Introduction

Cloud-based data warehouses Snowflake and Redshift both offer a wide range of exciting alternatives for managing large data sets. Both Snowflake and Redshift have their similarities and differences, so let’s dive into them!

Snowflake

Using Snowflake’s data warehouse, you can analyze structured and layered data with ease. It is possible to develop scalable modern data architectures with maximum flexibility and little downtime using this SaaS (software-as-a-service). Using a SQL database engine simplifies understanding and use of the data warehouse. As a result, Snowflake allows you to use third-party services like Amazon S3 or Elastic Compute Cloud (EC2) instances to store data. Snowflake’s design is simple, quick, and adaptable because it makes use of a notion known as “virtual warehouse”. Using this virtual warehouse, you can establish numerous data warehouses on top of the database storage service. A query service layer sits above this virtual warehouse and maintains the architecture, query optimization, and safety of the virtual data warehouse. This design allows you to conduct a variety of tasks at the same time without influencing one another.

Snowflake Advantages

• It’s a cloud-based software service with an intuitive online interface.
• As it separates the computation from the storage, it allows users to scale up or down according to their needs, and charges accordingly.
• Microsoft Azure, Google Cloud Platform (GCP), and others can all be accessed via this multi-cloud platform.
• It has a self-maintenance feature.
• It can read and write JSON and other semi-structured data formats.

Snowflake Disadvantages

• Cloud computing is the primary mode of operation, and on-premises infrastructure is not supported.
• In most cases, Amazon Redshift will be less expensive.
• If you’re using an older model, it may not be up to snuff in terms of security compliance.
• Snowpipe, SnowSQL, Snowpark, and other tools are required to operate with Snowflake, making it difficult for non-technical users to interact with it.

Redshift

There are a number of data warehouse solutions offered by Amazon, including Redshift, which is meant to store and analyze enormous amounts of data in real-time for commercial purposes. Users may also implement Machine Learning capabilities into their Redshift clusters thanks to Redshift ML’s straightforward, safe and efficient interface with Amazon SageMaker. It has a columnar data format and a query layer that is compatible with PostgreSQL. By allowing customers to run SQL queries directly on Amazon S3 bucket data and supporting additional data types including JSON, Parquet, ORC, Avro, and other file formats using Amazon Redshift Spectrum, a feature of Amazon Redshift, users may execute faster and more complete analyses of their data. The Data warehouse capabilities of Amazon Redshift can be bolstered with the addition of Redshift Spectrum. Amazon Redshift’s integration with the AWS big data ecosystem is a notable feature. It’s a one-stop shop for creating data loading and processing pipelines using ETL. Additionally, it provides near real-time analytics with streaming data input and query optimization.

The architecture of Amazon Redshift is based on a shared-nothing model. Each compute node in this system has its own dedicated memory, disk space, and CPU. These nodes are grouped together by the service. When it comes to running queries and communicating with other cluster members, each node has a leader node that takes care of everything. Multiple databases can be built on a single cluster, and the architecture facilitates frequent inserts and updates. The ability to share data across several clusters is another feature of Redshift. It eliminates the need to duplicate data between clusters and databases, or even across various AWS accounts. In contrast to Snowflake, Amazon Redshift is better suited for high-performance applications. They also allow for the usage of other business intelligence tools, such as Excel spreadsheets. For those that need to execute complex queries on big amounts of data, Amazon Redshift provides a scalable and affordable solution. Amazon Redshift RA3 nodes come with managed storage, allowing you to scale and pay for computation and managed storage independently in order to optimize your data warehouse. RA3 lets you customize the number of nodes to meet your specific performance needs, and it only bills you for the managed storage you really use.

Redshift Advantages

• Coexistence with on-premises infrastructure is possible, as is tight integration with the rest of AWS.
• The on-demand pricing model is simple and straightforward, while the RI pricing model offers significant savings.
• Safe and reliable backup options are provided as well as enhanced security.
• For near-time and concurrent analyses, it speeds up query executions.
• It can output data in a variety of formats.
• ML integration, independent memory and computation with RA3 servers, AQUA, concurrent scalability free for one hour/day of use, variable loads with predictable prices, and more AWS capabilities are regularly added to make it the best, cost-controlled warehousing solution.

Redshift Disadvantages

• Amazon Redshift Spectrum comes at a premium price.
• Redshift on Amazon is now available in two different release cycles: the current maintenance track and the trailing maintenance track The user can select which track they want to follow, however, the default is the Current Maintenance Track.

Similarities

• Faster performance can be achieved by using Massive Parallel Processing (MPP).
• Column-oriented databases are used by both platforms to connect BI applications to databases.
• SQL query engines are used to access data in both warehouses.
• In order to make data-driven decisions and obtain insights, Snowflake and Redshift were built to separate data management activities.

Differences

In spite of the similarities, there are some important variances that we must address.

Performance

Regardless of the type of ongoing job, Snowflake or Amazon Redshift have distinct architectures and behave differently. As a result, comparing efficiency can be a bit of a thorn in the side. Snowflake and Amazon Redshift use columnar storage and huge parallel processing.. Concurrent computation in this design allows for advanced analytics and significant time savings on large queries. Amazon Redshift features machine learning capabilities in addition to concurrent scaling.

As for query execution time, the two services are quite different. Snowflake, on the other hand, is better at handling queries that aren’t optimized. Amazon Redshift’s research regarding time may be longer, but the query cache optimizes recurring requests. Amazon Redshift standardizes searches and data structure. Redshift’s ATO (Automatic Table Optimizations) automatically manages SORTKEY and DISTKEY to optimize queries and reduce runtime for JOIN and where queries. Redshift lets clients manually set these settings.

Maintenance

In the past, Snowflake had the benefit of automated upkeep. Amazon Redshift, on the other hand, necessitated some manual maintenance.

It was only afterward that Amazon Redshift unveiled its auto-vacuuming, automatic workload management queue (WLM), better queues utilizing machine learning (ML), and other features. As a result of this automation, Amazon Redshift maintenance has been considerably reduced.

Integrating the Ecosystem

Data collection can only be effective if firms are able to comprehend it. So, third-party analytic tools are required to deliver precise insights.

Third-party integration is supported by both Snowflake and Amazon Redshift. Amazon Redshift, on the other hand, has the most comprehensive ecosystem and third-party connections, including ETL and business intelligence tools, which gives it a clear advantage.

Costs

Using Snowflake, you only pay for what you use. This may be a preferable option if you have a small number of queries over a long period of time. When there is no query load, the cluster automatically shuts down and the service does not charge the user. 

Nevertheless, it’s difficult to estimate Snowflake’s true cost because of its complex tiering computational structure. There are seven layers of computational warehouses offered by Snowflake, which complicates the process of calculating the computing costs. Therefore, Snowflake may be more expensive in the majority of scenarios. Amazon Redshift, on the other hand, provides pricing that is clear and unambiguous. As an example, users can save up to 75% by committing to a certain amount of usage.

The following formula can also be used to calculate the price:

Cost of Amazon Redshift Monthly: [Price Per Hour] x [Cluster Size] x [Hours per Month].

It is also possible to purchase Amazon Redshift on-demand or as a Reserved Instance (RI). Compared to Snowflake’s on-demand pricing, Amazon Redshift is supposedly 1.3 times cheaper than Snowflake, and 1.9 to 3.7 times cheaper when booking servers for one or three years.

Security

Even in its data warehouse products, AWS has always sought to ensure the highest level of user protection. Snowflake takes a more haphazard approach to security than Amazon Redshift.

Snowflake provides VPC/VPN network separation and encryption. Security features vary by product edition, and the one you choose has an impact on the price.

Amazon Redshift, on the other hand, provides end-to-end encryption that can be customized to meet your security needs. Security solutions like VPC/VPN and SSL connections are also available to help you keep your data safe, as are additional security features such as access management and cluster encryption. There is no additional licensing cost or tier pricing for implementing security features in Redshift.

Separation of Storage System and Security System

Storage and computation are kept distinct in Snowflake, making it possible for users to increase or decrease their usage as needed.

Until now, Amazon Redshift has not provided a way to physically separate computing from storage. Adding new clusters for increased storage space or computational capacity is required because of this lack of isolation. By using R3 nodes, users can grow to compute and storage independently, enabling a scaling environment similar to Snowflake.

When you use Redshift Spectrum, you can run Database queries immediately on stored data in an S3 bucket, reducing the amount of data transit. AQUA (Advanced Query Accelerator) is included with Amazon Redshift Managed Storage with RA3 nodes at no additional charge. Using AQUA, Amazon Redshift is 10 times faster than other commercial cloud data warehouses because it boosts specific types of queries automatically.

Conclusion

In terms of data warehouses, Snowflake and Amazon Redshift are head-to-head competitors. The decision is based on your company’s needs and resources.

There are a number of reasons why Snowflake might be the best fit for your organization’s data storage needs.A high-query workload employing other AWS services, on the other hand, means that Amazon Redshift is the clear winner in this situation. Consider your requirements and available resources when comparing Snowflake vs Amazon Redshift. When you have the correct tool, you can begin to maximise your data’s value. Both of these widely used data warehouse systems can be used by Mission to help you develop a concrete data architecture.