Need advice about which tool to choose?Ask the StackShare community!
Amazon Athena vs Azure Synapse: What are the differences?
Introduction
In this article, we will discuss the key differences between Amazon Athena and Azure Synapse, two popular cloud-based analytics services. Markdown code will be used to format the content for a website.
Integration with Cloud Ecosystem: Amazon Athena is part of the Amazon Web Services (AWS) ecosystem, while Azure Synapse is part of the Microsoft Azure ecosystem. This means that Athena is tightly integrated with other AWS services like S3 for data storage and AWS Glue for data cataloging. On the other hand, Azure Synapse seamlessly integrates with other Azure services such as Azure Data Lake Storage and Azure SQL Data Warehouse, providing a unified experience within the Azure environment.
Query Execution Engine: Athena uses Presto, an open-source distributed SQL query engine, for executing queries on data stored in Amazon S3. Synapse, on the other hand, utilizes a combination of massively parallel processing (MPP) architecture through an optimized version of SQL Server and Apache Spark for processing big data workloads. This difference in query execution engines may lead to variations in performance and query capabilities.
Data Warehousing Capability: Azure Synapse is primarily designed as a data warehousing solution, providing features like columnar storage, data optimization, and workload management. It offers integration with other Azure services like Azure Machine Learning and Power BI, facilitating a comprehensive analytics and data exploration experience. In contrast, Amazon Athena focuses on providing ad-hoc query capabilities on data stored in S3, without specifically targeting data warehousing functionalities.
Pricing Model: Both Amazon Athena and Azure Synapse have different pricing models. Athena follows a pay-per-query pricing model, where users are billed based on the amount of data scanned by each query. This can be cost-effective for sporadic querying but may become expensive for heavy workloads. Azure Synapse, on the other hand, offers different pricing tiers based on the size and performance level of the provisioned resources, allowing users to tailor their pricing based on their specific requirements.
Data Integration and ETL: Azure Synapse provides built-in data integration and ETL (extract, transform, load) capabilities through the Azure Data Factory, enabling seamless data movement and transformation between various data sources. Amazon Athena, on the other hand, relies on AWS Glue for data cataloging and integration tasks, requiring additional configuration and setup for managing data pipelines.
Scalability and Workload Management: Scaling capabilities differ between Amazon Athena and Azure Synapse. Athena automatically scales query execution based on the input data size, optimizing performance as the workload increases. Azure Synapse offers both serverless and provisioned options, allowing users to scale performance and resources based on their workload demands. It provides more fine-grained control over scaling and workload management compared to Athena.
In summary, Amazon Athena is tightly integrated with the AWS ecosystem, focuses on ad-hoc querying on S3 data, and follows a pay-per-query pricing model. Azure Synapse, part of the Azure ecosystem, targets data warehousing with comprehensive analytics capabilities, offers data integration and ETL functionalities, and provides flexibility in scaling and workload management.
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 Azure Synapse
- ETL4
- Security3
- Serverless2
- Doesn't support cross database query1
Sign up to add or upvote prosMake informed product decisions
Cons of Amazon Athena
Cons of Azure Synapse
- Dictionary Size Limitation - CCI1
- Concurrency1
Sign up to add or upvote consMake informed product decisions
What is Amazon Athena?
What is Azure Synapse?
Need advice about which tool to choose?Ask the StackShare community!
What companies use Azure Synapse?
Sign up to get full access to all the companiesMake informed product decisions
What tools integrate with Azure Synapse?
Sign up to get full access to all the tool integrationsMake informed product decisions
Blog Posts
+16
+25