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AWS Athena

Amazon Athena facilitates straightforward analysis of data stored in Amazon S3 by utilizing the standard Structured Query Language (SQL), Since Athena is serverless, there is no infrastructure to maintain, and you only pay for the queries you execute. It's simple to get started with Athena. Simply specify the Amazon S3 bucket containing your data, specify the structure, and begin regular SQL queries.



Amazon Athena is a query service that allows you to interact with your data in order to perform basic SQL analysis on data stored in Amazon S3. Athena allows users to direct it at their S3 data with a few clicks in the AWS Management Console, at which point they can begin using conventional SQL to conduct interactive queries and obtain responses in seconds. Due to Athena’s serverless nature, users only need to pay for the queries they execute and not for any associated infrastructure. Athena can be used for a variety of log processing, data analysis, and interactive querying tasks. With Athena, scaling is handled automatically, with queries executed in parallel, allowing for quick returns even with big datasets and sophisticated queries.

Serverless with an inexistent support system and no management

Managing infrastructure is unnecessary because Amazon Athena does not have any servers. As your datasets and user base expand, you can focus on developing new applications rather than worrying about your infrastructure. Athena handles this mechanically so you may concentrate on the data rather than the underlying infrastructure.


To get started, log into the Athena console, define your schema with the console wizard or DDL statements, and start querying with the built-in editor. AWS Glue can automatically explore data sources to find data and update your Data Catalog with new table and partition definitions. In seconds, results are presented in the console and written to S3. You can also save them. With Athena, you don’t require sophisticated ETL jobs to analyze data. Anyone with SQL expertise can evaluate massive datasets fast.


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Use regular SQL to query

Amazon Athena leverages Presto, a low-latency, interactive SQL query engine. You can perform ANSI SQL queries against big Amazon S3 datasets with support for massive joins, window functions, and arrays. Athena supports CSV, JSON, ORC, Avro, and Parquet. With Athena’s federated data source connectors, you may query and join data from Amazon S3. Athena’s JDBC and ODBC drivers let you perform queries via the console, API, CLI, and AWS SDK, and support BI and SQL development apps.

Pay per query

With Amazon Athena, you pay just for queries. You’re charged based on the amount of data scanned by each query. You can save money and improve speed by compressing, splitting, or transforming your data to a columnar format. Each of these processes minimizes the amount of data Athena has to scan to run a query.

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Quick speed

The quick performance you need doesn’t necessitate any expertise in cluster management or tweaking thanks to Amazon Athena. Athena’s integration with Amazon S3 is intended for speed. Athena automatically parallelized query execution, allowing for rapid response times even on massive datasets.
High availability and long life span
Because of its high availability, Amazon Athena performs queries using compute resources from many data centers, automatically rerouting them if necessary. Your data is very accessible and durable because Athena uses Amazon S3 as its underlying data store. Amazon Simple Storage Service (S3) is a reliable data storage service with an object durability design of 99.999999999%. Your information is saved in various locations, and at each location, it is stored on multiple devices.


Through the use of AWS Identity and Access Management (IAM) policies, access control lists (ACLs), and Amazon S3 bucket policies, you may restrict who has access to your data in Amazon Athena. If you utilize IAM policies, you can give specific permissions to users for your S3 buckets. Data in S3 can be protected from Athena queries by limiting access to specific users. Athena makes it simple to query encrypted Amazon S3 data and save the decrypted results back to the same bucket. It is possible to encrypt data either on the server or on the client’s end.


Amazon Athena is preconfigured to work with AWS Glue. Glue Data Catalog allows you to centralize metadata across several services, discover new data through crawling, populate your Data Catalog with updated table and partition definitions, and manage schema versions. Additionally, Glue’s fully-managed ETL features can be used to transform data or convert it into columnar forms for better query performance and lower costs.

Distributed query

Connectors for common third-party data stores including MySQL, PostgreSQL, and Redis are supported by Athena, along with Amazon DynamoDB, Amazon Redshift, Amazon OpenSearch, and many more. Athena’s data connectors make it possible to draw conclusions from disparate data sets without having to manually move or transform the data using ETL scripts. You can extend SQL queries to hundreds of users when you run them as AWS Lambda functions and offer cross-account access using data connectors.

Learning by machine (ML)

With an Athena SQL query, you may conduct inference using a model you created in SageMaker Machine Learning. The use of ML models into SQL queries simplifies previously difficult operations like anomaly detection, customer cohort analysis, and sales forecasting. All one needs is some familiarity with SQL queries in order to use Athena to execute machine learning models hosted on Amazon SageMaker.


Instant querying

Athena is an ETL that doesn’t require a server. You can run queries on your data fast without needing to manage servers or data storage facilities. Simply connect to your Amazon S3 data, define the schema, and begin querying with the integrated query editor. You can access all of your S3 data with Amazon Athena, and there’s no need to put up any elaborate procedures to extract, transform, and load the data (ETL).

Get charged per search

Data scanning is an add-on service.

Costs for using Amazon Athena are decoupled from the number of queries you send it. There is a $5 fee for each terabyte that your queries scan. By compressing, splitting, and turning your data into columnar formats, you can save 30%-90% on per-query expenses and improve performance. Athena conducts queries on Amazon S3 files directly. The S3 storage plan is free of any additional fees.

Fully accessible, very effective, and universally accepted

Powered by Presto and regular SQL.

Amazon Athena is built on Presto and supports ANSI SQL, allowing it to read and write to common file formats like CSV, JSON, ORC, Avro, and Parquet. Athena excels in complicated analysis, including massive joins, window functions, and arrays, and it also facilitates interactive querying. Amazon Athena has excellent availability because it distributes query processing over numerous data centers and multiple devices in each data center. Since Amazon S3 is the underlying data store for Amazon Athena, you can rest assured that your data will be both accessible and stable.

Incredibly fast

Despite the size of the data set, the performance remains interactive.

You can rest assured that Amazon Athena will provide you with the computational resources you need for quick, interactive query performance. Because Amazon Athena processes queries in parallel automatically, you may expect to receive the most results in a matter of seconds.


When using Amazon Athena, you will only be charged for the queries you execute. Each query has its own cost associated with the amount of data it scans. By reducing the amount of data that Athena must scan to run a query, you can save money and improve speed by compressing, dividing, or transforming your data to a columnar format.

Jacob Murphy
Jacob Murphy

Jake is a writer and marketing associate for AllCode with a wealth of experience in a variety of industries.

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