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Amazon Athena vs Amazon RDS for Aurora: What are the differences?
Developers describe Amazon Athena as "Query S3 Using SQL". Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. On the other hand, Amazon RDS for Aurora is detailed as "MySQL and PostgreSQL compatible relational database with several times better performance". Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability.
Amazon Athena can be classified as a tool in the "Big Data Tools" category, while Amazon RDS for Aurora is grouped under "SQL Database as a Service".
"Use SQL to analyze CSV files" is the primary reason why developers consider Amazon Athena over the competitors, whereas "MySQL compatibility " was stated as the key factor in picking Amazon RDS for Aurora.
Medium, StackShare, and Zumba are some of the popular companies that use Amazon RDS for Aurora, whereas Amazon Athena is used by Auto Trader, Zola, and Twilio SendGrid. Amazon RDS for Aurora has a broader approval, being mentioned in 121 company stacks & 31 developers stacks; compared to Amazon Athena, which is listed in 50 company stacks and 18 developer stacks.
Hi all,
Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?
you can use aws glue service to convert you pipe format data to parquet format , and thus you can achieve data compression . Now you should choose Redshift to copy your data as it is very huge. To manage your data, you should partition your data in S3 bucket and also divide your data across the redshift cluster

First of all you should make your choice upon Redshift or Athena based on your use case since they are two very diferent services - Redshift is an enterprise-grade MPP Data Warehouse while Athena is a SQL layer on top of S3 with limited performance. If performance is a key factor, users are going to execute unpredictable queries and direct and managing costs are not a problem I'd definitely go for Redshift. If performance is not so critical and queries will be predictable somewhat I'd go for Athena.
Once you select the technology you'll need to optimize your data in order to get the queries executed as fast as possible. In both cases you may need to adapt the data model to fit your queries better. In the case you go for Athena you'd also proabably need to change your file format to Parquet or Avro and review your partition strategy depending on your most frequent type of query. If you choose Redshift you'll need to ingest the data from your files into it and maybe carry out some tuning tasks for performance gain.
I'll recommend Redshift for now since it can address a wider range of use cases, but we could give you better advice if you described your use case in depth.

It depend of the nature of your data (structured or not?) and of course your queries (ad-hoc or predictible?). For example you can look at partitioning and columnar format to maximize MPP capabilities for both Athena and Redshift
you can change your PSV fomat data to parquet file format with AWS GLUE and then your query performance will be improved
Pros of Amazon Athena
- Use SQL to analyze CSV files15
- Glue crawlers gives easy Data catalogue8
- Cheap7
- Query all my data without running servers 24x75
- No data base servers yay4
- Easy integration with QuickSight3
- Query and analyse CSV,parquet,json files in sql2
- Also glue and athena use same data catalog2
- No configuration required1
- Ad hoc checks on data made easy0
Pros of Amazon Aurora
- MySQL compatibility14
- Better performance12
- Easy read scalability10
- Speed8
- Low latency read replica7
- High IOPS cost2
- Good cost performance1
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Cons of Amazon Athena
Cons of Amazon Aurora
- Vendor locking2
- Rigid schema1