Introduction
Welcome to another exciting article from the Flare Compare Team! Today we are going to compare two modern and widely used data querying tools: AWS Athena and Google BigQuery. Both of these services are valuable in their own right, but which one is best suited for your data visualization needs? Let's find out!
Pricing
First, let's talk about the cost factor. As a data analyst, cost plays a critical role in choosing between the two options. Both AWS Athena and Google BigQuery have a pay-per-query model. However, Athena is slightly cheaper than BigQuery. Athena costs $5 per TB of the amount of data scanned, and BigQuery costs $5 per TB of queries processed. Depending on the type of queries and how often you run them, this could make a significant difference.
Performance
When it comes to performance, both of these services are exceptional. However, Athena is more suited for small to medium-sized datasets, while BigQuery has the upper hand in handling larger datasets. Athena is based on the Apache Presto engine, which works well with smaller datasets. BigQuery, on the other hand, utilizes a distributed query engine that can handle queries across massive datasets.
Query Syntax
Another significant factor is the difference in the query syntax. Athena uses the Presto query syntax, while BigQuery uses a proprietary SQL syntax. This may not seem like a significant factor, but if you are already familiar with SQL, BigQuery may be more intuitive for you.
Integration
Both Amazon Web Services and Google Cloud Platform offer a range of integrations with other tools. AWS Athena integrates seamlessly with AWS Glue, which is used to create, manage, and run ETL jobs. On the other hand, Google BigQuery integrates with Google Data Studio, which is a perfect tool for creating reports and dashboards. These integrations make it easier to create a complete data visualization ecosystem within either of the two platforms.
Conclusion
In summary, both AWS Athena and Google BigQuery have their pros and cons. However, when it comes to data visualization, BigQuery is better suited for organizations with massive datasets, complex queries, and high performance requirements. However, for small to medium-sized datasets, Athena is faster, more cost-efficient, and uses query syntax that is simple to understand.