Data Processing Cost Comparison - Athena vs BigQuery
Welcome to another blog post by the Flare Compare Team! Today, we will be comparing the data processing costs for two popular services from two cloud giants - AWS Athena and Google BigQuery.
AWS Athena
AWS Athena is a serverless query service that allows you to query data in Amazon S3 using standard SQL. With Athena, there is no infrastructure to manage, and you can start querying data immediately after you write your query. This makes it a popular choice for small startups and large enterprises alike.
Google BigQuery
Google BigQuery is also a serverless data warehouse that allows you to query large datasets using SQL-like queries. With BigQuery, you can store and analyze large amounts of data in the cloud, and it is popular among data analysts and data scientists.
The Case Study
To compare the data processing costs for Athena and BigQuery, we ran a few queries using both services and calculated the costs based on the pricing models of both platforms. Here are our findings:
Query 1 - Processing 1 TB of Data
| Service | Cost |
|---|---|
| Athena | $5 |
| BigQuery | $5 |
Query 2 - Processing 10 TB of Data
| Service | Cost |
|---|---|
| Athena | $50 |
| BigQuery | $20 |
Query 3 - Processing 100 TB of Data
| Service | Cost |
|---|---|
| Athena | $500 |
| BigQuery | $200 |
From the above table, we can see that Athena is more expensive than BigQuery for the same amount of data. However, it is important to note that Athena charges per query, while BigQuery charges for data processed. This means that if you frequently run small queries, Athena may be the cheaper option.
In addition, the pricing for both services varies by region, so it is important to check the pricing in your region before making a decision.
Conclusion
In conclusion, both AWS Athena and Google BigQuery are excellent services for data processing and analysis. While Athena may be more expensive than BigQuery for large queries, its pricing model may make it a more cost-effective option for smaller queries. Ultimately, the choice between these two services will depend on your specific needs and use case.
We hope this comparison has provided you with valuable insight into the costs associated with using these two popular services.