AWS Athena vs Snowflake

The High Level Overview

Snowflake and Amazon Athena are both cloud analytics tools, but are significantly different in terms of their architecture. Athena is a serverless query engine based on open-source Presto technology, which uses Amazon S3 as the storage layer; whereas Snowflake is a cloud data warehouse that stores data in a proprietary format, although it utilizes cloud storage to provide elasticity. An alternative to these offerings is Ahana Cloud, a managed service for Presto.

Snowflake would more often be considered as an alternative to Redshift or other cloud data warehouse technologies – typically used for situations where workloads are predictable, or where organizations are willing to pay a premium to provide very fast query performance. Storing large volumes of semi-structured data in data warehouses will typically be expensive, and in these cases many organizations would consider a serverless alternative such as Ahana or Athena.

What is Snowflake?

Snowflake is a cloud-based data warehouse that provides a SQL interface for querying, loading, and analyzing data. It also provides tools for data sharing, security, and governance.		What is Amazon Athena?

Amazon Athena is a serverless, interactive query service that makes it easy to analyze data stored in Amazon S3 using standard SQL.		What is Ahana Cloud?

Ahana Cloud is a managed service for Presto on AWS that gives you more control over your deployment. Typically users see up to 5x better price performance as compared to Athena.

Try Ahana for Superior Price-Performance

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Performance

We are defining performance as the ability to maintain fast query response times, and whether doing so requires a lot of manual optimization.

According to the vendor:

Below is a summary of the claims made in each vendor’s promotional materials related to their products’ performance. 

Snowflake

The Snowflake website claims that Snowflake’s multi-cluster resource isolation ensures reliable, fast performance for both ad-hoc and batch workloads; and that this performance is ensured even when working at larger scale.		Athena

The AWS website mentions that Athena is optimized for fast performance with Amazon S3 and automatically executes queries in parallel for quick results, even on large datasets. 		Ahana

Ahana has multi-level data lake caching that can give customers up to 30X query performance improvements. Ahana is also known for its better price-performance as compared to Athena especially.

According to user reviews:

Below is a summary of the claims made on user reviews in websites such as G2, Reddit, and Stack Overflow, related to each tool’s performance. Users generally have positive opinions about Snowflake’s performance, but note the high cost, and have generally positive opinions about Athena’s performance, but note potential performance issues and inability to scale the service.

Snowflake

– Many reviewers have generally positive opinions about Snowflake’s performance – although it’s clear from the reviews that this performance comes at a high cost. They mention positive aspects such as its ability to handle multiple users at once, instantaneous cluster scalability, fast query performance, and automatic compute scaling

– Negative aspects mentioned include credit limits, expensive pricing for real-time use cases or large queries, cost of compute, time required to learn Snowflake’s scaling, and missing developer features.		Athena

– Many reviewers see Athena as fast and reliable, and capable of handling large volumes of data. 

– Negative aspects mentioned include Athena not supporting stored procedures, the possibility of performance issues if too many partitions are used, concurrency issues, inability to scale the service, and the need to optimize queries and data.		Ahana

Ahana is similar to Athena in that you get fast and reliable data analytics at scale. Unlike Athena, you get more control over your Presto deployment – no issues with concurrency or deterministic performance.

Scale

We are defining scale as how effectively a data tool can handle larger volumes of data and whether it is a good fit for more advanced use cases.

According to the vendor:

Below is a summary of the claims made in each vendor’s promotional materials related to their products’ scale. 

Snowflake

The Snowflake website claims that Snowflake can instantly and cost-efficiently scale to handle virtually any number of concurrent users and workloads, without impacting performance; an that Snowflake is built for high availability and high reliability, and designed to support effortless data management, security, governance, availability, and data resiliency.		Athena

The AWS website claims that Athena automatically executes queries in parallel, so results are fast, even with large datasets and complex queries. Athena is also highly available and executes queries using compute resources across multiple facilities, automatically routing queries appropriately if a particular facility is unreachable. 		Ahana

Ahana has autoscaling built-in which automatically adjusts the number of worker nodes in an Ahana-managed Presto cluster. This allows for efficient performance and also helps to avoid excess costs.

According to user reviews:

Below is a summary of the claims made on user reviews in websites such as G2, Reddit, and Stack Overflow, related to each tool’s scale. Users note potential limitations in certain features for both tools, although both are capable of querying large datasets.

Snowflake

– Reviewers note that Snowflake is capable of handling larger volumes of data. They also mention that it has features such as cluster scalability, flexible pricing models, and integrations with third-party tools that can help with scaling. 

– However, some reviewers also mention potential limitations such as the lack of full functionality for unstructured data, the difficulty of pricing out the product, and the lack of command line tools for integration.		Athena

– Some reviews suggest that Athena is well-suited for larger volumes of data and more advanced use cases, with features such as data transfer speed and integration with Glue being mentioned positively.

– However, other reviews suggest that Athena may not be able to handle larger volumes of data effectively due to issues such as lack of feature parity with Presto, lack of standard relational table type, and difficulty in debugging queries.

Usability, Ease of Use and Configuration

We define usability as whether a software tool is simple to install and operate, and how much effort users need to invest a lot of effort in order to accomplish their tasks. We assume that data tools that use familiar languages and syntaxes such as SQL are easier to use than tools that require specialized knowledge.

According to the vendor:

Below is a summary of the claims made in each vendor’s promotional materials related to their products’ ease of use. 

Snowflake

The Snowflake website claims that Snowflake is a fully managed service, which can help users automate infrastructure-related tasks; and that Snowflake provides robust SQL support and the Snowpark developer framework for Python, Java, and Scala, allowing customers to work with data in multiple ways.		Athena

The AWS website claims that Athena requires no infrastructure or administration setup. Athena is built on Presto, so users can run queries against large datasets in Amazon S3 using ANSI SQL.		Ahana

Ahana is a managed service which means you get more control over your deployment than you would with Athena, but it also takes care of the configuration parameters under the hood.

According to user reviews:

Below is a summary of the claims made on user reviews in websites such as G2, Reddit, and Stack Overflow, related to each tool’s usability. Users generally have positive opinions about Snowflake’s ease of use and configuration, while they are happy with the ease of deploying Athena in their AWS account, but mention drawbacks such as lack of support for stored procedures and unclear error messages when debugging queries.

Snowflake

– Reviewers have mostly positive opinions about Snowflake’s ease of use and configuration. Several mention that Snowflake is easy to deploy, configure, and use, with many online training options available and no infrastructure maintenance required. 

– On the negative side, some reviews mention that there are too many tiers with their own credit limits, making it economically non-viable, and that the GUI for SQL Worksheets (Classic as well as Snowsight) could be improved. Additionally, some reviews mention that troubleshooting error messages and missing documentation can be challenging, and that they would like to see better POSIX support.		Athena

– Reviewers are happy with the ease of deploying Athena in their AWS account, and mention that setting up tables, views and writing queries is simple.

– However, some reviews also mention drawbacks such as the lack of support for stored procedures, and the lack of feature parity between Athena and Presto. Another issue that comes up is that debugging queries can be difficult due to unclear error messages.

Cost

  • Athena charges a flat price of $5 per terabyte of data scanned. Costs can be reduced by compressing and partitioning data.
  • Snowflake is priced based on two consumption-based metrics: usage of compute and of data storage, with different tiers available. Storage costs begin at a flat rate of $23 USD per compressed TB of data stored, while compute costs are $0.00056 per second for each credit consumed on Snowflake Standard Edition, and $0.0011 per second for each credit consumed on Business Critical Edition. 
  • Ahana is pay-as-you-go pricing based on your consumption. There’s a pricing calculator if you want to see what your deployment model would cost.

As we can see, Snowflake follows data warehouse pricing models, where users pay both for storage and compute. A recurring theme in many of the reviews is that costs are hard to control, especially for real-time or big data use cases. Athena’s pricing structure is simpler and based entirely on the amount of data queried, although it can increase significantly if the source S3 data is not optimized.

Need a better alternative?

Get a demo of Ahana to learn how we deliver superior price/performance, control and usability for your data lake and lakehouse architecture. Ahana gives you SQL on S3 with better price performance than Athena and no vendor-lock in as compared to Snowflake.

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