection criteria to match business needs.
Determining Your Requirements
Before diving into product capabilities, prioritizing your unique functional and technical imperatives is essential for filtering alternatives. Let‘s examine key elements to consider:
Workloads and Use Cases: What analytics workflows will the data warehouse support? Do you need real-time capabilities? At what scale of data and query complexity?
User Personas: Will business analysts conduct simple reporting or will data scientists build machine learning models? Understanding dominant personae guides capability tradeoffs.
In-House Skills: What database, analytics and cloud skill sets exist internally today? Options requiring extensive retraining carry greater change management needs.
Ecosystem Integration: Will you integrate analytics with operational systems like ERPs? Support for BI tools or notebooks? These influence product selection.
Data Gravity and Governance: Does IT want centralized platforms compliant with internal security, access and governance policies? Radically different hosting models exist between options.
Multi-Cloud Strategy: If adopting a multi-cloud posture, will you remain aligned to a single provider or support hybrid analytics? Important for longer-term flexibility.
Migration Approach: Are you replatforming legacy EDW solutions or greenfield building? Migration complexity varies greatly depending on starting point.
With your organization‘s specific priorities clarified, let‘s now contrast how Snowflake and Redshift compare on key evaluation criteria.
Snowflake Overview
Purpose-built for the cloud, Snowflake uses innovative architecture to decouple storage, compute and services for true elasticity. With extensive configurability, governance controls and broad ecosystem support, Snowflake rapidly became the industry‘s fastest-growing data warehouse.
Key Capabilities and Components
- Virtual warehouses for independent compute scaling
- Micro-partitioned storage for performance optimization
- Time travel for data correction and auditability
- Data sharing for direct queries across accounts
- Cloning for agile copy management
- BI partner connectors and data integration adapters
Redshift Overview
As AWS‘s petabyte-scale data warehouse solution, Redshift provides tight integration across analytics, data lakes and machine learning services. Clustered nodes empower performance fine-tuning for structured workloads.
Key Capabilities and Components
- Columnar storage with advanced compression
- Massively parallel processing (MPP) across nodes
- Result caching for repeated queries
- Workload management (WLM) for concurrency controls
- Integration with SageMaker, Quicksight and Glue
- Spectrum for direct S3 query access
Recommended Read: Top Cloud Data Warehouses
Comparing Performance and Scalability
For read-intensive workloads, few factors matter more than being able to sustain peak query concurrency with consistently fast response times as data volumes scale. Let‘s examine how Snowflake and Redshift architectures influence outcomes.
Observing public cloud data warehousing benchmarks reveals compelling advantages stemming from Snowflake‘s unique architecture:
- Up to 7x more queries before performance degraded (source)
- Sub-second response times for complex queries across petabytes (source)
- Zero tuning required for most demanding workloads (source)
- AWS themselves migrated from Redshift to Snowflake (source)
This real-world performance advantage stems from Snowflake‘s unique “virtual warehouse” consumption model that isolates resources to eliminate noisy neighbor issues plaguing shared architectures. Additionally, innovative micro-partitioning and optimized indexing ensure consistently blazing fast responsiveness even under intense loads.
However, Snowflake’s independent scale-out model forfeits some degree of centralized governance and performance fine-tuning available in clustered Redshift architectures. For organizations running complex queries across federated sources or scheduling ETL pipelines, Redshift may better optimize with fewer servers. But for general analytics, Snowflake’s ease of use reigns supreme.
Recommendations
Snowflake if you value elasticity, concurrency support and simplicity. Faster insights without tuning.
Redshift if you have SQL experts who can optimize complex pipelines. Tightly coupled to handle orchestration.
Architecting Agility and Flexibility
In lightning-fast business environments, having analytical systems adapt to evolving demands represents core value. Let‘s check how Snowflake and Redshift fare on key agility dimensions:
Infrastructure Agility – Both scale smoothly to handle usage spikes without over-provisioning but Snowflake‘s full separation of storage and compute eases growth forecasting for capacity planning teams with its usage-based model requiring zero idle resources.
Query Flexibility – Snowflake’s innovative virtual warehouses uniquely provide instant isolation beneficial for dynamic tenant loads. Redshift offers more advanced pipeline, queue and prioritization controls for expert DBAs.
Configurability – For on-premise experts accustomed to tuning, Redshift exposes more advanced configuration of nodes, clusters and memory. Snowflake radically simplifies most parameters for instant productivity.
Business User Empowerment – With extensive capabilities accessible through native UIs, Snowflake firmly targets business users coding less without IT bottlenecks. Redshift requires more scripting proficiency for equivalent functionality.
Maintenance Overhead – The fully-managed nature of Snowflake all but eliminates the patching, upgrades and routine maintenance DBAs wrestle with using Redshift. Automatic tuning further reduces oversight demands.
Across critical dimensions like dynamic scaling, insulation from noisy neighbors and reduced tuning, Snowflake’s cloud-native architecture shines for simplified agility. If your team values business user autonomy over SQL scripting, Snowflake surely satisfies. For legacy data warehousing teams with advanced tuning expertise, Redshift may offer deeper configurability.
Recommendations
Snowflake if you have dynamic loads and value business user self-service.
Redshift for workloads needing extensive, manual SQL tuning and management.
Data Integration and Ecosystem Support
Given snowballing diversity of inputs fueling modern data pipelines from apps, IoT sensors, clicks and more, assessing end-to-end integration capabilities proves paramount. Let‘s contrast key elements:
Ingestion Methods – Both offer extensive connectivity across files, streams, clouds and databases. Redshift preferable for expert ETL developers while Snowflake better serves business users.
Orchestration – Redshift natively schedules and monitors pipelines with console. Snowflake relies on external workflow orchestration platforms which can manage broader workflows.
Cloud Services – Redshift deeply integrates with the entirety of AWS analytics like QuickSight, Data Exchange and SageMaker. Snowflake instead priortizes multi-cloud flexibility.
3rd Party Partners – Via extensive co-innovation, Snowflake easily exchanges and enriches data acrossleading BI tools like Tableau, Databricks, and ThoughtSpot natively avoiding delays exporting data externally.
Hybrid Sources – Snowflake sharing capabilities uniquely federate queries in real-time across cloud and on-premise sources without data replication. Redshift Spectrum similarly queries data lake sources.
While Redshift represents the centerpiece of AWS analytics, Snowflake delivers extensive connectivity across the entire data ecosystem both cloud-based and on-premise. Unless your analytics adoption centers wholly within AWS, Snowflake’s versatility prevails. And innovations like data sharing radically transform what’s possible.
Recommendations
Snowflake for simplicity uniting cloud, on-premise and third-party sources.
Redshift if your analytics ecosystem resides exclusively on AWS services.
Also Read: Top ETL Tools for Data Integration
Securing Systems and Data
With data breaches constant front-page news, evaluating the security posture and access controls provided by each platform proves paramount before trusted ingestion begins. How do Snowflake and Redshift compare on key aspects?
IAM Controls – Both deliver granular identity and access management to authenticate approved users and restrict data access. Redshift natively integrates with AWS IAM while Snowflake supports SAML, OIDC and MFA standards.
Network Security – Redshift utilizes native VPC controls including security groups and private endpoints to protect cluster access. Snowflake instead relies on cloud key pair authentication, access control and micro-segmentation models.
Encryption – For encryption at rest, both utilize industry standards like AES-256 though Snowflake operates a default secure service minimizing risks of misconfiguration. Data in transit is similarly protected.
Certifications – Snowflake adheres to stringent standards including FedRAMP, HITRUST, PCI DSS, SOC 1/2/3 and ISO to satisfy security-conscious organizations in regulated industries. Redshift trails somewhat here.
Auditing – Both platforms track user activity for access pattern analysis but Snowflake uniquely offers immutable time travel capabilities to retrospectively analyze corrected records for troubleshooting.
With multiple layers securing infrastructure access, platform operations and in-database protections, both data warehouses satisfy security-minded CISOs. Slight advantages realized stem from Snowflake‘s superior compliance track record and innovative time travel for unprecedented auditability. But inheriting the battle-hardened AWS security portfolio also proves profoundly reliable.
Recommendations
Snowflake for failsafe security defaults and robust compliance.
Redshift if your security policies mandate isolation within an AWS VPC.
Evaluating Ongoing Costs
Given exploding data volumes whose storage and analysis costs quickly skyrocket, selecting cost-efficient solutions remains imperative. How do bundled pricing, discounts and optimizations contrast between options? Let‘s demystify key differences in cost:
Pricing Model – Core consumption for Snowflake and Redshift remains quite similar, charging per second for virtual warehouse compute used and per GB for cloud storage occupied. Key distinction resides in bundled pricing where reserved capacity and various enterprise discounts can significantly reduce Redshift effective price.
Data Lifecycle – For inactive data accessed infrequently, both platforms utilize object storage tiers at major cost savings ranging from 50-90% reduction. Archive compression, tier scheduling and automated policies determine significant savings.
Performance – As per benchmarks referenced earlier, Snowflake requires much lower compute provisioning to support equivalent query concurrency. Combined with innovations like data sharing bypassing replication, Snowflake drives pronounced TCO reductions.
Evaluating cost holistically – incorporating storage, daily operations and monthly fees plus vendor discounts – both solutions can provide tremendous economies versus legacy enterprise data warehouses both on a total cost and price-performance basis. Unless you plan to analyze over an exabyte and negotiate steep discounts around bulk capacity commitments, most organizations realize lower costs with Snowflake.
Recommendations
Snowflake for maximizing value from cloud elasticity and minimize wasted over-provisioning.
Redshift if you expect significant reserved instance discounts from bulk capacity purchases.
Also Read: Top Cloud Database Services
Recommended Use Cases
Now that we’ve dissected key technical elements, which product aligns better to specific analytics use cases? Below I suggest ideal scenarios to guide your selection.
Use Snowflake If You Need To:
- Radically simplify analytics with zero tuning
- Empower business users with self-service
- Unify real-time insights across cloud and on-prem sources
- Handle unpredictable, spiky query loads gracefully
- Centralize security policies across multi-cloud sources
- Satisfy stringent compliance regulations
Use Redshift If You Require:
- Tight integration managing AWS data and ML pipelines
- Fine-tune performance across workloads
- Customize with advanced SQL scripting capabilities
- Schedule and monitor complex data integrations
- Analyze over an exabyte with significant discounts
These recommendations capture the essence for matching dominant requirements with solution strengths. Of course, specific components may suit specialized needs as discussed.
Key Considerations for Migration and Multi-Cloud
If migrating from legacy enterprise data warehouses or desiring multi-cloud interoperability, additional factors affect selection. Specifically:
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Bulk Data Migration – For petabyte-scale data replication, AWS offers Database Migration Service (DMS) and Snowpipe natively transports data into Snowflake from S3. Both viable options.
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Live Sync – For continuous replication, triggers and change data capture better suit OLTP systems whereas periodic ELT scripts match batch/ELT workloads. Both should suffice for common CDC needs.
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Hybrid Queries – Snowflake’s zero-copy data sharing and query federation uniquely avoids delays and reconciliation by directly querying sources spanning cloud and on-prem. Redshift Federation similarly queries between AWS accounts and data lakes.
If migrating from legacy solutions or adopting multi-cloud practices, Snowflake’s flexibility latitudes typically simplify interoperability. With robust native data protection also built-in, both minimize risks during transition.
Conclusion and Key Recommendations
Evaluating between Snowflake and Redshift extensively, my core recommendations match technical requirements with solution strengths:
For Simplified Cloud Analytics free from tuning, Snowflake’s innovative architecture delivers on ease of use and concurrency. If empowering business users is paramount or you support functions like data science demanding flexibility, Snowflake offers the most seamless experience with elastic scale.
For Tightly Coupled AWS Analytics, Redshift integrates phenomenally with SageMaker, Quicksight and other services to uniquely optimize pipelines. If your organization standardized on AWS with skilled SQL users, Redshift aligns well.
For Unifying Hybrid Analytics, Snowflake data sharing and query flexibility is unmatched. Unique benefits sharing live data across cloud and on-prem sources make Snowflake perfect for decentralized enterprises and digital transformation.
With my years advising clients on finding analytics solutions matching unique needs, I hope examining key technical elements, ecosystem integrations and recommended use cases proves helpful guiding your selection as well between these two popular cloud data warehouse solutions. Please connect online if any outstanding questions remain unanswered.
