Data analysis is a difficult task, which has seen an attempt to make things simple, forever. Amazon Athena is a data analysis platform that allows you to perform complex queries in a short amount of time. It employs no servers, which makes the setup simple. It is not a database management system. As a result, you get charged for the queries you run. Simply point your data into S3, define the required schema, and you’re ready to go with a typical SQL query.
An introduction to AWS Athena
- Amazon Athena is a query service that allows you to analyse data easily.
- Amazon released Athena as one of its services on November 20, 2016.
- As previously stated, Amazon Athena is a serverless query service that analyses data stored in Amazon S3 using conventional SQL. Customers may aim Amazon Athena to their data stored in Amazon S3 with a few clicks in the AWS Management Console and perform queries using conventional SQL to obtain results in seconds.
- There is no infrastructure to set up or administer with Amazon Athena, and customers just pay for the queries they run. Even with huge datasets and sophisticated queries, Amazon Athena expands automatically, processing queries in parallel and providing rapid results.
How AWS Athena is priced
- Amazon Athena is a serverless data query tool, which means it’s both scalable and affordable.
- Customers are often charged per inquiry, which equates to the number of queries conducted over a certain time period.
- The standard cost of scanning 1TB of data from S3 is $5. Although it appears to be a little sum at first glance, when several queries are running on hundreds of thousands of GB of data, the cost can quickly escalate.
Which data types are supported by Amazon Athena?
Other data types that are supported include Boolean,TinyIT, Column, CHAR, VARCHAR etc.
What are Amazon Athena’s integration options?
Athena connects with a number of different AWS services. AWS Glue, for example, connects with Athena to provide more advanced data catalogue capabilities, including a metadata repository, automated schema, partition detection, and Python-based data pipelines. For S3 data, Glue Data Catalog stores and retrieves table metadata.
Athena’s underlying data store is Amazon S3, which enables data redundancy.
Features of Amazon AWS Athena
Athena is one of the many services offered by Amazon. It has a number of characteristics that make it ideal for data analysis.
- Serverless: Because it’s serverless, users don’t have to worry about infrastructure, configuration, scaling, or failure. Athena handles everything on her own.
- Fast: Athena is an extremely quick analytical tool. Complex searches can be swiftly executed by splitting them down into shorter questions and processing them in parallel, then merging the results to get the desired output.
- Easy Implementation: Athena does not require installation and can be accessed straight from the AWS console using the AWS CLI.
- Pay Per Query: Athena only charges you for the Query you run, not the amount of data it manages. You can save a lot of money if you compress your files and format your data correctly.
- Flexibility: Amazon Athena’s open and versatile architecture doesn’t restrict you to a specific vendor, technology,
- Secure: Athena gives you complete control over the data set thanks to IAM policies and AWS credentials. IAM policies can help you manage user controls because data is housed in S3 buckets.
Athena is serverless, which means it can quickly scale up or down depending on the system’s load. Athena’s backend is based on Presto, an open-source distributed SQL engine for querying and processing large data sets.
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