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Table of contents
1.
Introduction
2.
Difference Between Amazon Athena and Microsoft SQL Server
3.
Uses of Amazon Athena
4.
Access Amazon Athena
5.
Features of AWS Athena
6.
Frequently Asked Questions
6.1.
What is the use of Amazon Athena?
6.2.
Is Amazon Athena a database?
6.3.
What language does Athena use?
6.4.
What is Amazon Athena good for?
6.5.
How does AWS Athena work?
7.
Conclusion
Last Updated: Mar 27, 2024
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Amazon Athena

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Introduction

In this article, we will learn about AWS Athena. AWS Athena is an Amazon service that lets customers query data from S3 using regular SQL syntax. It's an interactive data analysis tool for processing complex queries in a shorter amount of time. It is server-less, so there is no effort in getting started and no infrastructure to manage. 

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We pay for the queries we execute because it is not a database service. We point our data in S3, define the schema required, and we are ready to start using standard SQL. Even with a vast dataset and sophisticated queries, Amazon Athena expands automatically, processing queries in parallel, resulting in rapid responses.

Let’s see the difference between the Microsoft SQL server and Amazon Athena.

Difference Between Amazon Athena and Microsoft SQL Server

            Microsoft SQL Server                  Amazon Athena
Microsoft SQL Server is a database administration and analytical solution from Microsoft.  Amazon Athena is a query service that allows you to analyze data easily.
It requires server installation. No server installation is needed.
It is used for DCL, DML, TCL, and DDL operations on the Database. It is used for DML operations on Database.
This is integrated using SQLDep, Sequlize, and Presto. Amazon S3, AWS Glue, and Presto are used for integration for Amazon Athena

Now, we will see some uses of amazon Athena.

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Uses of Amazon Athena

If you're a Data Analyst who's worked with data stored on S3, you'll be familiar with this.

Data Analysts: Do you provide storage?

AWS: Yes.

Data Analyst: Do you have analytics tools?

AWS: I'm not certain.

  • Amazon developed Amazon Athena as a result of their efforts. You now have a tool with which to experiment with your data. Athena assists you in analyzing data stored in Amazon S3 that is unstructured, semi-structured, or structured. 
  • You may make dynamic queries for your dataset using Athena. Athena also integrates with AWS Glue to provide a more efficient way to store metadata in S3.
  • You may utilize named queries with AWS CloudFormation and Athena. You can name your query and then call it using named queries.
  • Instead of running the entire query, Data Scientists and developers can use AWS' interactive service to get a sneak peek at the table. It's also used to retrieve data from S3, load it into different data stores via the Athena JDBC driver, and store and analyze logs for Data Warehousing events.

 

Now, let’s learn about how to access Amazon Athena.

Read more, Amazon Hirepro

Access Amazon Athena

Athena is simple to use and can be accessed by any of the following:

  • AWS Console
  • AWS CLI
  • Athena, with your JDBC

 

These are just a few options for getting into Amazon Athena. You've learned almost everything there is to know about Amazon Athena. Let us go through the various features of Athena.

Features of AWS Athena

Athena is one of the many services offered by Amazon. It has a lot of features that make it ideal for data analysis. Let's go over the various features one by one.

  • Athena is simple to implement because it does not require installation. It can be accessed directly from the AWS Console and through the AWS CLI.
  • Serverless: Because it is serverless, the end-user is not concerned with infrastructure, configuration, scaling, or failure. Athena handles everything on its own.
  • Pay per query: Athena only charges us for the query we run, not the amount of data managed per query. We can save a lot of money by compressing them and formatting your dataset correctly.
  • Athena is a lightning-fast analytics tool. It can run complex queries in less time by breaking them down into simpler ones and running them in parallel, then combining the results to produce the desired output.
  • Athena provides complete control over the data set through IAM policies and AWS Identity. Because the data is stored in S3 buckets, IAM policies can help you manage user control.
  • Highly available: Athena is highly available because of  AWS's assurance, and users can run queries around the clock. Athena, like AWS, is 99.999 percent operational.
  • Integration: The best feature of Athena is its compatibility with AWS Glue. AWS Glue will assist the user in building a more unified data repository. This enables you to create better data versions, tables, views, etc.

 

Now, we will see some faqs to clear your doubts regarding Athena.

Frequently Asked Questions

What is the use of Amazon Athena?

Amazon Athena helps you analyze data stored in Amazon S3. You can use Athena to run interactive analytics using ANSI SQL without aggregating or loading the data into Athena. Amazon Athena can process unstructured, semi-structured, and structured data sets.

Is Amazon Athena a database?

Athena is a query engine rather than a database. This means that computing and storage are distinct: databases store data at rest while also providing the resources required to perform queries and calculations. Each of these has both direct and indirect costs.

What language does Athena use?

Amazon Athena allows users to use Structured Query Language to analyze Amazon S3 (SQL) data. The application is intended for quick, ad hoc, and complex analysis.

What is Amazon Athena good for?

Amazon Athena is a query service that allows you to analyze data in Amazon S3 using standard SQL easily. Because Athena is serverless, there is no infrastructure to manage, and you only pay for the queries you run. Athena is simple to use.

How does AWS Athena work?

Athena works with data stored in S3 directly. To run queries, Athena employs Presto, a distributed SQL engine. It also uses Apache Hive to create, delete, and modify tables and partitions. In the Athena query editor, you can write Hive-compliant DDL statements and ANSI SQL statements.

Conclusion

In this article, we have extensively discussed Amazon Athena. We discussed the Optimisation Techniques and features of Amazon Athena. In the end, we have seen some Faqs related to Amazon Athena.

After reading about the Amazon Athena, are you not feeling excited to read/explore more articles on the topic of AWS? Don't worry; Coding Ninjas has you covered. To learn, see Introduction to AWSAWS FeaturesManaging Devices with AWS IoTAWS Amplify, and AWS Cost & Usage Report.

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