AWS Athena VS Google BigQuery – Comparison Table
Aspect |
AWS Athena |
Google BigQuery |
---|---|---|
Ease of Use |
SQL-like queries, but requires knowledge of AWS services |
SQL-like queries, user-friendly interface |
Flexibility |
Limited to querying data in S3 |
Can query data in Google Cloud Storage, Google Drive, Bigtable, Sheets, etc. |
Scalability |
Automatically scales based on query complexity |
Automatically scales based on query complexity |
Security |
Uses AWS IAM for access control |
Uses Google Cloud IAM for access control |
Performance |
Good for ad-hoc queries, can be slower for complex queries |
Optimized for speed, especially for large datasets |
Community |
Large community, many resources available |
Large community, many resources available |
Cost |
Pay per query and data scanned |
Pay for storage and query processing |
Customization |
Limited customization options |
More customization options available |
Content Management |
Does not provide content management features |
Does not provide content management features |
Updates and Maintenance |
Managed by AWS, regular updates |
Managed by Google, regular updates |
AWS Athena vs Google BigQuery: A Comparison of Data Analysis Services
In recent times, many companies have preferred serverless data storage. The reason behind this is because of the many advantages it poses regarding operational costs, and ease of management within a company among other things. Google and Amazon have developed two similar products that are part of their great service delivery in the serverless operation space which include: Google BigQuery and Amazon Athena.
Despite being good tools for data analysis, each has its pros and cons. This article will therefore give an overview of what these two services are about as well as compare them against each other in terms of functionality.