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

Amazon Athena

+ 1
Apache Drill

+ 1
Add tool

Amazon Athena vs Apache Drill: What are the differences?


Amazon Athena and Apache Drill are two powerful query engines that allow users to process data on a distributed file system without the need for traditional data loading or transformation processes. While both aim to provide fast and cost-effective query processing capabilities, there are some key differences between the two.

  1. Data Source Support: Amazon Athena is primarily designed for querying data stored in Amazon S3, making it a good choice for users who store their data in the Amazon Web Services (AWS) ecosystem. On the other hand, Apache Drill supports a wide range of data sources including Hadoop Distributed File System (HDFS), NoSQL databases, traditional SQL databases, and cloud storage platforms like S3 and Azure.

  2. Data Localization: In Amazon Athena, the data must be localized (partitioned) before querying, which can improve performance but requires additional steps. Apache Drill, on the other hand, does not require data localization and can directly query raw data without any pre-processing.

  3. Query Language: Amazon Athena uses a version of SQL, specifically Presto SQL, to query data. It provides ANSI SQL compatibility and supports a wide range of SQL functions. Apache Drill, on the other hand, supports SQL as well as NoSQL query languages like JSON and MongoDB query syntax, making it more flexible for querying different types of data.

  4. Dependency on Infrastructure: Amazon Athena is a managed service provided by AWS, which means users do not need to worry about deploying and managing the infrastructure. Apache Drill, on the other hand, requires users to set up and manage the cluster infrastructure themselves, giving them more control but also requiring more effort.

  5. Performance Optimization: While both Amazon Athena and Apache Drill provide query optimization techniques, Apache Drill offers more advanced optimization features like query planning, execution, and pushdown optimization. This makes Apache Drill more suitable for complex queries and large datasets that require sophisticated optimization strategies.

  6. Ecosystem Integration: Amazon Athena integrates seamlessly with other AWS services like AWS Glue, which can automate the data cataloging and schema inference processes. It also supports integration with AWS QuickSight for data visualization. Apache Drill, being an open-source project, can be integrated with various tools and frameworks in the Hadoop ecosystem, providing more flexibility in terms of ecosystem integration.

In summary, Amazon Athena and Apache Drill are both powerful query engines, but they differ in terms of data source support, data localization requirements, query language capabilities, dependency on infrastructure, performance optimization capabilities, and ecosystem integration. The choice between the two depends on specific requirements and the existing infrastructure ecosystem.

Advice on Amazon Athena and Apache Drill

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 · 237.9K 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 Apache Drill
  • 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
  • 4
    NoSQL and Hadoop
  • 3
  • 3
    Lightning speed and simplicity in face of data jungle
  • 2
    Well documented for fast install
  • 1
    SQL interface to multiple datasources
  • 1
    Nested Data support
  • 1
    Read Structured and unstructured data
  • 1
    V1.10 released -

Sign up to add or upvote prosMake 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 Apache Drill?

Apache Drill is a distributed MPP query layer that supports SQL and alternative query languages against NoSQL and Hadoop data storage systems. It was inspired in part by Google's Dremel.

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

What companies use Amazon Athena?
What companies use Apache Drill?
See which teams inside your own company are using Amazon Athena or Apache Drill.
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 Apache Drill?

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

Blog Posts

Aug 28 2019 at 3:10AM


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


Google AnalyticsAmazon S3New Relic+25
What are some alternatives to Amazon Athena and Apache Drill?
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