Introducing the Data Warehouse Heavyweights
The world of data warehousing is dominated by two heavy-hitting platforms – Snowflake and Amazon Web Services (AWS). As cloud-based solutions, both offer scalable and flexible data storage, processing, and analytics capabilities that were previously unattainable for many organizations.
But when it comes down to choosing between Snowflake and AWS, there are key factors technologists must weigh up. The two solutions take quite divergent approaches, which suit different use cases better.
As a data engineer with over a decade‘s experience deploying data warehouses, I‘ve had extensive hands-on exposure to both Snowflake and AWS. Here I‘ll provide a technical deep dive into how the platforms compare on architecture, performance, security, ecosystem, and more – as well as tips on when each solution excels.
A Quick History
Let‘s first understand the background of each company…
Snowflake
Founded 2012 by data warehouse veterans from Oracle, Vectorwise.
Official product launched 2015. Steady growth since, with 3400+ customers.
Achieved breakout success in 2020 with extremely successful IPO.
Recognized as industry leader in cloud data warehousing.
AWS
Cloud arm of Amazon launched in 2006.
Pioneer and dominant force in cloud computing market.
Offers over 200 cloud services including storage, networking, databases, analytics.
Highly diversified customer base from startups to enterprises.
Clear market leader – accounts for over 30% of global cloud infrastructure market.
Architectural Differences
The underlying infrastructure of Snowflake and AWS may seem invisible to the end user, but it has major implications on functionality, flexibility and performance.
Snowflake
Unique hybrid architecture combining cloud and SQL database engine. Separates storage, compute and cloud services layers.
Benefits:
- Flexible scaling of storage and compute independently
- Faster query processing via parallel task execution
- Semi-structured and structured data support
AWS
Distributed "shared-nothing" architecture. Logically isolates data into shards/nodes for parallel processing.
Benefits:
- Enhances fault tolerance and availability
- Facilitates horizontal scaling
The choice depends on your workload patterns – if fast ad-hoc analytical queries are critical, Snowflake may be better suited. AWS provides more granular control for ETL pipelines and workloads relying on clustered databases like Redshift.
Security – How Safe is My Data?
For any data solution, security is paramount. Both platforms incorporate robust mechanisms to protect customer data, but key philosophical differences exist.
Snowflake
Encryption for all data at rest and in transit. Complies with industry standards like HIPAA, PCI DSS, FedRAMP.
Security handled internally by Snowflake. Customers benefit from baked-in protection.
AWS
Offers tools and features for encryption, access controls, auditing, but customers responsible for implementation.
Complies with security standards, but the burden is on you to enable controls.
Snowflake‘s security posture is stronger out of the box. For regulated industries like healthcare and finance, it reduces compliance burden. AWS offers more flexibility – you assume more responsibility in defining security policies on your terms.
Performance & Scalability
For data platforms, the numbers that matter are speed and size. How fast can you analyze growing data volumes?
Snowflake
Unique architecture accelerates complex analytical workloads. Separate storage from compute allows independent scaling.
Customers have near-limitless scale available.
AWS
Data warehousing services like Redshift offer high performance at petabyte scale. Can configure instances to balance between cost and speed.
Unstructured data workloads slower unless data lake used. Overall flexibility in sizing clusters.
Both deliver impressive specs. Snowflake has the edge for bigger/broader data given independent resource scaling. AWS allows fine-tuning instance types to workloads. For extremely large or complex analysis – best to benchmark with actual data.
Ecosystem Integrations & Support
Beyond the core platform, the richness of integrations and partnerships is key for a robust data solution.
Snowflake
Partnered with leading ETL, BI and data tools vendors – so plugs seamlessly into modern data stack.
Growing marketplace of 3rd-party add-ons and connectors.
AWS
Fully integrated with AWS analytics services (Quicksight, Athena), data pipelines (Glue, Kinesis), S3 storage.
Largest ecosystem of apps and services being cloud provider.
As standalone data warehouse, Snowflake has best-of-breed integrations. For broader use cases, AWS provides a "one-stop shop" linking storage, ETL, analysis, and more. Vendor lock-in can be issue with AWS long-term.
Workload Suitability – Use Cases Where Each Excels
We‘ve covered the technical nitty-gritty. Where do Snowflake and AWS each shine when it comes to real-world usage?
Snowflake Best For…
- Cloud-first enterprises lacking on-prem infrastructure
- Advanced analytical workloads and ad-hoc querying
- Regulated industries requiring stringent security
- Dynamic teams where speed and simplicity are critical
AWS Best For…
- Batch and ETL-centric workloads
- Companies already using AWS services extensively
- Custom tuning of data warehouse clusters
- Low-latency querying needs over unstructured data
The choice mainly hinges on your technical environment, workload patterns and governance needs. Of course, cost is always a factor too – where case-by-case benchmarking should happen.
The Final Verdict
So there we have it – a comprehensive technical comparison of Snowflake vs AWS for data warehousing.
When it comes to simplicity, out-of-box security and query performance – Snowflake is ahead. AWS provides more granular control and existing integration benefits for the heavily AWS-invested.
Ultimately there‘s no vendor "lock-in" here – you can even run Snowflake securely on AWS infrastructure. As always, the recommendation is to trial each platform with your actual data and workloads. Only then does the best choice become clear for your needs.
