![]() ![]() An ETL engine that’s capable of generating Scala or Python code.A flexible scheduler for handling job monitoring.Since its initial release in August 2017, AWS Glue has been operating as a fully-managed Extract, Transform, and Load (ETL) service. Keep in mind, though, that Redshift is costlier since it charges for both compute and storage. This means you can, for instance, apply it in real-time data analysis, clickstream events, and log analysis. Then AWS Redshift, contrastingly, is ideal for analyzing large structured data sets - as it’s capable of generating results much faster than Athena. ![]() It should be able to work on structured, semi-structured, and unstructured data formats. Instead, Redshift relies on clusters, for which you’ll be required to bring in the data extracts and create tables before proceeding with your query.Īs such, you could say that AWS Athena is best reserved for instances when you need to use Presto and ANSI SQL to launch ad-hoc queries on Amazon S3 data sets. ![]() The queries here don’t just run directly. It runs directly over Amazon S3 data sets as a read-only service, setting up external tables without manipulating the S3 data sources.Īmazon Redshift, on the other hand, is a petabyte-scale data warehouse service that’s based on PostgreSQL. Well, AWS Athena is a serverless service that doesn’t require any additional infrastructure to scale, manage, and build data sets. But, overall, Amazon Athena shines in terms of cost and portability, while Redshift triumphs when it comes to scale and performance. There are many factors that come into play when comparing AWS Athena to Redshift. How Does Athena Compare To AWS Redshift And AWS Glue? AWS Athena vs. This architecture allows Amazon to charge Athena users for only the queries they run, consequently making the service a conveniently cost-effective option for organizations leveraging Amazon S3. With auto scaling, even when you’re dealing with complex queries and large data sets, you can count on it to execute your queries in parallel and quickly generate the results. The fact that Athena is serverless means you won’t be required to set up or manage any infrastructure. You’ll be able to retrieve the query results in a couple of seconds.ĪWS Athena is also serverless and built to scale automatically. To start, open your AWS Management Console, direct Amazon Athena towards your Amazon S3 data, and then launch standard SQL queries. This system was introduced to simplify the whole process of analyzing Amazon S3 data. What Is AWS Athena And When Would You Use It?ĪWS Athena is best described as an interactive query service that’s capable of seamlessly using standard Structured Query Language (SQL) to conduct analysis of data stored in Amazon Simple Storage Service (Amazon S3). What Are The Benefits And Disadvantages Of Athena?.How Does Athena Compare To AWS Redshift And Glue?.What Is AWS Athena And When Would You Use It?.To help give you a better understanding of the Amazon service, this article dives deep into what exactly AWS Athena is, what it does, how it runs, how much it costs, and how it compares with AWS Redshift and AWS Glue. AWS Athena also happens to have its fair share of weaknesses, which could substantially influence your overall data analysis. ![]() There are even claims that it’s not only cheaper than similar services, but also manages to save you the trouble of managing infrastructure. Whatever you choose to call it, there’s no doubt that Athena is already creating ripples in the big data analytics space, alongside the likes of Amazon DynamoDB, and Redshift. AWS Athena is the name, although its creators prefer to stick with Amazon Athena. Just a few years ago, Amazon introduced yet another service into its data analytics arsenal. ![]()
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