Overall Satisfaction with Amazon Athena
In my current organization, we use Amazon Athena for querying data from AWS S3 location. It provides faster access to data as compared to the traditional relational database management system. Also, it helps to work with complex data structures such as JSON, Parquet, CSV, and Avro. Earlier we were using some traditional RDBMS for reporting Ecommerce related KPIs which has lots of transactional data coming in. Performance was not much good for querying huge amount of real-time inventory data. So, we moved to Amazon Athena to support fast interactive querying of data and processing.
- Nested Schemas like JSON data structure
- Ability to adapt the data model to fit your queries better
- Performance Improvement
- Complex query optimization
- Limited performance on AWS S3
- Partioning and columnar format to maximize MPP
- Easy to query terabytes of data with faster response
- Pricing model is also cheap
- No indexing and partitioning
Amazon Redshift and EMR require explicit configuration for underlying compute infrastructure. In Amazon Athena, Users don't have to set up any underlying infrastructure. It saves a lot of costs required for infrastructure. Users have to pay only for scanned data. Athena is good for ad-hoc query analysis without setting up any infrastructure.
Do you think Amazon Athena delivers good value for the price?
Are you happy with Amazon Athena's feature set?
Did Amazon Athena live up to sales and marketing promises?
Did implementation of Amazon Athena go as expected?
Would you buy Amazon Athena again?
Best suited for analyzing huge amounts of data by just querying on Amazon Athena. Amazon Athena is also best to integrate with Amazon Quickight for visualization and reporting of data. Easy to work with CSV, JSON, and columnar data formats like Parquet, and ORC. Less appropriate to work with AVRO data format and also stored procedures are not supported in Amazon Athena. The size of a single row is also limited to 32 MB.