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Amazon Athena vs Amazon RDS for Aurora: What are the differences?
Amazon Athena is a serverless interactive query service to analyze data in Amazon S3 using standard SQL while Amazon RDS for Aurora is a fully managed relational database service compatible with MySQL and PostgreSQL. Let's explore the key differences between them:
Architecture: Amazon Athena is a serverless query service that allows you to run SQL queries on data stored in Amazon S3. It leverages Presto, an open-source distributed SQL engine, to execute queries on data files directly from S3 without requiring any infrastructure provisioning. On the other hand, Amazon RDS for Aurora is a managed relational database service that is compatible with MySQL and PostgreSQL. It provides a traditional database management system with features such as data storage, transaction management, and advanced querying capabilities.
Querying: Amazon Athena is a serverless query service that enables ad-hoc querying and analysis of data stored in Amazon S3 using standard SQL queries. On the other hand, Amazon RDS for Aurora is a fully managed relational database service that provides a traditional SQL interface for querying and managing structured data.
Data Storage: Amazon Athena does not require you to load data into a separate database. It directly queries data stored in Amazon S3, which allows for flexible and cost-effective storage of large datasets. Amazon RDS for Aurora, however, requires you to load and manage your data within the Aurora database engine, which provides optimized storage and indexing capabilities.
Scalability and Management: Amazon Athena automatically scales resources to handle query workloads and does not require any infrastructure management. It is a serverless service where you pay for the queries you run. Amazon RDS for Aurora, on the other hand, provides a scalable and managed relational database environment that takes care of infrastructure provisioning, scaling, and backups, but requires more management overhead compared to Athena.
In summary, Amazon Athena is a serverless query service for analyzing large datasets stored in Amazon S3, offering automatic scaling and flexible schema-less querying. Amazon RDS for Aurora is a managed relational database service optimized for high-performance transactional workloads, providing strong consistency and durability with customizable scaling options.
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 files16
- Glue crawlers gives easy Data catalogue8
- Cheap7
- Query all my data without running servers 24x76
- 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
- Speed9
- 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
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What is Amazon Athena?
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