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Amazon Athena

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Azure Storage

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Amazon Athena vs Azure Storage: What are the differences?

Amazon Athena: 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; Azure Storage: Reliable, economical cloud storage for data big and small. Azure Storage provides the flexibility to store and retrieve large amounts of unstructured data, such as documents and media files with Azure Blobs; structured nosql based data with Azure Tables; reliable messages with Azure Queues, and use SMB based Azure Files for migrating on-premises applications to the cloud.

Amazon Athena can be classified as a tool in the "Big Data Tools" category, while Azure Storage is grouped under "Cloud Storage".

"Use SQL to analyze CSV files" is the primary reason why developers consider Amazon Athena over the competitors, whereas "All-in-one storage solution" was stated as the key factor in picking Azure Storage.

According to the StackShare community, Azure Storage has a broader approval, being mentioned in 82 company stacks & 42 developers stacks; compared to Amazon Athena, which is listed in 47 company stacks and 17 developer stacks.

Advice on Amazon Athena and Azure Storage

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?

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Replies (4)

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

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Carlos Acedo
Data Technologies Manager at SDG Group Iberia · | 4 upvotes · 152K views
Recommends
Amazon RedshiftAmazon Redshift

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.

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Alexis Blandin
Recommends
Amazon AthenaAmazon Athena

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

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Recommends

you can change your PSV fomat data to parquet file format with AWS GLUE and then your query performance will be improved

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Pros of Amazon Athena
Pros of Azure Storage
  • 15
    Use SQL to analyze CSV files
  • 8
    Glue crawlers gives easy Data catalogue
  • 7
    Cheap
  • 5
    Query all my data without running servers 24x7
  • 4
    No data base servers yay
  • 3
    Easy integration with QuickSight
  • 2
    Query and analyse CSV,parquet,json files in sql
  • 2
    Also glue and athena use same data catalog
  • 1
    No configuration required
  • 0
    Ad hoc checks on data made easy
  • 23
    All-in-one storage solution
  • 15
    Pay only for data used regardless of disk size
  • 9
    Shared drive mapping
  • 2
    Cost-effective
  • 2
    Cheapest hot and cloud storage

Sign up to add or upvote prosMake informed product decisions

Cons of Amazon Athena
Cons of Azure Storage
    Be the first to leave a con
    • 2
      Direct support is not provided by Azure storage

    Sign up to add or upvote consMake informed product decisions

    What is Amazon Athena?

    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.

    What is Azure Storage?

    Azure Storage provides the flexibility to store and retrieve large amounts of unstructured data, such as documents and media files with Azure Blobs; structured nosql based data with Azure Tables; reliable messages with Azure Queues, and use SMB based Azure Files for migrating on-premises applications to the cloud.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Amazon Athena?
    What companies use Azure Storage?
    See which teams inside your own company are using Amazon Athena or Azure Storage.
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    What tools integrate with Amazon Athena?
    What tools integrate with Azure Storage?

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    Blog Posts

    Aug 28 2019 at 3:10AM

    Segment

    PythonJavaAmazon S3+16
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    Jul 2 2019 at 9:34PM

    Segment

    Google AnalyticsAmazon S3New Relic+25
    10
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    What are some alternatives to Amazon Athena and Azure Storage?
    Presto
    Distributed SQL Query Engine for Big Data
    Amazon Redshift Spectrum
    With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 “data lake” -- without having to load or transform any data.
    Amazon Redshift
    It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
    Cassandra
    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
    Spectrum
    The community platform for the future.
    See all alternatives