Cloud Data Analysis: BigQuery vs. Athena – Cost and Performance Comparison
The competition between Google and AWS in the cloud industry has intensified, particularly in the realm of serverless querying tools. Two prominent offerings in this space are Amazon Athena and Google BigQuery. In this blog, we will closely examine these services and compare their real-world performance by executing a series of SQL queries on the same dataset.
Amazon Athena: Amazon Athena provides a serverless approach to querying data stored in Amazon S3 using SQL. It performs exceptionally well with datasets ranging from small to multiple petabytes in size. The cost of queries on Athena is calculated based on the amount of data scanned, starting at $5 per terabyte. Each query incurs a minimum charge of 10 MB.
Athena supports various data storage formats such as CSV, JSON, ORC, Parquet, and even Apache Weblogs format. It also allows the use of compressed CSV files in GZIP format, which not only reduces query costs but also enhances performance compared to uncompressed CSV files.
It’s important to understand that Athena is not a general-purpose database. It leverages Presto, a query execution engine built on top of the Hadoop stack, to process queries efficiently.
Google BigQuery: Google BigQuery offers a web user interface (UI) as well as a software development kit (SDK) for accessing its capabilities.
BigQuery enables users to query native tables within Google Cloud, external tables, or logical views. Data can be loaded into BigQuery storage through batch loads or real-time streaming. Supported data formats for loading into BigQuery include CSV, JSON, AVRO, and cloud datastore backups.
Now, let’s consider the performance comparison between these two services and evaluate their respective strengths and weaknesses in executing SQL queries on a shared dataset.
Comparing Pricing: Athena vs. BigQuery
When it comes to pricing, Athena and BigQuery both follow a $5 per terabyte queried structure. However, there are notable distinctions in how these services handle data compression and billing.
Athena charges based on the bytes read from S3, meaning that compressing your data can significantly reduce costs for both storage and queries. The Athena pricing documentation explicitly advises, “Compressing your data allows Athena to scan less data.” You can find more details on Athena pricing here.
On the other hand, BigQuery employs data compression behind the scenes, but users do not see this process. The crucial disparity lies in how storage and queries are billed by BigQuery. Rather than compressed bytes, BigQuery bills based on decompressed bytes, which can lead to a substantial difference in calculated data size. For more information on BigQuery pricing, you can visit the Google BigQuery Pricing page.
Considering these nuances, it becomes evident that understanding the impact of data compression and its relationship to pricing is crucial for evaluating the cost-effectiveness of Athena and BigQuery.
Results: Athena vs. BigQuery
Based on the comparison, it is evident that BigQuery outperforms Athena in terms of data processing speed. However, when considering the cost aspect, Athena proves to be a more economical and budget-friendly option.
In terms of performance, BigQuery emerges as the clear winner with its efficient data processing capabilities. It excels in handling large datasets and delivering faster results.
On the other hand, Athena offers a cost-effective solution. Its pricing structure, based on bytes read from S3, allows for potential savings by compressing data and reducing the amount of data scanned. This makes Athena a more affordable choice for those mindful of their budget.
Ultimately, the choice between Athena and BigQuery depends on your specific requirements, prioritizing either performance or cost-effectiveness. Evaluating the trade-offs and considering your specific use case will help determine which platform aligns best with your needs.
Conclusion:
In conclusion, BigQuery surpasses Athena in terms of query performance and the ability to handle large volumes of data. It offers fast query results, making it ideal for analyzing petabytes of data efficiently. On the other hand, Athena provides a simple and efficient solution for ad hoc querying of data stored in Amazon S3
Athena’s key advantage lies in its ease of use and minimal setup requirements. It is well-suited for simple and aggregated queries, making it a practical choice for certain use cases. Additionally, Athena is relatively cost-effective compared to BigQuery, especially for smaller-scale analysis.
When choosing between BigQuery and Athena, it is essential to consider the specific needs and priorities of your business. If you require high-performance analysis on large datasets, BigQuery is the recommended option. However, if you prefer a user-friendly and cost-effective solution for ad hoc queries, Athena may be more suitable.
Ultimately, the decision should be based on your unique requirements, data size, performance expectations, and budget considerations. Both BigQuery and Athena offer powerful capabilities for cloud data analysis, catering to different needs within the spectrum of serverless querying tools.