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Amazon Athena vs Amazon Quicksight: 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 Quicksight is detailed as "Fast, easy to use business analytics at 1/10th the cost of traditional BI solutions". Amazon QuickSight is a fast, cloud-powered business analytics service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data.
Amazon Athena and Amazon Quicksight are primarily classified as "Big Data" and "Business Intelligence" tools respectively.
Auto Trader, Zola, and Twilio SendGrid are some of the popular companies that use Amazon Athena, whereas Amazon Quicksight is used by Haymarket Media Asia, MPOWER Financing, and Kobalt Music. Amazon Athena has a broader approval, being mentioned in 68 company stacks & 60 developers stacks; compared to Amazon Quicksight, which is listed in 13 company stacks and 19 developer stacks.
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
- 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 Quicksight
- Dataset versionning1
- Good integration with aws Glue ETL services1
- More features (table calculations, functions, insights)1
- Better integration with aws1
- Super cheap1
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Cons of Amazon Athena
Cons of Amazon Quicksight
- Very basic BI tool1
- Only works in AWS environments (not GCP, Azure)1