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Amazon Athena vs Snowflake: What are the differences?
- Data Warehouse Architecture: Amazon Athena is a serverless and scalable query service that allows querying data directly from Amazon S3, without the need for infrastructure management. On the other hand, Snowflake is a cloud-based data warehouse platform that provides a multi-cluster shared data architecture, enabling query processing across multiple nodes for better performance.
- Data Processing: Athena supports querying structured, semi-structured, and unstructured data using a SQL-like query language. It provides fast and linearly scalable queries with automatic scaling of compute resources. Snowflake, on the other hand, supports both structured and semi-structured data, with advanced data processing capabilities like semi-structured data optimization, result caching, and automatic query optimization.
- Concurrency: Athena offers high concurrency with a limit of 20 queries per account, while Snowflake provides higher concurrency with up to 2000 queries per account.
- Data Partitioning: Athena partitions data based on the underlying folder structure in Amazon S3, allowing for efficient querying of large datasets. Snowflake enables partitioning based on user-defined columns, which provides more flexibility in managing and optimizing data partitions.
- Pricing Model: Athena follows a pay-per-query pricing model, where users only pay for the queries they execute. Snowflake, on the other hand, offers different pricing models, including on-demand and prepaid options based on compute and storage usage.
- Data Integration: Amazon Athena integrates seamlessly with other AWS services, such as AWS Glue for data cataloging and AWS Lambda for automation. Snowflake also offers various integrations and connectors with popular data integration platforms, enabling easy data ingestion and integration workflows.
In Summary, Amazon Athena is a serverless and scalable query service designed for querying data directly from Amazon S3, while Snowflake is a cloud-based data warehouse platform with advanced data processing capabilities and a multi-cluster shared data architecture.
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 Snowflake
- Public and Private Data Sharing7
- Multicloud4
- Good Performance4
- User Friendly4
- Great Documentation3
- Serverless2
- Economical1
- Usage based billing1
- Innovative1
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