Snowflake vs Redshift: Cloud Data Warehouse Comparison

Cloud data warehouses have gained quite a lot of attention these days. In the light of rapidly growing volume, variety, velocity, and veracity of data, more and more businesses and organizations are in the process of migrating their legacy database to the Cloud platform in order to be data-smart at decision-making. When it comes to selecting the best cloud warehouses, Snowflake and Amazon’s Redshift have been constantly compared, analysed and recommended. In this blog, we will discuss the similarities and differences between these two market leaders to help you pick the right one.

Architecture and Performance

Columnar storage and Massively Parallel Processing (MPP) are both the key selling points for Snowflake and Redshift. But how would your data engineer differentiate between these two platforms?

Have a look at the following high-level conceptual diagrams from Snowflake and Redshift. 

                                                                                                 Snowflake Architecture

                                                                                                Redshift Architecture

Snowflake’s unique 3-layer structure provides the benefits of simplicity and efficiency over Redshift’s. This architecture supports both structured and semi-structured data.

Connectivity

Once data is loaded into the warehouses, subsequent processes are, cleanse, model and visualize the data into actual insights. That is why the ability to connect the warehouses of your choice to the next step tools is critical. 

If the organization is already deploying AWS (SageMaker, CloudWatch, or Database Migration Service (DMS)) then Redshift fits right into the Amazon ecosystem. However, Snowflake has expanded its connectivity over the years and can offer connectors to Power BI, Qlik, Apache Spark, etc. It can also be integrated with many of the AWS services.

Maintenance and Usage

Snowflake, a Datawarehouse-as-a-Service model, requires no maintenance from its customers. Users do not need to manually start or shut off a warehouse once it is set up. Snowflake uses AUTO_SUSPEND and AUTO_RESUME functions to efficiently utilize the resources. On the other side, Redshift, Users require paying attention to the cluster and available resources.  This is because, Redshift requires manual workload management (WLM) to monitor the usage.

Based on the requirement, scaling up and down is possible with Snowflake.  If the workload increases as well, it can handle queries seamlessly. With Redshift, data scientists must be more strategic and thoughtful before they run the query.  This is because, Redshift does not scale up and down easily. In case of increased workloads, prior preparation must be performed so that the workload can be managed.
            

Security and Compliance

Snowflake takes security and compliance seriously and developed a series of reports. All five editions meet the SOC 2 Type II certification, Federated authentication and SSO for centralizing and streamlining User authentication, OAuth for authorizing account access without sharing or storing user login credentials. All editions have standard 1-day time travel for accessing or restoring modified, deleted data, and disaster recovery of modified or deleted data through Fail-safe.

For Snowflake’s business critical tier accounts, there are more features to ensure that data is handled with extra care. For instance, Business Critical accounts allow customer-managed encryption keys through Tri-Secret Secure, support PHI data in accordance with HIPAA, as well as PCI DSS. 

Redshift takes a holistic approach to security and compliance. Amazon allows third-party auditors to assess the security and compliance, including SOC, PCI, FedRAMP and HIPAA. Redshift also allows Users to isolate the network within a virtual private cloud (VPC) and connect to the IT infrastructure through a virtual private network (VPN).

Cost

Snowflake offers tier-based, on-demand pricing.  This means that customers start with choosing the tiered package that best suits their needs, and subsequently receive bills (compute and storage usages are separated). 

Redshift’s pricing, on the other hand, is a bundled deal. Compute and storage are bundled together, and customers pay for an hourly rate, based on the number of nodes in the cluster. With Redshift, companies might find a better price over the long-term deal (1 or 3-year contract) whereas Snowflake provides the flexibility to switch to the next tier edition if customers require more storage or compute. 

Conclusion 

To sum up the comparison between Snowflake and Redshift, we recommend you consider the architecture, performance, connectivity, maintenance, usage, security and compliance, as well as the cost of both. Regardless of the business vertical, it is important that you migrate the legacy system in place, into a more powerful, durable and fast cloud environment.

Both Snowflake and Redshift are serious contenders if you want to migrate your data in into Cloud. Ameex is a solutions partner of Snowflake and has certified developers (in both Snowflake and AWS).

To know more on how Ameex has helped in providing data warehousing solution to many leading clients across industry verticals, write to us at analytics@ameexusa.com.