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Amazon Athena vs Azure Storage: What are the differences?
Introduction
In this article, we will discuss the key differences between Amazon Athena and Azure Storage. Both Amazon Athena and Azure Storage are popular cloud-based storage and analytics services. While they offer similar features, they have some distinct differences that make them suitable for different use cases. Let's explore these differences in more detail below.
Data Processing Approach: One of the key differences between Amazon Athena and Azure Storage is their data processing approach. Amazon Athena follows a serverless query engine design that allows users to run interactive SQL queries directly on their data stored in Amazon S3. On the other hand, Azure Storage provides data storage and retrieval capabilities but does not offer a built-in serverless query engine like Athena. Therefore, to process and analyze data stored in Azure Storage, users need to use additional services like Azure Data Lake Analytics or Azure Databricks.
Integration with Ecosystem: Another significant difference is the integration of these services with their respective cloud ecosystems. Amazon Athena is tightly integrated with the Amazon Web Services (AWS) ecosystem, which means it seamlessly works with other AWS services like AWS Glue, AWS Lambda, and Amazon Redshift. This integration allows for a more seamless data pipeline and leverages the capabilities of other AWS services. In contrast, Azure Storage is part of the larger Microsoft Azure ecosystem and offers seamless integration with several Azure services such as Azure Data Factory, Azure Databricks, and Azure Analysis Services.
Pricing Model: Pricing is another aspect where Amazon Athena and Azure Storage differ. Amazon Athena pricing is based on the amount of data scanned by the queries and the cost per terabyte for the storage. Users only pay for the queries they run, making it a cost-effective option for ad-hoc and on-demand analysis. In contrast, Azure Storage pricing is based on the amount of stored data and transactions, such as data writes and reads. This pricing model may be more suitable for scenarios where data storage and retrieval are the primary concerns.
Data Source Support: When it comes to data source support, Amazon Athena has broader support compared to Azure Storage. Athena can directly query data in Amazon S3, AWS Glue Data Catalog, and external tables defined in AWS Glue. It also supports various data formats like Parquet, ORC, JSON, and CSV. Azure Storage, on the other hand, can work with various data types such as blobs, files, queues, and tables, but additional tools and services are required for complex data transformations and analysis.
Managed Service vs. Cloud Storage: Amazon Athena is a fully managed service provided by AWS, meaning that AWS takes care of managing the infrastructure, scalability, and performance of the service. Users can focus on executing queries and analyzing results without worrying about managing underlying servers or clusters. In contrast, Azure Storage primarily provides cloud storage capabilities, and users need to provision and manage their own infrastructure if they want to perform analytics on the data.
In summary, Amazon Athena offers a serverless query engine for data stored in Amazon S3, tightly integrates with the AWS ecosystem, follows a pay-per-query pricing model, and provides broader data source support. On the other hand, Azure Storage is a cloud storage service that requires additional services for data processing and analytics, integrates with the Azure ecosystem, follows a different pricing model based on storage and transactions, and supports various data types.
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 Storage
- All-in-one storage solution24
- Pay only for data used regardless of disk size15
- Shared drive mapping9
- Cost-effective2
- Cheapest hot and cloud storage2
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Cons of Amazon Athena
Cons of Azure Storage
- Direct support is not provided by Azure storage2
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