Amazon Athena vs Amazon Quicksight

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

Amazon Athena

+ 1
Amazon Quicksight

+ 1
Add tool

Amazon Athena vs Amazon Quicksight: What are the differences?

Amazon Athena and Amazon QuickSight are two powerful cloud-based services offered by Amazon Web Services (AWS) that cater to specific needs in data analytics and visualization. Both services have their unique features and functionalities, making them suitable for different use cases. Below are the key differences between Amazon Athena and Amazon QuickSight.

  1. Data Processing Methodology: Amazon Athena is a serverless interactive query service that allows users to directly query data stored in Amazon S3 using standard SQL syntax. It does not require any infrastructure setup or management, as it operates on a pay-per-query model. On the other hand, Amazon QuickSight is a fast, cloud-powered business intelligence (BI) service that focuses on data visualization and reporting. It allows users to connect various data sources, create interactive dashboards, and share those dashboards with others.

  2. Usability and User Interface: Amazon Athena primarily targets data analysts, data engineers, and developers who are comfortable with writing SQL queries. It provides an SQL interface via the AWS Management Console, AWS Command Line Interface (CLI), or any JDBC/ODBC connection. Amazon QuickSight, on the other hand, is designed for business users and non-technical users who need to quickly create visualizations and reports. It offers a drag-and-drop interface with various built-in visualization options.

  3. Data Source Integration: Amazon Athena directly integrates with data stored in Amazon S3. It supports various file formats, including Parquet, CSV, JSON, and more. Users can create external tables to map the data stored in S3 for querying purposes. In contrast, Amazon QuickSight can connect to different data sources such as Amazon S3, Amazon Redshift, Amazon RDS, and other third-party databases. It provides options to import data or set up a live connection to these sources.

  4. Data Transformation and Preprocessing: Amazon Athena is primarily focused on querying and extracting data without performing extensive data transformations or preprocessing. It can extract data from files, but users need to apply transformations outside of Athena before ingesting the data. Amazon QuickSight, on the other hand, offers various data transformation capabilities such as data cleansing, filtering, joining, and aggregating within the tool itself. It allows users to perform these transformations for creating meaningful visualizations and reports.

  5. Collaboration and Sharing: Amazon Athena does not have built-in collaboration features or sharing capabilities. It is primarily a query service for individual users or teams who have access to the same account. In contrast, Amazon QuickSight provides collaboration features, allowing users to invite others to view or edit dashboards. It also offers options to share dashboards publicly or embed them in other applications.

  6. Pricing Model: Amazon Athena pricing is solely based on the amount of data scanned by the queries and the amount of data stored in Amazon S3. Users only pay for the queries they run and the storage they utilize. Amazon QuickSight, on the other hand, follows a subscription-based pricing model. It offers different pricing tiers based on the number of users and the level of functionality required.

In summary, Amazon Athena is a serverless query service designed for querying and analyzing data stored in Amazon S3 using SQL. It is suitable for data analysts and engineers who are comfortable with SQL syntax. On the other hand, Amazon QuickSight is a cloud-powered BI service focused on data visualization and reporting. It is geared towards business users and provides a user-friendly interface for creating interactive dashboards and sharing them with others.

Advice on Amazon Athena and Amazon Quicksight

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?

See more
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

See more
Carlos Acedo
Data Technologies Manager at SDG Group Iberia · | 5 upvotes · 239.1K views
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.

See more
Alexis Blandin
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

See more

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

See more
Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Amazon Athena
Pros of Amazon Quicksight
  • 16
    Use SQL to analyze CSV files
  • 8
    Glue crawlers gives easy Data catalogue
  • 7
  • 6
    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
  • 1
    Dataset versionning
  • 1
    Good integration with aws Glue ETL services
  • 1
    More features (table calculations, functions, insights)
  • 1
    Better integration with aws
  • 1
    Super cheap

Sign up to add or upvote prosMake informed product decisions

Cons of Amazon Athena
Cons of Amazon Quicksight
    Be the first to leave a con
    • 1
      Very basic BI tool
    • 1
      Only works in AWS environments (not GCP, Azure)

    Sign up to add or upvote consMake informed product decisions

    What companies use Amazon Athena?
    What companies use Amazon Quicksight?
    See which teams inside your own company are using Amazon Athena or Amazon Quicksight.
    Sign up for StackShare EnterpriseLearn More

    Sign up to get full access to all the companiesMake informed product decisions

    What tools integrate with Amazon Athena?
    What tools integrate with Amazon Quicksight?

    Sign up to get full access to all the tool integrationsMake informed product decisions

    What are some alternatives to Amazon Athena and Amazon Quicksight?
    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.
    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.
    The community platform for the future.
    See all alternatives