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Data analytics and data management have become crucially important as digital transformation makes business ever more competitive. But with the volume of data to be analyzed rapidly rising, organizations need a way to corral all that data in one place, ripe for analysis. Enter modern cloud-based data warehouses such as Snowflake and AWS Redshift. Both are well-respected data warehousing platforms.
Both provide the volume, speed, and quality demanded by business intelligence and data analytics applications. But while there are many similarities between these data warehouse platforms, they each have a different orientation. Therefore, selection often boils down to platform preference and suitability for the organization’s data strategy.
Also see: Best Data Analytics Tools
Snowflake vs. Redshift: Comparing Key Features
Snowflake is a relational database management system and analytics data warehouse for structured and semi-structured data. Offered via the Software-as-a-Service (SaaS) model, it uses an SQL database engine to manage how information is stored in the database. It processes queries against virtual warehouses within the overall warehouse, each one in its own cluster node independent of others and not sharing compute resources.
Sitting on top of this are cloud services for authentication, infrastructure management, queries, access controls, and so on. The Snowflake Elastic Data Warehouse enables users to analyze and store data utilizing Amazon S3 or Azure resources.
AWS Redshift positions itself as a petabyte-scale data warehouse service that can be used by BI tools for analysis. Users can scale up and down easily. Like Snowflake, Amazon offers independent clusters to users. These clusters are also used for load balancing to enhance performance. It offers good query performance courtesy of high-bandwidth connections, close proximity to users due to the numerous Amazon data centers around the world, and tailored communication protocols. Due to the many services that exist within Amazon, users have easy access to reliable backups for their Redshift datasets.
Comparing the two data warehouses on features, Snowflake has more robust support for JSON-based functions as well as better database maintenance automation. Redshift, on the other hand, requires more hands-on maintenance work. Both provide columnar storage and massively parallel processing (MPP) for simultaneous analytics computations and fast querying even on huge datasets. Snowflake keeps compute, storage, and cloud services separate, though it offers concurrent scaling. Redshift has been playing catchup on such features and now isn’t far behind.
Overall, Snowflake wins on broad features.
Is Snowflake or Redshift Easier to Use?
The Snowflake data warehouse is said to be user-friendly with an intuitive SQL interface that makes it easy to get set up and running. Amazon Redshift, too, is said to be user-friendly and demands very little administration for everyday use.
If the user is already storing data on Amazon S3, then set up, integration, and query running are easy. Redshift also supports multiple data output formats, including JSON. Those with a background in SQL will find it easy to harness PostgreSQL to work with data.
Both data warehouse platforms offer online support, but Snowflake also provides 24/7 live support. Redshift is a little more complex and ties up more IT management on maintenance due to lack of automation compared to Snowflake, which automates data vacuuming, compression, diagnosis, and other features.
There is no need to copy data during scale up operations with Snowflake. Amazon does require some copying and other plumbing. Similarly on third party data sharing and accessing it to conduct analysis, Snowflake makes the entire process much easier. Snowflake supports structured and semi-structured while Redshift lacks support for semi-structured data types.
Snowflake wins in this category.
Snowflake vs. Redshift: Comparing Security
Redshift scores some key points on security and compliance. These features are enforced comprehensively for all users. Additionally, tools are available for access management, cluster encryption, security groups for clusters, data encryption in transit and at rest, SSL connection security, and sign-in credential security. Access rights are granular and can be very localized.
Thus, Redshift makes it easy to restrict inbound or outbound access to clusters. The network can also be isolated within a virtual private cloud (VPC) and linked to the IT infrastructure via a VPN.
Snowflake also boasts always-on encryption, along with network isolation, and other robust security features. But unlike Amazon, its security features come in tiers and each higher tier costs more. Yet on the plus side, you don’t end up paying for security features you don’t need or want.
AWS Redshift wins on security.
Snowflake vs. Redshift: Comparing Integration
Obviously, those already committed to the AWS platforms will find integration seamless on Redshift with services like Athena, DMS, DynamoDB, and CloudWatch.
Snowflake is on the AWS Marketplace but is not so embedded with the AWS ecosystem and lacks the vendor partnership depth and breadth that Amazon can muster. In some cases, it can be challenging to integrate Snowflake with other tools. But in other cases, Snowflake is highly integrated. Tableau, Apache Spark, IBM Cognos, and Qlik are all fully integrated. Those using these tools will find analysis easy to accomplish.
Integration: Redshift wins.
What is the Price Difference Between Snowflake and Redshift?
On-demand pricing is a feature of both products. But these two data warehouse platforms take a different approach to packaging.
Snowflake keeps compute and storage separate in its pricing structure. Redshift combines them. Snowflake provides concurrency scaling automatically with all editions at no extra cost. Redshift provides a dedicated amount of daily concurrency scaling. But you get charged by the second if it is exceeded.
Redshift’s long-term contracts come with big discounts. Customers can be charged an hourly rate (by type and cluster nodes) or by amount of byte scanning. Snowflake pricing is more complex with five different editions – from basic up – and prices rise as you move up the tiers.
Thus, the differences between them make it difficult to do a full apples-to-apples comparison. Users are advised to assess the resources they expect to need to support their forecast data volume, amount of processing, and their analysis requirements. For some users, Amazon will be cheaper, for others Snowflake will come out ahead.
Roughly speaking, Redshift costs about 25 cents per hour and Snowflake about $40 a month. But rate of usage will vary tremendously depending on the workload. Some users say Redshift is less expensive for on-demand pricing and that large data sets cost more on Snowflake due to its pricing for compute and storage separately.
This category is a close one as it varies from use case to use case. But Amazon Redshift gets the nod.
Snowflake vs. Redshift: Conclusion
Snowflake and Redshift are both excellent data warehouses for data analysis purposes. Each has its pros and cons. The comparison comes down to usage patterns, data volumes, workloads, and data strategies.
Amazon isn’t appropriate for transactional processing applications. If the data pattern means that there will constantly be byte scanning happening, pricing might get out of control. But pricing can also escalate on Snowflake when higher tiers become involved. If you need the highest level of functionality and security at the highest tier, Amazon may work out a better option.
Some say Snowflake is better when you are starting small and gradually scaling up. Redshift is said to be best for major enterprise-class implementations. But these are generalities and won’t always pan out. Each business needs to research how costs will work out for them.
For some, Redshift’s bundling of compute and storage will make it much cheaper. But the opposite might hold true for other workloads. In those cases, Snowflake’s ability to split compute and storage pricing may be best.
Another point of differentiation is JSON storage. Both support it but Snowflake offers more options. Those with a lot of JSON traffic and queries are better off on Snowflake.
And then there is the clout of Amazon. Yes, Snowflake runs on Amazon but heavy AWS users would be best on Redshift due to better integration with the entire Amazon ecosystem. Finally, Snowflake functions well with live app databases and Redshift does not. Ultimately, it is up to the user to determine by examining their workloads for suitability, then weighing which of these two fine data platforms will suit their data patterns best.