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Amazon Athena vs AtScale: What are the differences?
Introduction:
When comparing Amazon Athena and AtScale, two prominent data analytics tools, there are key differences that set them apart in terms of functionality and use cases.
Data Source Integration: Amazon Athena is designed primarily for querying data stored in Amazon S3, providing a serverless interactive query service. In contrast, AtScale is a BI acceleration platform that creates a virtual cube on top of various data sources, including cloud data warehouses like Snowflake, Redshift, and BigQuery. This allows AtScale to provide a unified view of multiple data sources for faster querying and analytics.
User Interface: Amazon Athena offers a simple SQL-based interface for querying data in S3, making it more suitable for data engineers and analysts familiar with SQL. On the other hand, AtScale provides a business-user-friendly interface with drag-and-drop functionality and visualization capabilities, catering to a broader range of users including business analysts and data scientists.
Query Performance Optimization: While Amazon Athena optimizes query performance through partitions and columnar storage formats like Parquet, AtScale goes a step further by creating intelligent aggregates and cubes to accelerate query processing. This enables AtScale to handle complex queries more efficiently compared to Amazon Athena, especially when dealing with large datasets.
Security and Governance: Amazon Athena integrates with AWS Identity and Access Management (IAM) for access control, encryption, and data security within the AWS ecosystem. In contrast, AtScale provides advanced security features like role-based access control, data masking, and audit trails, making it more suitable for enterprise-grade security and governance requirements across different data sources.
Scalability and Workload Management: Amazon Athena scales automatically based on the demands of the queries and leverages the underlying AWS infrastructure for resource management. AtScale, on the other hand, offers workload management capabilities that allow administrators to prioritize and allocate resources for different queries and users, ensuring optimal performance and resource utilization in a multi-tenant environment.
Pricing Model: Amazon Athena follows a pay-as-you-go pricing model based on the amount of data scanned by queries, making it cost-effective for sporadic or ad-hoc querying. In comparison, AtScale's pricing is based on the number of users and data sources connected, making it more suitable for organizations that require a unified analytics layer across diverse data platforms with predictable costs.
In Summary, Amazon Athena and AtScale differ in terms of data source integration, user interface, query performance optimization, security and governance, scalability, workload management, and pricing model, catering to different use cases and user requirements.
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 AtScale
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