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Amazon Athena vs Apache Aurora: What are the differences?
Introduction:
Amazon Athena and Apache Aurora are two popular data processing and management tools used in the computing world. Both serve unique purposes and offer distinct features to meet various user needs.
Deployment and Use Case: Amazon Athena is a serverless interactive query service that enables users to analyze data in Amazon S3 using standard SQL. It is commonly used for ad-hoc querying and analysis of large amounts of data. On the other hand, Apache Aurora is a mesos framework for long-running services and cron jobs, ideal for service-oriented architectures and scalable applications.
Cost Structure: Amazon Athena follows a pay-as-you-go pricing model, where users are charged only for the amount of data scanned by their queries. In contrast, Apache Aurora is open-source software, meaning there are no licensing fees associated with its deployment. However, users may incur costs related to infrastructure and maintenance.
Scaling Capabilities: Amazon Athena is designed to automatically scale query processing based on the amount of data being scanned, allowing users to efficiently handle varying workloads. Apache Aurora, on the other hand, requires users to configure and manage the scaling of their services manually, providing greater control over resource allocation.
Data Source Integration: Amazon Athena is specifically optimized for querying data stored in Amazon S3, offering seamless integration with a wide range of AWS services. In comparison, Apache Aurora can be used with various storage systems and databases, providing more flexibility in data source connectivity.
Resource Management: Amazon Athena handles resource management internally, allowing users to focus solely on querying data without the need for infrastructure provisioning. Apache Aurora, on the other hand, requires users to configure resource allocation and scheduling for their services, requiring additional setup and maintenance.
Community Support and Development: Amazon Athena is a fully managed service by AWS, receiving continuous updates and enhancements from a dedicated development team. Apache Aurora, being open-source, benefits from a community of contributors and developers who actively improve the software and provide support to users.
In Summary, Amazon Athena and Apache Aurora differ in deployment use cases, cost structure, scaling capabilities, data source integration, resource management, and community support, catering to distinct user needs and preferences in the data processing and management domain.
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